Domino Effect Analysis Using Bayesian Networks

Authors

  • Nima Khakzad,

    1. Process Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5.
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  • Faisal Khan,

    Corresponding author
    1. Process Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5.
      Faisal Khan Process Engineering, Faculty of Engineering and Applied Science Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5; tel: +1 709 864 8939; fax: +1 709 864 6793; fikhan@mun.ca.
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  • Paul Amyotte,

    1. Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada B3J 2X4.
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  • Valerio Cozzani

    1. Dipartimento di Ingegneria Chimica, Mineraria e delle Tecnologie Industriali, Alma Mater Studiorum, Università di Bologna, via Terracini 28, 40131, Bologna, Italy.
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Faisal Khan Process Engineering, Faculty of Engineering and Applied Science Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5; tel: +1 709 864 8939; fax: +1 709 864 6793; fikhan@mun.ca.

Abstract

A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier-studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.

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