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Characteristics of volatile release rate and fuel nitrogen conversion for three typical coals under high temperature fast pyrolysis

Drop Tube Furnace experiments were carried out to investigate the fast pyrolysis characteristics of three typical coals. The process of fast pyrolysis was simulated by a one-step pyrolysis model as well as a Chemical Percolation Devolatilization (CPD) model. The results indicate that coal rank, reaction temperature and residence time have complex influences on volatile release and nitrogen conversion. Both activation energy and frequency factor, which decrease with increasing coal rank, show obvious differences for the three considered coals. The volatile release coefficient QR is relative to coal rank and heating rate which fluctuates from 1.02 to 1.72. Predictions of volatile release and coal nitrogen conversion obtained from CPD model agree well with experiments. In addition, the one-step pyrolysis model could predict the volatile release of low-rank coal, whose precision is related to coal rank.


Introduction
As one of the primary processes in coal combustion, coal pyrolysis has a considerable influence on the ignition behavior [1], pollutant emissions [2] and the flame stability [3]. With the increasing of environment regulations on industrial, the focus of coal research has shifted to control pollutant such as nitrogen oxides (NOx).
Fletcher et al. [4] presented a coal nitrogen distribution model which revealed that the coal nitrogen is being released in two stages during coal combustion. During the first stage (T<1050K), nitrogen releases with tar and light gas called tar nitrogen (N T ). During the second stage (T>1050K), additional nitrogen is released as HCN and NH 3 which is known as volatile nitrogen(N V ), residues in the char is char nitrogen(Nc).Staged combustion technology has achieved moderate success in reducing the amount of volatile nitrogen which is converted to NOx. As a result, char nitrogen become the dominant factor of controlling the amount of NOx emission.
In the last decade, some network devolatilization models based on the coal chemical structure have been developed, such as the one-step model [5], two-step model [6] and Chemical percolation devolatilization (CPD) model [4]. The one-step model has been widely use in experimental investigation and engineering applications due to its simplified formulation, CPD model represents the coal as a two-dimensional Bethe lattice of aromatic clusters linked by aliphatic bridges via the NMR spectroscopy technology. CPD model is widely used in CFD technology for improving predicted accuracy of pollutant emissions during coal combustion. Many researchers have conducted both experimental and numerical studies to better understand the characteristics of coal devolatilization  [7][8][9][10]. They revealed that devolatilization is affected by many factors with complex interactions. On the other hand, useful studies dealing with different coal types have been performed on the coal nitrogen conversion and NOx emissions [11][12][13][14], particularly for high volatile coals [15,16]. However, little work has been done on the low volatile coal behavior under pyrolysis, and the comparison of pyrolysis characteristics for different coal rank has not been done. To authors' best knowledge, the investigation on comparison between fast pyrolysis experiments and CPD model prediction has not been addressed in literature.
The main purpose of this study is to provide fast pyrolysis characteristics of three typical Chinese coals in a Drop Tube Furnace (DTF), in terms of the influence of coal rank, residence time, reaction temperature on the volatile yields and conversion of coal nitrogen to char nitrogen. A ratio coefficient Q D is established to improve predicting accuracy and to expand engineering applications range. The kinetics parameters are measured under fast pyrolysis conditions. The experimental results are compared with numerical simulations by using one-step equation and CPD models to discuss the model predicting accuracy.

Coal samples preparations
The coal samples used in this study were three typical Chinese coals: XY meager-lean coal, QTH bituminous coal and ZD sub-bituminous coal. The raw coals were crushed and then sieved to obtain average 75 μm coal particles. The ultimate and proximate analyses of the three coal samples are presented in Table 1.

Drop Tube Furnace experiments
The high-temperature devolatilization reactions were performed in a DTF, whose scheme is shown in Figure 1. The DTF is an alumina tube having an internal diameter of 40 mm heated by six MoSi 2 heating elements wrapped around the tube. Before the experiments, the DTF was heated electrically to the reaction temperature. A uniform feed rate of 0.3 g/min was achieved by use of a mechanical vibrator and by partial fluidization of the coal particles in the feeder. The narrow stream emerging from the feeder was rapidly heated by the surrounding preheated argon stream and by radiation from the walls. Before being introduced into the reaction zone, the reaction gas flow was preheated to reaction temperature through preheating furnace, which has the similar construction and heating power as the reaction furnace. The particle stream was withdrawn at various distances from the feeder through a water cooled stainless steel filter in which the char residue was collected. L/min 7.50,7.17,6.33 For this pyrolysis experimental study, the reaction gas consisted of Ar with high purity(99.999%), and the reaction temperatures were set to1223, 1373 and 1523 K. The temperature field of the DTF was measured by a B-type thermocouple, and a 700 mm long constant temperature zone was maintained, as shown in Figure 1. Therefore, assuming the difference in relative velocities between the gas and char particles is zero, the residence time can be controlled from 0.02 to0.08 s with an interval of 0.01s. The main parameters are listed in Table 2. For this study, the residence time is defined as follows: where L is the heating length, Q 1 and Q 2 are the flux of carrier gas and reaction gas, A 1 and A 2 are the flow area ofcarrier gas andreaction gas.

Basic assumptions.
Because of highly heating rate and low sample particles loading, the reactions in DTF were approximately considered as isothermal. So the particle temperature Tp supposed to be as constant and equal to the reaction temperature.

Devolatilization model
In this paper, the volatile yields of coal V during pyrolysis reaction were obtained with ash tracer method, expressed as: where A 0 and A 1 are the fractions of ash under dry basis of rawcoal and residual char respectively. The fraction of coal nitrogen converted to char nitrogen Nchar can be modeled by whereN 0 and N 1 presented the fractions of nitrogen under dry basis ofraw coal and residual char respectively.

One-step model
In the DTF experimental study secondary reactions of pyrolysis product will not be considered as well as the mass transfer and heat transfer during mass diffusion. The variation of volatiles with time during fast pyrolysis is given as one-step model: where K is the reaction rate constant, Vm is the maximal volatile yields during reaction, usually reaction rate constant and reaction temperature Tp can be expressed in Arrhenius form: whereAr is the frequency factor and Er is the activation energy for pyrolysis, R is the universal gas constant equal to 8.314, and Tp is the particle temperature (equal to the reaction temperature as mentioned before. Substituting K from Eq. (6) into Eq. (5) and taking the logarithm, we have the correlation of Ln(Ar) and 1/Tp, from which Ar and Er can be determined using the least square method. Figure 2 presents the variations of volatile release rate V with respect to the residence time t for three different coals and different pyrolysis temperatures. Comparison of the three coals, it is observed that V highly increases with the increase of t for each coal whatever the temperature and quickly reaches an almost steady-state regime. V highly increases with residence time until 60 ms while it slowly increases for residence time higher than 60ms.It is also noticed that for the three coals, volatile yields increase with the increase of reaction temperature T when both coal rank and residence time are constant. Slight declines of V can be seen when T varies from 1223K to 1373Kwhile the relatively slow decline of V is corresponding to the heating temperature as 1523K.It indicates that the coal devolatilization is considered as thermal decomposition of plenty functional groups which require different energies, as a result, the quantity of pyrolysis yields increases with the increases of T.

Volatile release rate V
Comparisons between the three cases show that V is the lowest for XY meager-lean coal and the highest for ZD sub-bituminous coal. ZD coal prefers to decompose as gaseous under fast heated condition owing to the worst stability of function group. In addition, the activation energy of ZD coal for pyrolysis maybe the lowest among the three coals. Notably, for all three coals, the volatile yields by using proximate analysis Vdaf (dry and ash free ) are less than the experimental results by using DTF. Surprisingly, there is a similar variation trend of volatile yields between proximate analysis and experimental results (V ZD , daf >V QTH,daf >V XY,daf ), that is to say, the proximate analysis can be used to qualitatively distinguish volatile yield of different rank coal for some engineering applications.
Furthermore, there is obvious difference of volatile release rate (dV/dt) of the three coals for a constant temperature T. dV/dt for ZD sub-bituminous coal is almost the highest and XY meager-lean coal has the lowest dV/dt under the same conditions. Comparing the influence of temperature and coal rank, the latter has a stronger influence on coal devolatilization illustrating the volatile yields are complexly affected by coal rank, reaction temperature and residence time.  As mentioned above, theone-step and CPD models have been widely used in engineering and numerical simulations to explore the characteristics of pyrolysis. However, the one-step model is based on empirical results, leading to critical that calculation precision limited to the experimental conditions [17,18]. On the contrary, CPD model is not limited to coal rank and can be used with better generality. As unexpected, this model needs many input parameters and longly time of calculation. Besides, both models regard proximate analysis results as input parameters, and give as result, the predicting precision of models which is affected by reaction condition as lower heating rate and longer heating period than that in pulverized coal. Vascellari et al. [17] have built a coefficient Q R to modify the initial input parameters of FLUENT software, which determine the ratio of numerical results from models(as CPD, FG-DVC, FLASHCHAIN)to proximate analysis results. As expected, the numerical results are in good agreement with experimental results. Similarly, a new coefficient Q R is proposed to improve the predicted precision of pyrolysis model and to expand the application range. The details of Q R varying with coal rank and temperature is presented in Figure 3 and the expression is shown as follow:

Determination of ratio coefficient Q R
Q R =Vcjl/Vdaf (7) where Vcjl and Vdaf present the volatile yields of DTF experiments and proximate analysis respectively.
From this figure, it can be found that V cjl is higher than V daf for all cases and Q R varies from1.02 to1.72. It can be interpreted as follows: coal particle is polydisperse suspended and exposed in DTF furnace which can absorb a great amount of radiant heat. Conversely, coal particles obscured each other under fixed stacking state of the proximate analysis based on Chinese standard for coal analysis (GB/T212-2008)which results in obvious difference betweenV cjl and V daf . Figure 4 presents the variation of volatile nitrogen Nvol with residence time t for different reaction temperature T and the three colas used in this paper. Similarly, the variation of char nitrogen Nchar with residence time t for different reaction temperature T is shown in Figure 5. Generally, volatile nitrogen Nvol increases with the increase of residence time. It maybe the consequence of the increasing residence time t which leads to the increment of temperature and enhancing the functional group decomposition forced caused by the temperature difference. It is noticed that the notable variation can be seen when t varies from 0 to 60ms, while the relatively stable variation is corresponding to the residence time i.e., from 60ms to 80ms.

Conversion of coal nitrogen
Comparing the three coals, volatile nitrogen Nvol increases with the increase of temperature T, while char nitrogen Nchar presents an opposite variation. Furthermore, there are notable differences in the variation gradient of three coals, the curves of XY meager-lean coal have the smallest gradients while the curves of ZD bituminous coal have the largest gradients. It reveals that char nitrogen can mainly be regarded as heterocyclic nitrogen with high stability, which release characteristics are affected by both temperature and coal rank.    Table 3 presents the reaction rate coefficient B calculated by Eq.6, which concerns with residence time t, temperature T, and coals rank. In order to obtain the average activation energy Er and frequency factor Ar for different coals, Figure 6 shows the relationship between reaction temperature and the intrinsic reaction rate measured by DTF.

Variation of activation energy and frequency factor
As expected, there is an excellent linear correlation between lnB and 1/T P . Besides, the correlation increases with the increase of volatile yields of coal. The predicting accuracy of one-step model is limited to experimental conditions. Surprisingly, the results are precise for the coal devolatilization process combined fast pyrolysis condition of DTF system with one-step model, especially for high volatile coal rank.  Figure 6. Arrhenius plots of different coals.

Comparison of experimental results and devolatilization model prediction
In CPD model, coal is considered as a macromolecular array whose building blocks are clusters of fused aromatic rings of various sizes and types, including heteroaromatic systems with both nitrogen and oxygen atoms. These aromatic clusters are interconnected by a variety of chemical bridges, some of which are labile bonds that break readily during coal pyrolysis, while others are stable at a given temperature [19]. Four parameters derived from 13C NMR experiments that describe the structure of the parent coal are used directly as input parameters to the CPD model [20,21],as shown in Table 5. This includes Mcl (the average molecular weight per aromatic cluster), Mδ(the average side-chain molecular weight), σ+1(the average number of attachments per cluster),and P0(the fraction of intact bridges). Dominic [22]has proposed an advanced model based on the five parameters mentioned above which received from large amount of experiments by using 13 Table 5 presents the input parameters for CPD model as well as the structure parameters of coal used in this study are shown in Table 6.   conditions. In addition, nitrogen conversion received by CPD modeling and experimental results are presented in Figure 8. Figure 7 presents the variation of volatile release with residence time t under different by using CPD model and one-step model.

Volatile yields
Comparing the prediction precision of CPD and one-step model, numerical results of CPD model are in better agreement with the experiment results, especially for XY meager-lean and QTH bituminous coals. However, errors exist at the beginning of the devolatilization process which may be due to multiple reactions of different function groups considered in CPD model, and the heating rate of CPD model is higher than experimental condition, leading to lower volatile yields predicted by CPD model than experiment results. As expected, the particle temperature increases with the increment of residence time t, while the influence of heating rate on volatile yields decrease with increasing reaction time. It is remarkable that comparing with CPD model, one-step model has a better prediction for ZD sub-bituminous coal but not for the other two coals, which indicates that the kinetic parameters come from one-step model are appropriate for low rank coal similarly with ZD subbituminous coal.  Figure8 presents the char nitrogen conversion along DTF obtained by experimental measurements and CPD model results. As unexpected, the prediction of char nitrogen conversion by the one-step model cannot be obtained due to the limitation of calculation method. Generally, it is seen that volatile yields have a strong effect on char nitrogen matter, and Nchar decreases significantly with the increase of volatile yields. Comparisons among the three coals show that Nchar is the lowest for ZD subbituminous coal and is the lowest for XY meager-lean coal, indicating CPD model has an appropriate prediction for char nitrogen conversion of coal of high temperature fast pyrolysis.

Conclusions
The characteristics of fast high temperature pyrolysis of three Chinese typical coals in DTF were explored under different temperatures. The volatile release and coal nitrogen conversion were predicted by numerical modeling. The main findings are as follows: (1) Both volatile release and char nitrogen conversion were affected by coal rank, reaction temperature, and residence time. For the given T and t, volatile nitrogen conversion Nv and V decrease with the increase of coal rank. For all coal rank in the experiment, Nv and V have obviously increments with the increasing of t, while Nc decreases with the increments of residence time. For a given t, V and Nv increase with the increasing of T. Oppositely, Nc obviously declines with increasing reaction temperature.  10 (2) The volatile yields obtained from DTF experiments are superior to that from proximate analysis under the same condition. The prediction precision of proximate analysis for high temperature pyrolysis can be improved by using ratio coefficient Q R . In addition, Q R varies significantly among different coals.
(3) The activation energy and frequency factor were affected by coal rank several. Er and Ar increase with the increasing coal rank for a given condition.
(4) One-step model is usually used in predicting volatile yields of low rank coal with high volatile matters, whose precision is significantly related to the coal rank. Comparing with the one-step model, the CPD model is widely used and has better prediction accuracy for fast pyrolysis of different coal rank.