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Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures

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  • Khakzad, Nima

Abstract

A domino effect is a low frequency high consequence chain of accidents where a primary accident (usually fire and explosion) in a unit triggers secondary accidents in adjacent units. High complexity and growing interdependencies of chemical infrastructures make them increasingly vulnerable to domino effects. Domino effects can be considered as time dependent processes. Thus, not only the identification of involved units but also their temporal entailment in the chain of accidents matter. More importantly, in the case of domino-induced fires which can generally last much longer compared to explosions, foreseeing the temporal evolution of domino effects and, in particular, predicting the most probable sequence of accidents (or involved units) in a domino effect can be of significance in the allocation of preventive and protective safety measures. Although many attempts have been made to identify the spatial evolution of domino effects, the temporal evolution of such accidents has been overlooked. We have proposed a methodology based on dynamic Bayesian network to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect. The application of the developed methodology has been demonstrated via a hypothetical fuel storage plant.

Suggested Citation

  • Khakzad, Nima, 2015. "Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 263-272.
    • Handle: RePEc:eee:reensy:v:138:y:2015:i:c:p:263-272
    DOI: 10.1016/j.ress.2015.02.007
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    References listed on IDEAS

    as
    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. Kohda, Takehisa & Cui, Weimin, 2007. "Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1716-1723.
    3. Matellini, D.B. & Wall, A.D. & Jenkinson, I.D. & Wang, J. & Pritchard, R., 2013. "Modelling dwelling fire development and occupancy escape using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 75-91.
    4. Montani, S. & Portinale, L. & Bobbio, A. & Codetta-Raiteri, D., 2008. "Radyban: A tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 922-932.
    5. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2013. "Risk-based design of process systems using discrete-time Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 5-17.
    6. Marquez, David & Neil, Martin & Fenton, Norman, 2010. "Improved reliability modeling using Bayesian networks and dynamic discretization," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 412-425.
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