An Integrated Task Approach to Expert Systems for Fault     Diagnosis
{'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 proposed work is a novel approach to designing automated                    control mechanisms that use not only plant sensors but also                     product quality to activate and adjust these control                            mechanisms.  The overall objective is to develop an expert                      system framework based on the integration of computational                      strategies for these two tasks.  The system will finally be                     tested on real industrial chemical process installations.                                                                                                       Technological improvements and enhanced data collection methods                 have made chemical plants more operationally complex and have                   provided a large amount of information for a plant operator to                  interpret.  Potential benefits exist in increasing the role of                  process control computers to aid the operator in diagnosing                     equipment failures, instrumentation problems and process                        abnormalities.  Knowledge-based approaches can capture an                       important layer of human problem solving which is present in                    the operation of chemical plants.  An expert system can                         potentially make available, at all times, a known level of                      knowledge and decision-making ability concerning the operation                  of the plant and therefore, can function as an intelligent                      consultant to the operator.                                                                                                                                     The PIs plan to build upon previous experience in other areas                   (medicine and nuclear power plants) to develop a knowledge-                     based operator assist system that mirrors the human expert's                    approach to the problem.  This includes modelling uncertainty,                  i.e. processing qualitative knowledge as well as quantitative                   algorithm development.
[{'FirstName': 'James', 'LastName': 'Davis', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'James Davis', 'EmailAddress': 'jdavis@oarc.ucla.edu', 'NSF_ID': '000299877', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Balakrishnan', 'LastName': 'Chandrasekaran', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Balakrishnan Chandrasekaran', 'EmailAddress': 'chandra@cis.ohio-state.edu', 'NSF_ID': '000217483', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
{'Name': 'Ohio State University Research Foundation -DO NOT USE', 'CityName': 'Columbus', 'ZipCode': '432101016', 'PhoneNumber': '6146888734', 'StreetAddress': '1960 KENNY RD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Ohio', 'StateCode': 'OH', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'OH03', 'ORG_UEI_NUM': 'QR7NH79713E5', 'ORG_LGL_BUS_NAME': None, 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '140300', 'Text': 'Proc Sys, Reac Eng & Mol Therm'}, {'Code': '146500', 'Text': 'PRODUCTION SYSTEMS'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8703746.xml'}