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A fuzzy Bayesian network approach for risk analysis in process industries
Yazdi, M.
Yazdi, M.
Publication Date
2017-10
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Rights
© 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
Peer-Reviewed
Yes
Open Access status
Accepted for publication
2017-08-13
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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.
Version
Accepted manuscript
Citation
Yazdi M and Kabir S (2017) A fuzzy Bayesian network approach for risk analysis in process industries. Process Safety and Environmental Protection. 111: 507-519.
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Article