A fuzzy Bayesian network approach for risk analysis in process industries

Yazdi, Mohammad; Kabir, Sohag

Abstract

Fault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian Network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant.

Citation

Yazdi, M., & Kabir, S. (2017). A fuzzy Bayesian network approach for risk analysis in process industries. Process Safety and Environmental Protection, 111, 507-519. https://doi.org/10.1016/j.psep.2017.08.015

• Acceptance Date: Aug 13, 2017
• Online Publication Date: Aug 24, 2017
• Publication Date: 2017-10
• Deposit Date: Aug 31, 2017
• Publicly Available Date: Aug 28, 2018
• Journal: Process safety and environmental protection
• Print ISSN: 0957-5820
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 111
• Pages: 507-519
• DOI: https://doi.org/10.1016/j.psep.2017.08.015
• Keywords: Hazard Analysis; Fault tree analysis; Bayesian Networks; Fuzzy Set Theory; Process industry; Safety analysis; Reliability analysis
• Public URL: https://hull-repository.worktribe.com/output/454367
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0957582017302586
• Additional Information: Authors' accepted manuscript of article published in: Process safety and environmental protection, 2017
• Contract Date: Aug 31, 2017