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Vulnerability analysis of process plants subject to domino effects

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  • Khakzad, Nima
  • Reniers, Genserik
  • Abbassi, Rouzbeh
  • Khan, Faisal

Abstract

In the context of domino effects, vulnerability analysis of chemical and process plants aims to identify and protect installations which are relatively more susceptible to damage and thus contribute more to the initiation or propagation of domino effects. In the present study, we have developed a methodology based on graph theory for domino vulnerability analysis of hazardous installations within process plants, where owning to the large number of installations or complex interdependencies, the application of sophisticated reasoning approaches such as Bayesian network is limited. We have taken advantage of a hypothetical chemical storage plant to develop the methodology and validated the results using a dynamic Bayesian network approach. The efficacy and out-performance of the developed methodology have been demonstrated via a real-life complex case study.

Suggested Citation

  • Khakzad, Nima & Reniers, Genserik & Abbassi, Rouzbeh & Khan, Faisal, 2016. "Vulnerability analysis of process plants subject to domino effects," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 127-136.
    • Handle: RePEc:eee:reensy:v:154:y:2016:i:c:p:127-136
    DOI: 10.1016/j.ress.2016.06.004
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    References listed on IDEAS

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    1. Johansson, Jonas & Hassel, Henrik & Zio, Enrico, 2013. "Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 27-38.
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    4. Khakzad, Nima & Reniers, Genserik, 2015. "Using graph theory to analyze the vulnerability of process plants in the context of cascading effects," Reliability Engineering and System Safety, Elsevier, vol. 143(C), pages 63-73.
    5. 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.
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    Citations

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    Cited by:

    1. Tao Zeng & Guohua Chen & Yunfeng Yang & Genserik Reniers & Yixin Zhao & Xia Liu, 2020. "A Systematic Literature Review on Safety Research Related to Chemical Industrial Parks," Sustainability, MDPI, vol. 12(14), pages 1-27, July.
    2. Lan, Meng & Gardoni, Paolo & Qin, Rongshui & Zhang, Xiao & Zhu, Jiping & Lo, Siuming, 2022. "Modeling NaTech-related domino effects in process clusters: A network-based approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Guo, Xiaoxue & Ding, Long & Ji, Jie & Cozzani, Valerio, 2022. "A cost-effective optimization model of safety investment allocation for risk reduction of domino effects," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Laobing Zhang & Gabriele Landucci & Genserik Reniers & Nima Khakzad & Jianfeng Zhou, 2018. "DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1585-1600, August.
    5. Chen, Chao & Khakzad, Nima & Reniers, Genserik, 2020. "Dynamic vulnerability assessment of process plants with respect to vapor cloud explosions," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    6. 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).
    7. Nima Khakzad & Gabriele Landucci & Genserik Reniers, 2017. "Application of Graph Theory to Cost‐Effective Fire Protection of Chemical Plants During Domino Effects," Risk Analysis, John Wiley & Sons, vol. 37(9), pages 1652-1667, September.
    8. 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.
    9. Chen, Chao & Reniers, Genserik & Khakzad, Nima, 2021. "A dynamic multi-agent approach for modeling the evolution of multi-hazard accident scenarios in chemical plants," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    10. 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).
    11. Wang, Likun & Huang, Ruiling & Shi, Wenming & Zhang, Caiyun, 2021. "Domino effect in marine accidents: Evidence from temporal association rules," Transport Policy, Elsevier, vol. 103(C), pages 236-244.
    12. Li, Xiaofeng & Chen, Guohua & Amyotte, Paul & Khan, Faisal & Alauddin, Mohammad, 2023. "Vulnerability assessment of storage tanks exposed to simultaneous fire and explosion hazards," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Dimaio, F. & Scapinello, O. & Zio, E. & Ciarapica, C. & Cincotta, S. & Crivellari, A. & Decarli, L. & Larosa, L., 2021. "Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Jian Guo & Jun Wang & Baikang Zhu & Bingyuan Hong & Cuicui Li & Jianhui He, 2022. "A Risk Evaluation Method of Coastal Oil Depots for Heavy Rainfall Vulnerability Assessment," Sustainability, MDPI, vol. 14(11), pages 1-12, June.
    15. 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).
    16. Khakzad, Nima, 2023. "A goal programming approach to multi-objective optimization of firefighting strategies in the event of domino effects," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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