Generating cause-implication graphs for process systems via blended hazard identification methods

Németh, Erzsébet, Seligmann, Benjamin J., Hockings, Kim, Oakley, Jim, O'Brien, Con, Hangos, Katalin M., and Cameron, Ian T. (2011) Generating cause-implication graphs for process systems via blended hazard identification methods. Computer-Aided Chemical Engineering, 29. pp. 1070-1074.

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Causal knowledge in complex process systems is a powerful representational model that permits a range of important applications related to process risk management. These include the development of operator training systems, diagnosis tools, emergency response planning as well as implications on process and control system retrofit and design.

Using a blended hazard identification approach we show how causal knowledge can be generated from design documentation and represented in a structured language, which is then amenable to display cause-implication graphs that explicitly show the links between failures, causes and implications. A case study illustrates the application of the methodology to a safety system in an industrial coke making plant.

Item ID: 49604
Item Type: Article (Research - C1)
ISBN: 978-0-444-53896-3
ISSN: 2543-1331
Keywords: hazard identification, diagnosis, causal analysis, risk management, operator training
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Presented at ESCAPE 21: 21st European Symposium on Computer Aided Process Engineering 2011, 29 May - 01 June 2011, Chalkidiki, Greece.

Funders: Australian Research Council (ARC)
Projects and Grants: ARC Linkage Grant LP0776636
Date Deposited: 19 Jul 2017 07:30
FoR Codes: 09 ENGINEERING > 0904 Chemical Engineering > 090499 Chemical Engineering not elsewhere classified @ 100%
SEO Codes: 86 MANUFACTURING > 8606 Industrial Chemicals and Related Products > 860601 Industrial Gases @ 100%
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