Showing 3 results for Storage Tank
Ehsan Ramezanifar, Kamran Gholamizadeh, Iraj Mohammadfam, Mostafa Mirzaei Aliabadi,
Volume 13, Issue 1 (3-2023)
Abstract
Introduction: Risk assessment is a scale for predicting reliability and can manage interactions between components and process variables. Moreover, the reliability of one component or barrier affects the overall risk of the system. Being one of the most critical safety barriers of the storage tank, the failures of Fixed Foam Systems (FFS) on demand can result in severe consequences. FFS, is of grave importance in decreasing the risks associated with fires and damages.
Material and Methods: This study aims to determine the probability of root causes related to FFS failure through Fuzzy Fault Tree Analysis (FFTA) to estimate system reliability. In conventional fault tree analysis, accurate data is usually used to assess the failure probability of basic events. Therefore, the introduced approaches were employed to quantify failure probabilities and uncertainty handling. Finally, system reliability was estimated according to the failure probability of the top event.
Results: The findings showed that 13 baseline events involved FFS performance. According to the results, failures of cable path and detection system (or resistance temperature detectors), set the activation switch (multi-position) incorrectly, and foam makers not continuously running are the three most critical basic events influencing the reliability of fixed foam systems. In addition, this paper estimated the system reliability at 0.8470.
Conclusion: The results showed that the FFTA could be used in matters such as reliability evaluation failure and risk assessment using experts’ judgment. This paper can also show the adaptation of the fuzzy approach to assess the failure probability of the basic event in the fault tree analysis (FTA).
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.
