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Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects

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  • Khakzad, Nima
  • Landucci, Gabriele
  • Reniers, Genserik

Abstract

The propagation of fire in chemical plants – also known as fire domino effects - largely depends on the performance of add-on passive and active protection systems such as sprinkler systems, water deluge systems, emergency shut down and emergency blow down systems, fireproofing, and emergency response. Although such safety barriers are widely employed to prevent or delay the initiation or escalation of fire domino effects, their inclusion in the modeling and risk assessment of fire domino effects has hardly been taken into account. In the present study, the dynamic evolution of fire protection systems has been investigated qualitatively using event tree analysis. To quantify the temporal changes and their impact on the escalation of fire domino effects, a dynamic Bayesian network methodology has been developed. The application of the methodology has been demonstrated using an illustrative case study, considering a variety of fire scenarios, target installations, and firefighting systems.

Suggested Citation

  • Khakzad, Nima & Landucci, Gabriele & Reniers, Genserik, 2017. "Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 232-247.
  • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:232-247
    DOI: 10.1016/j.ress.2017.06.004
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    References listed on IDEAS

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    5. Zhou, Jianfeng & Reniers, Genserik & Khakzad, Nima, 2016. "Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 202-209.
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    10. Ovidi, Federica & Zhang, Laobing & Landucci, Gabriele & Reniers, Genserik, 2021. "Agent-based model and simulation of mitigated domino scenarios in chemical tank farms," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    11. Misuri, Alessio & Landucci, Gabriele & Cozzani, Valerio, 2021. "Assessment of safety barrier performance in the mitigation of domino scenarios caused by Natech events," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
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    17. Wu, Jiansong & Bai, Yiping & Fang, Weipeng & Zhou, Rui & Reniers, Genserik & Khakzad, Nima, 2021. "An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    18. Hou, Lei & Wu, Xingguang & Wu, Zhuang & Wu, Shouzhi, 2020. "Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    19. Jinhua Mi & Yuhua Cheng & Yufei Song & Libing Bai & Kai Chen, 2022. "Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions," Annals of Operations Research, Springer, vol. 311(1), pages 311-333, April.
    20. Jiansong Wu & Zhuqiang Hu & Jinyue Chen & Zheng Li, 2018. "Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network," Sustainability, MDPI, vol. 10(10), pages 1-21, October.
    21. Hong, Bingyuan & Shao, Bowen & Guo, Jian & Fu, Jianzhong & Li, Cuicui & Zhu, Baikang, 2023. "Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines," Applied Energy, Elsevier, vol. 333(C).
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    23. Louis Anthony Cox, 2020. "Answerable and Unanswerable Questions in Risk Analysis with Open‐World Novelty," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2144-2177, November.
    24. Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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