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Knowledge-Based Framework for Automating HAZOP Analysis.

PB98130834

Publication Date	1997
Personal Author	Venkatasubramanian, V.
Page Count	16
Abstract	Automating HAZOP analysis is achieved by understanding the different types of expertise required for performing HAZOP analysis and developing a framework for representing and reasoning with this knowledge. The authors have demonstrated that the knowledge required for HAZOP analysis can be separated into process-specific and process-general knowledge. The authors have shown that these mental models can be represented using directed graph-based qualitative models of process units, called HAZOP-diagraph (HDG) models. The authors have also shown how these HDG-models can be developed for common process units. The authors also developed bi-directional message-passing based inference methods for the control of reasoning. The authors also solved assocated problems concerning cyclic loops among process variables due to recycle and control loops. The authors' investigation of large-scale flowsheets identified that strict qualitative reasoning could lead to ambiguous values for process variables. The authors developed a novel semiquantitative and order-of-magnitude reasoning methodology which uses quantitative information in the form of the design specifications and normal operating conditions of the process units and the quantitative properties of process materials to filter and rank the adverse consequences found by strict qualitative reasoning. The authors have tested these techniques by developing a knowledge-based system, called HAZOPExpert, to perform automated HAZOP analysis. The authors tested the HAZOPExpert system with the aid of five complex, real-life, industrial case studies.
Keywords	Knowledge based systems Automation Industrial plants Safety management Knowledge bases(Artificial intelligence) Reasoning Expert systems Graphical user interface Object-oriented programming Flowsheets Industrial safety Occupatonal safety and health Accident prevention HAZOP(Hazards and Operability Study) Hazards and Operability Study HAZOPExpert Mental models
Source Agency	National Institute for Occupational Safety and Health
Corporate Authors	Purdue Univ., Lafayette, IN. School of Chemical Engineering.; National Inst. for Occupational Safety and Health, Cincinnati, OH.
Supplemental Notes	See also PB92-115724. Sponsored by National Inst. for Occupational Safety and Health, Cincinnati, OH.
Document Type	Technical Report
Title Note	Final rept. 15 Sep 93-15 Mar 97.
NTIS Issue Number	199811

Knowledge-Based Framework for Automating HAZOP Analysis.

Knowledge-Based Framework for Automating HAZOP Analysis.
PB98130834

Publication Date	1997
Personal Author	Venkatasubramanian, V.
Page Count	16
Abstract	Automating HAZOP analysis is achieved by understanding the different types of expertise required for performing HAZOP analysis and developing a framework for representing and reasoning with this knowledge. The authors have demonstrated that the knowledge required for HAZOP analysis can be separated into process-specific and process-general knowledge. The authors have shown that these mental models can be represented using directed graph-based qualitative models of process units, called HAZOP-diagraph (HDG) models. The authors have also shown how these HDG-models can be developed for common process units. The authors also developed bi-directional message-passing based inference methods for the control of reasoning. The authors also solved assocated problems concerning cyclic loops among process variables due to recycle and control loops. The authors' investigation of large-scale flowsheets identified that strict qualitative reasoning could lead to ambiguous values for process variables. The authors developed a novel semiquantitative and order-of-magnitude reasoning methodology which uses quantitative information in the form of the design specifications and normal operating conditions of the process units and the quantitative properties of process materials to filter and rank the adverse consequences found by strict qualitative reasoning. The authors have tested these techniques by developing a knowledge-based system, called HAZOPExpert, to perform automated HAZOP analysis. The authors tested the HAZOPExpert system with the aid of five complex, real-life, industrial case studies.
Keywords	Knowledge based systems Automation Industrial plants Safety management Knowledge bases(Artificial intelligence) Reasoning Expert systems Graphical user interface Object-oriented programming Flowsheets Industrial safety Occupatonal safety and health Accident prevention HAZOP(Hazards and Operability Study) Hazards and Operability Study HAZOPExpert Mental models
Source Agency	National Institute for Occupational Safety and Health
Corporate Authors	Purdue Univ., Lafayette, IN. School of Chemical Engineering.; National Inst. for Occupational Safety and Health, Cincinnati, OH.
Supplemental Notes	See also PB92-115724. Sponsored by National Inst. for Occupational Safety and Health, Cincinnati, OH.
Document Type	Technical Report
Title Note	Final rept. 15 Sep 93-15 Mar 97.
NTIS Issue Number	199811