A fuzzy Bayesian network approach for risk analysis in process industries

Yazdi, Mohammad; Kabir, Sohag

doi:10.1016/j.psep.2017.08.015

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
Electronic ISSN	1744-3598
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