Development of a predictive accident model for dynamic risk assessment of propane storage tanks

故障树分析 易燃液体 事件树 炼油厂 可靠性工程 法律工程学 工程类 风险评估 故障评估 事件树分析 环境科学 废物管理 风险分析(工程) 计算机科学 结构工程 计算机安全 医学 断裂力学
作者
Kazem Sarvestani,Omran Ahmadi,Seyed Bagher Mortazavi,Hassan Asilian Mahabadi
出处
期刊:Chemical Engineering Research & Design [Elsevier BV]
卷期号:148: 1217-1232 被引量:46
标识
DOI:10.1016/j.psep.2021.02.018
摘要

Abstract Investigation of past accidents has shown that LPG tank accidents cause significant damage to the industry due to the storage of large volumes of flammable materials in them. This study aimed for developing a predictive accident model for dynamic risk assessment of propane storage tanks of the refinery. Hazards and safety barriers were identified using MIMAH methodology. The basis of MIMAH methodology is Bow-tie method. To construct the Bow-tie diagram, first, accidents that occurred on LPG tanks were extracted from the databases of accidents and valid sources. The top events of the accidents were identified and analyzed by the fault tree. The Bow-tie diagrams were constructed and the barriers on the diagrams were identified and verified by refinery experts. According to the SHIPP model, safety barriers were categorized into seven main barriers. The failure rates of the fault tree basic events were extracted from reliable sources and the prior probability of barriers was calculated. Based on the failure or success of safety barriers, 6 levels of severity of consequences, safe, near miss, mishap, incident, and catastrophic accident were considered. Using the prior probability of failure of the barriers, the probability of occurrence of each level of severity of consequences was calculated with the event tree. In the next step, this paper employed LPG storage tanks past accidents to construct a likelihood function and update prior probability using the Bayesian equation. Finally, the posterior probability of occurrence of the consequences was calculated using the posterior probability of failure of the barriers. Because LPG accidents occur with low probability and high severity, predicting accidents dynamically helps people to always be prepared to prevent their occurrence.
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