Volume 14, Issue 4 (12-2024)
J Health Saf Work 2024, 14(4): 736-755 |
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Ghaljahi M, Omidi L, Karimi A. Evaluation of Domino Effects and Vulnerability Analysis of Oil Product Storage Tanks Using Graph Theory and Bayesian Networks in a Process Industry. J Health Saf Work 2024; 14 (4) :736-755
URL: http://jhsw.tums.ac.ir/article-1-7083-en.html
URL: http://jhsw.tums.ac.ir/article-1-7083-en.html
1- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran | Department of Occupational Health Engineering, School of Public Health, Zabol University of Medical Sciences, Zabol, Iran
2- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
3- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ,a_karimi@sina.tums.ac.ir
2- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
3- Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ,
Abstract: (174 Views)
Introduction: Safety in process industries is of paramount importance, as these industries typically deal with hazardous chemicals and complex processes that can lead to irreparable consequences in the event of accidents. The present study aims to evaluate domino effects and analyze the vulnerability of storage tanks using graph theory and Bayesian networks in a process industry. This approach can help identify system vulnerabilities and facilitate the prediction of potential accidents, ultimately leading to improved safety measures.
Material and Methods: In this study, after collecting initial information related to the location of storage tanks and determining accident scenarios, the tanks under investigation were selected based on the type of stored materials and their layout, with input from experts. These tanks were modeled as nodes in a graph, and the probability of accident spreading among them was represented as edges in the graph based on the amount of heat radiation. Additionally, for modeling domino effects and analyzing vulnerability, graph theory and Bayesian networks were employed.
Results: Based on the target tanks related to the pool fire scenario, domino effects in the tanks were identified and modeled as a theory graph. Tank number 4 was determined to be the most influential and susceptible tank in the spread and initiation of domino effects, with the highest betweenness index (0.2381), outcloseness index (0.35211), and incloseness index (0.3663). Additionally, based on the allcloseness index, the most likely sequence of the tank involvement in fires caused by domino effects was identified.
Conclusion: In order to reduce the likelihood of exacerbating domino effects, modeling the effects using Bayesian networks and graph theory is proposed; the results can also be applied to optimize fire suppression strategies. Additionally, vulnerability analysis through graph theory and the assessment of tanks regarding their potential for fire initiation and spread can be beneficial in managing the risks associated with domino effects.
Material and Methods: In this study, after collecting initial information related to the location of storage tanks and determining accident scenarios, the tanks under investigation were selected based on the type of stored materials and their layout, with input from experts. These tanks were modeled as nodes in a graph, and the probability of accident spreading among them was represented as edges in the graph based on the amount of heat radiation. Additionally, for modeling domino effects and analyzing vulnerability, graph theory and Bayesian networks were employed.
Results: Based on the target tanks related to the pool fire scenario, domino effects in the tanks were identified and modeled as a theory graph. Tank number 4 was determined to be the most influential and susceptible tank in the spread and initiation of domino effects, with the highest betweenness index (0.2381), outcloseness index (0.35211), and incloseness index (0.3663). Additionally, based on the allcloseness index, the most likely sequence of the tank involvement in fires caused by domino effects was identified.
Conclusion: In order to reduce the likelihood of exacerbating domino effects, modeling the effects using Bayesian networks and graph theory is proposed; the results can also be applied to optimize fire suppression strategies. Additionally, vulnerability analysis through graph theory and the assessment of tanks regarding their potential for fire initiation and spread can be beneficial in managing the risks associated with domino effects.
Type of Study: Research |
Received: 2025/01/6 | Accepted: 2024/12/30 | Published: 2024/12/30
Received: 2025/01/6 | Accepted: 2024/12/30 | Published: 2024/12/30
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