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dc.contributor.authorNémeth, E.
dc.contributor.authorSeligmann, Ben
dc.contributor.authorHockings, K.
dc.contributor.authorOakley, J.
dc.contributor.authorO'Brien, C.
dc.contributor.authorHangos, K.
dc.contributor.authorCameron, I.
dc.date.accessioned2017-01-30T15:25:00Z
dc.date.available2017-01-30T15:25:00Z
dc.date.created2016-01-11T20:00:21Z
dc.date.issued2011
dc.identifier.citationNémeth, E. and Seligmann, B. and Hockings, K. and Oakley, J. and O'Brien, C. and Hangos, K. and Cameron, I. 2011. Generating cause-implication graphs for process systems via blended hazard identification methods. Computer Aided Chemical Engineering. 29: pp. 1070-1074.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/46058
dc.identifier.doi10.1016/B978-0-444-53711-9.50214-5
dc.description.abstract

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.

dc.titleGenerating cause-implication graphs for process systems via blended hazard identification methods
dc.typeJournal Article
dcterms.source.volume29
dcterms.source.startPage1070
dcterms.source.endPage1074
curtin.departmentDepartment of Chemical Engineering
curtin.accessStatusFulltext not available


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