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A fuzzy Bayesian network approach for risk analysis in process industries

Yazdi, Mohammad; Kabir, Sohag

Authors

Mohammad Yazdi

Sohag Kabir



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

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