Vulnerability analysis of process plants subject to domino effects

• Published in: Reliability engineering & system safety Vol. 154; pp. 127 - 136
• Main Authors: Khakzad, Nima, Reniers, Genserik, Abbassi, Rouzbeh, Khan, Faisal
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2016
• Subjects: Domino effect; Dynamic Bayesian network; Graph metrics; Process plant; Process plant; Domino effect; Dynamic Bayesian network; Graph metrics
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2016.06.004

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. •Graph theory is a reliable tool for vulnerability analysis of chemical plants as to domino effects.•All-closeness centrality score can be used to identify most vulnerable installations.•As for complex chemical plants, the methodology outperforms Bayesian network.