Qualitative Modeling and Machine Learning Applied to the    Real-Time Estimation of Chemical Process Dynamics
{'Code': '07020000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CBET', 'LongName': 'Div Of Chem, Bioeng, Env, & Transp Sys'}}
{'SignBlockName': 'Maria Burka', 'PO_EMAI': None, 'PO_PHON': None}
The control of dynamically changing processes, for example those                operated in a batch or semi-batch mode, are complex because the                 mathematical models used to implement the control strategies do not             have analytical solutions.  Numerical solutions such as recursive               least-squares (RLS) estimation are sometimes used, but these are not            easy to implement for chemical processing systems which may be high             order, time-varying, non-linear or all three.  Modifying RLS                    incorporating specific system characteristics has been used                     successfully resulting in solutions that have limited applicability.            In this work, the PI plans to generate automatic control algorithms             using RLS that are based on classifying disturbances into one of three          categories and planning control action accordingly.  The resulting              method is expected to have general applicability.                                                                                                               There are three basic types of disturbances:                                                                                                                         1)   Slow drift in system state - for such systems there is an                       acceptable band of operating conditions which will                              permit the use of linearized models, but the process                            will ultimately move out of this band (real examples                            are caused by catalyst decay, fouling and slagging,                             etc.).                                                                                                                                                     2)   Short periods of steady state operation - this                                  causes linear dynamic models to be inoperable.                                                                                                             3)   Sudden change in setpoint - immediately following                               such disturbances linear models will be grossly                                 inaccurate (real examples occur when feed lots are changed,                     for  example).                                                                                                                                        The PI plans to design his algorithm so that it will decompose                  all disturbances into combinations of one or more of these.  The                method then involves continuous diagnosis of process behavior to                update control action.
{'FirstName': 'Douglas', 'LastName': 'Cooper', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Douglas J Cooper', 'EmailAddress': 'cooper@engr.uconn.edu', 'NSF_ID': '000267053', 'StartDate': '06/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Connecticut', 'CityName': 'STORRS', 'ZipCode': '062699018', 'PhoneNumber': '8604863622', 'StreetAddress': '438 WHITNEY RD EXTENSION UNIT 11', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Connecticut', 'StateCode': 'CT', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'CT02', 'ORG_UEI_NUM': 'WNTPS995QBM7', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CONNECTICUT', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
[{'Code': '140300', 'Text': 'Proc Sys, Reac Eng & Mol Therm'}, {'Code': '149500', 'Text': 'COMPUTATIONAL ENGINEERING'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8808596.xml'}