Bayesian estimation and consequence modelling of deliberately induced domino effects in process facilities |
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| Affiliation: | 1. Dipartimento di Ingegneria Civile e Industriale, Università di Pisa, Largo Lucio Lazzarino 2, 56126 Pisa, Italy;2. Safety and Security Science Group, Faculty of Technology, Policy, and Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands;3. Faculty of Economics and organizational sciences, Campus Brussels, KULeuven, Warmoesberg 26, 1000 Brussels, Belgium;4. DICAM − Laboratory of Industrial Safety and Environmental Sustainability, Alma Mater Studiorum − Università di Bologna, via Terracini n.28, 40131 Bologna, Italy;5. Istituto di Ricerche sulla Combustione, CNR, Via Diocleziano 328, 80124 Napoli, Italy;1. Safety and Security Science Group, Faculty of Technology, Policy and Management, TU Delft, Delft, the Netherlands;2. Faculty of Applied Economics, Antwerp Research Group on Safety and Security (ARGoSS), University Antwerp, Antwerp, Belgium;3. CEDON, KULeuven, Campus Brussels, Brussels, Belgium;1. Safety and Security Science Group, Delft University of Technology, The Netherlands;2. Australian Maritime College, University of Tasmania, Australia;3. Centre for Risk, Integrity, and Safety Engineering (C-RISE), Memorial University of Newfoundland, Canada;1. Faculty of Applied Economics, Research Groups ANT/OR, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium;2. Safety Science Group, TU Delft, Jaffalaan 5, 2628 BX Delft, The Netherlands;3. Center for Corporate Sustainability (CEDON), HUB, KULeuven, Stormstraat 2, 1000 Brussels, Belgium |
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| Abstract: | Process facilities handling hazardous chemicals in large quantities and elevated operating conditions of temperature/pressure are attractive targets to external attacks. The possibility of an external attack on a critical installation, performed with an intention of triggering escalation of primary incidents into secondary and tertiary incidents, thereby increasing the severity of consequences needs to be effectively analysed. A prominent Petrochemical Industry located in Kerala, India was identified for studying the possibility of a deliberately induced domino effect. In this study, a dedicated Bayesian network is developed to model the domino propagation sequence in the chemical storage area of the industry, and to estimate the domino probabilities at different levels. This method has the advantage of accurately quantifying domino occurrence probabilities and identifying possible higher levels of escalations. Moreover, the combined effect from multiple units can be modelled easily and new information can be added into the model as evidences to update the probabilities. Phast (Process hazard analysis) software is used for consequence modelling to determine the impact zones of the identified primary and secondary incidents. The results of the case study show that such analyses can greatly benefit green field and brown field projects in determining the appropriate safety and security measures to be implemented or strengthened so as to reduce its attractiveness to external threat agents. |
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| Keywords: | Domino effect Process plants Bayesian networks Consequence modelling Phast Security |
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