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

    Access Status
    Fulltext not available
    Authors
    Németh, E.
    Seligmann, Ben
    Hockings, K.
    Oakley, J.
    O'Brien, C.
    Hangos, K.
    Cameron, I.
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Németh, 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.
    DOI
    10.1016/B978-0-444-53711-9.50214-5
    School
    Department of Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/46058
    Collection
    • Curtin Research Publications
    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.

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