Volume 14, Issue 3 (10-2024)
J Health Saf Work 2024, 14(3): 614-630 |
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Khodabakhsh Z, Omidi L, Mostafaee Dolatabad K, Aleahmad M, Joveini H. Application of Bayesian networks in fire domino effects modeling in gasoline storage tanks area. J Health Saf Work 2024; 14 (3) :614-630
URL: http://jhsw.tums.ac.ir/article-1-7039-en.html
URL: http://jhsw.tums.ac.ir/article-1-7039-en.html
Zahra Khodabakhsh1 
, Leila Omidi * 2, Khadijeh Mostafaee Dolatabad3 , Matin Aleahmad4 , Hossein Joveini5
, Leila Omidi * 2, Khadijeh Mostafaee Dolatabad3 , Matin Aleahmad4 , Hossein Joveini5
1- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
2- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ,omidil@sina.tums.ac.ir
3- Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
4- Department of Industrial Engineering, Faculty of Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran
5- Research and Development Department, Sari Firefighting and Safety Services Organization, Mazandaran, Iran
2- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ,
3- Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
4- Department of Industrial Engineering, Faculty of Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran
5- Research and Development Department, Sari Firefighting and Safety Services Organization, Mazandaran, Iran
Abstract: (195 Views)
Introduction: Domino effects are a chain of low-probability and high-consequence accidents in which a primary event (fire or explosion) in one unit causes secondary events in adjacent units. Bayesian networks have been used to model the propagation patterns of domino effects and to estimate the probability of these effects at different levels. The unique modeling and flexible structure provided by Bayesian networks allow the analysis of domino effects through a probabilistic framework, taking synergistic effects into account.
Material and Methods: Firstly, collecting the basic information related to the location of the storage tanks and determining the scenario of the accidents were done. Furthermore, the values of the heat radiation as escalation vectors in case of a fire in one tank were determined using ALOHA software. The received heat flux values were compared with the heat radiation threshold of 15 kw/m2 and the escalation probability of the primary unit and the propagation of the initial scenario to nearby storage tanks were determined using Bayesian networks.
Results: The analysis of the heat flux values showed that among the 8 studied storage tanks, two storage tanks had the highest potential for spreading domino effects due to their location in a tank farm. Also, the implementation of Bayesian networks in GeNIe revealed that, compared to other storage tanks, the probability of domino effects propagating to other nodes is higher when a primary fire accident occurs in the two mentioned tanks, while considered as primary units.
Conclusion: Domino effect modeling and appropriate preventative measures can decrease the escalation probability in the process industries. Consideration of the synergistic effects of events at different levels by taking the escalation vectors into account leads to proper risk management and the determination of emergency response measures in storage tank farms.
Material and Methods: Firstly, collecting the basic information related to the location of the storage tanks and determining the scenario of the accidents were done. Furthermore, the values of the heat radiation as escalation vectors in case of a fire in one tank were determined using ALOHA software. The received heat flux values were compared with the heat radiation threshold of 15 kw/m2 and the escalation probability of the primary unit and the propagation of the initial scenario to nearby storage tanks were determined using Bayesian networks.
Results: The analysis of the heat flux values showed that among the 8 studied storage tanks, two storage tanks had the highest potential for spreading domino effects due to their location in a tank farm. Also, the implementation of Bayesian networks in GeNIe revealed that, compared to other storage tanks, the probability of domino effects propagating to other nodes is higher when a primary fire accident occurs in the two mentioned tanks, while considered as primary units.
Conclusion: Domino effect modeling and appropriate preventative measures can decrease the escalation probability in the process industries. Consideration of the synergistic effects of events at different levels by taking the escalation vectors into account leads to proper risk management and the determination of emergency response measures in storage tank farms.
Type of Study: Research |
Received: 2024/10/10 | Accepted: 2024/10/1 | Published: 2024/10/1
Received: 2024/10/10 | Accepted: 2024/10/1 | Published: 2024/10/1
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