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Using graph theory to analyze the vulnerability of process plants in the context of cascading effects

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  • Khakzad, Nima
  • Reniers, Genserik

Abstract

Dealing with large quantities of flammable and explosive materials, usually at high-pressure high-temperature conditions, makes process plants very vulnerable to cascading effects compared with other infrastructures. The combination of the extremely low frequency of cascading effects and the high complexity and interdependencies of process plants makes risk assessment and vulnerability analysis of process plants very challenging in the context of such events. In the present study, cascading effects were represented as a directed graph; accordingly, the efficacy of a set of graph metrics and measurements was examined in both unit and plant-wide vulnerability analysis of process plants. We demonstrated that vertex-level closeness and betweenness can be used in the unit vulnerability analysis of process plants for the identification of critical units within a process plant. Furthermore, the graph-level closeness metric can be used in the plant-wide vulnerability analysis for the identification of the most vulnerable plant layout with respect to the escalation of cascading effects. Furthermore, the results from the application of the graph metrics have been verified using a Bayesian network methodology.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:143:y:2015:i:c:p:63-73
    DOI: 10.1016/j.ress.2015.04.015
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. Johansson, Jonas & Hassel, Henrik, 2010. "An approach for modelling interdependent infrastructures in the context of vulnerability analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1335-1344.
    6. Reniers, G.L.L. & Sörensen, K. & Khan, F. & Amyotte, P., 2014. "Resilience of chemical industrial areas through attenuation-based security," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 94-101.
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