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Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network
Open accessAbstract
Abstract Among the various techniques used for safety analysis of process systems, bow-tie (BT) analysis is becoming a popular technique as it represents an accident scenario from causes to effects. However, the BT application in the dynamic safety analysis is limited due to the static nature of its components, i.e. fault tree and event tree. It is therefore difficult in BT to take accident precursors into account to update the probability of events and the consequent risk. Also, BT is unable to represent conditional dependency. Event dependency is common among primary events and safety barriers. The current paper illustrates how Bayesian network (BN) helps to overcome these limitations. It has also been shown that BN can be used in dynamic safety analysis of a wide range of accident scenarios due to its flexible structure. This paper also introduces the application of probability adapting in dynamic safety analysis rather than probability updating. A case study from the U.S. Chemical Safety Board has been used to illustrate the application of both BT and BN techniques, with a comparison of the results from each technique.
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Citations
Cited by
Methods and models in process safety and risk management: Past, present and future
Khan, F., Rathnayaka, S., & Ahmed, S. (2015) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2015.07.005 · Cited by 649
Self-citationZio, E. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.04.020 · Cited by 401
Kabir, S., & Papadopoulos, Y. (2019) · Safety Science · doi:10.1016/j.ssci.2019.02.009 · Cited by 389
Dynamic risk analysis using bow-tie approach
Khakzad, N., Khan, F., & Amyotte, P. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.04.003 · Cited by 373
Self-citationVilla, V., Paltrinieri, N., Khan, F., & Cozzani, V. (2016) · Safety Science · doi:10.1016/j.ssci.2016.06.002 · Cited by 294
Self-citationDynamic safety assessment of natural gas stations using Bayesian network
Zarei, E., Azadeh, A., Khakzad, N., Aliabadi, M. M., & Mohammadfam, I. (2017) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2016.09.074 · Cited by 257
Self-citationFault Tree Analysis Improvements: A Bibliometric Analysis and Literature Review
Yazdi, M., Mohammadpour, J., Li, H., Huang, H., Zarei, E., Pirbalouti, R. G., & Adumene, S. (2023) · Quality and Reliability Engineering International · doi:10.1002/qre.3271 · Cited by 199
Artificial intelligence in safety-critical systems: a systematic review
Wang, Y., & Chung, S. H. (2022) · Industrial Management & Data Systems · doi:10.1108/imds-07-2021-0419 · Cited by 96
Metadata
- Authors
- Khakzad, N., Khan, F., & Amyotte, P.
- Venue
- Process Safety and Environmental Protection
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 91
- Issue
- 1-2
- Pages
- 46-53
- ISSN
- 0263-8762 (ISSN-L)
- 0957-5820
- OpenAlex
- W2043089065
- Cited by
- 603
- References
- 0
- FWCI
- 21.23
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
