Showing 3 results for Domino Effects
Mostafa Mirzaei Aliabadi, Hossein Ramezani, Omid Kalatpour,
Volume 12, Issue 1 (3-2022)
Abstract
Introduction: In process industries, some of the primary events may result in secondary events in an industrial unit called the domino effect. Since refinery storage tanks are always at risk of fire and explosion, quantitative risk assessment is important in determining the severity and outcome of an accident, taking into account the effects of dominoes on the main industry, neighbors, and society and can play an important role in risk management. Therefore, the purpose of this study was to quantitatively evaluate the risk of condensate storage tanks taking into account the domino effect.
Material and Methods: The technique used in this study was Quantitative Risk Assessment (QRA), the analysis of the consequences of which was performed using PHAST (7.22) after setting goals, studying the process, identifying hazards and scenarios. Then, to determine the extent of the domino effects of the escalation vectors were matched against the threshold, and after screening, the overall vulnerability of the repositories for mapping individual risk levels was calculated.
Results: In the leakage scenario, after considering the domino effects, the risk contour 10-4 to about 250 meters and the risk contour 10-5 to about 400 meters increased. Also in the catastrophic rupture scenario, the radius of risk contour of the 10-5 increased to100 m after considering the domino effects up to around damage tank.
Conclusion: As can be deduced from the results, using this method can give a clear picture of the consequences of chain events and the probability of damage to nearby employees, equipment and neighbors, which is very important in risk, emergency and crisis management.
Zahra Khodabakhsh, Leila Omidi, Khadijeh Mostafaee Dolatabad, Matin Aleahmad, Hossein Joveini,
Volume 14, Issue 3 (10-2024)
Abstract
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.
Maryam Ghaljahi, Leila Omidi, Ali Karimi,
Volume 14, Issue 4 (12-2024)
Abstract
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.
