Volume 10, Issue 3 (Spring- In Press 2025)
Health in Emergencies and Disasters Quarterly 2025, 10(3): 0-0 |
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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)
URL: http://hdq.uswr.ac.ir/article-1-622-en.html
URL: http://hdq.uswr.ac.ir/article-1-622-en.html
1- Center of Excellence for Occupational Health, Occupational Health and Safety Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
2- Department of Ergonomics, Health Research Center in Accidents and Disasters, University of Rehabilitation Sciences and Social Health, Tehran, Iran. ,ir.mohammadfam@uswr.ac.ir
2- Department of Ergonomics, Health Research Center in Accidents and Disasters, University of Rehabilitation Sciences and Social Health, Tehran, Iran. ,
Abstract: (392 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 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 Bayesian network 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.
Discussion: Combining fuzzy theory and Bayesian networks allows fuzzy numbers to reduce uncertainty in basic events and safety barriers and leverages deductive and inductive reasoning.
Conclusion: The results demonstrate that applying fuzzy logic and Bayesian networks 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 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 Bayesian network 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.
Discussion: Combining fuzzy theory and Bayesian networks allows fuzzy numbers to reduce uncertainty in basic events and safety barriers and leverages deductive and inductive reasoning.
Conclusion: The results demonstrate that applying fuzzy logic and Bayesian networks 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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