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Conventional and dynamic safety analysis: Comparison on a chemical batch reactor

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  • Podofillini, L.
  • Dang, V.N.

Abstract

Dynamic safety analysis methodologies are an attractive approach to tackle systems with complex dynamics (i.e. with behavior highly dependent on the values of the process parameters): this is often the case in various areas of the chemical industry. The present paper compares analyses with Probabilistic Safety Assessment (PSA)/Quantitative Risk Assessment (QRA) methods with those from a dynamic methodology (Monte Carlo simulation). The results of a case study for a chemical batch reactor from the literature, overall risk figure and main contributors, are examined. The comparison has shown that, provided that the event success criteria are appropriately defined, consistent results can be obtained; otherwise important accident scenarios, identifiable by the dynamic Monte Carlo simulation, are possibly missed in the application of conventional methods. Defining such criteria was quite resource-intensive: for the analysis of this small system, the success criteria definitions required many system simulation runs (about 1000). Such large numbers of runs may not be practical in industrial-scale applications. It is shown that success criteria obtained with fewer simulation runs could have led to different quantitative PSA results and to the omission of important accident scenario variants.

Suggested Citation

  • Podofillini, L. & Dang, V.N., 2012. "Conventional and dynamic safety analysis: Comparison on a chemical batch reactor," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 146-159.
    • Handle: RePEc:eee:reensy:v:106:y:2012:i:c:p:146-159
    DOI: 10.1016/j.ress.2012.04.010
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    References listed on IDEAS

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    1. Podofillini, L. & Zio, E. & Mercurio, D. & Dang, V.N., 2010. "Dynamic safety assessment: Scenario identification via a possibilistic clustering approach," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 534-549.
    2. Yang, Xiaole & Sam Mannan, M., 2010. "The development and application of dynamic operational risk assessment in oil/gas and chemical process industry," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 806-815.
    3. M Kloos & J Peschke, 2008. "Consideration of human actions in combination with the probabilistic dynamics method Monte Carlo dynamic event tree," Journal of Risk and Reliability, , vol. 222(3), pages 303-313, September.
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    1. Karanki, D.R. & Dang, V.N. & MacMillan, M.T. & Podofillini, L., 2018. "A comparison of dynamic event tree methods – Case study on a chemical batch reactor," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 542-553.
    2. J. S. Busby & B. Green & D. Hutchison, 2017. "Analysis of Affordance, Time, and Adaptation in the Assessment of Industrial Control System Cybersecurity Risk," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1298-1314, July.
    3. Sharifzadeh, Mahdi & Meghdari, Mojtaba & Rashtchian, Davood, 2017. "Multi-objective design and operation of Solid Oxide Fuel Cell (SOFC) Triple Combined-cycle Power Generation systems: Integrating energy efficiency and operational safety," Applied Energy, Elsevier, vol. 185(P1), pages 345-361.

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