Volume 10, Issue 3 (Spring 2025)
Health in Emergencies and Disasters Quarterly 2025, 10(3): 193-206 |
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Eskandari T, Mohammadfam I. Application of Fuzzy Bayesian Network in Dynamic Risk Analysis of Explosion in Process Industries. Health in Emergencies and Disasters Quarterly 2025; 10 (3) :193-206
URL: http://hdq.uswr.ac.ir/article-1-622-en.html
URL: http://hdq.uswr.ac.ir/article-1-622-en.html
1- Department of Occupational Health, Occupational Health and Safety Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. & Department of Occupational Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
2- Department of Ergonomics, Health in Emergency and Disaster Research Center, School of Rehabilitation Sciences, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. ,ir.mohammadfam@uswr.ac.ir
2- Department of Ergonomics, Health in Emergency and Disaster Research Center, School of Rehabilitation Sciences, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. ,
Abstract: (822 Views)
Background: Fire and explosion in process industries can have catastrophic consequences. The frequency of these accidents has led safety experts to underscore the importance of conducting thorough risk analysis studies to implement effective control measures.
Materials and Methods: A tank gas leak was initially selected as a scenario for probable explosion risk assessment. The bowtie technique was utilized to analyze the potential causes and consequences of the selected incident. A fuzzy logic approach was used to quantify the probability of essential events, and the Bayesian network (BN) was employed for dynamic risk analysis.
Results: Using the bowtie method, 24 fundamental causes were identified for tank gas leaks (main scenario). Moreover, 4 safety barriers against the prevention of the selected scenario were identified, and evaluation of the success and failure of these safety barriers led to the identification of 5 potential consequences. According to the BN and fuzzy analysis, the inappropriate installation was the most influential event, with a near miss identified as the most likely consequence of the central event.
Conclusion: The results demonstrate that applying fuzzy logic and BNs could solve the uncertainty and static nature of traditional quantitative risk analysis studies.
Materials and Methods: A tank gas leak was initially selected as a scenario for probable explosion risk assessment. The bowtie technique was utilized to analyze the potential causes and consequences of the selected incident. A fuzzy logic approach was used to quantify the probability of essential events, and the Bayesian network (BN) was employed for dynamic risk analysis.
Results: Using the bowtie method, 24 fundamental causes were identified for tank gas leaks (main scenario). Moreover, 4 safety barriers against the prevention of the selected scenario were identified, and evaluation of the success and failure of these safety barriers led to the identification of 5 potential consequences. According to the BN and fuzzy analysis, the inappropriate installation was the most influential event, with a near miss identified as the most likely consequence of the central event.
Conclusion: The results demonstrate that applying fuzzy logic and BNs could solve the uncertainty and static nature of traditional quantitative risk analysis studies.
Type of Study: Research |
Subject:
Risk assessment
Received: 2024/05/26 | Accepted: 2024/10/20 | Published: 2025/04/1
Received: 2024/05/26 | Accepted: 2024/10/20 | Published: 2025/04/1
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This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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