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化工储罐区池火灾多米诺事故动态概率及定量风险计算方法研究。

Study on Dynamic Probability and Quantitative Risk Calculation Method of Domino Accident in Pool Fire in Chemical Storage Tank Area.

机构信息

Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China.

China Academy of Safety Science and Technology, Beijing 100012, China.

出版信息

Int J Environ Res Public Health. 2022 Dec 8;19(24):16483. doi: 10.3390/ijerph192416483.

Abstract

The domino event caused by fire is one of the common accidents in hydrocarbon storage tank farms, which further expands the severity and scope of the accident. Due to the different failure sequence of the storage tanks in a domino accident, the radiant heat generated by the failed storage tank to the target tank is different. Based on the influence of this synergistic effect, this study combined the Monte Carlo algorithm and FSEM, and proposed a fast real-time probability calculation method for a fire domino accident in a storage tank area, for the first time. This method uses the Monte Carlo algorithm to simulate all accident scenarios, and obtains the evolution of multiple escalation fire domino accidents under the synergistic effect according to FSEM, and then calculates the real-time failure probability and risk. Based on a comprehensive analysis of the accident propagation path, this method avoids the problem of a large amount of calculation, and is conducive to the rapid and effective analysis of the fire risk in a storage tank area and the formulation of corresponding risk reduction measures. The effectiveness and superiority of the proposed method were proved by a case study.

摘要

火灾多米诺效应是烃类储罐区常见事故之一,会进一步扩大事故的严重程度和波及范围。由于在多米诺事故中各个储罐的失效序列不同,失效储罐向目标罐传递的热辐射也存在差异。基于这种协同作用的影响,本研究首次结合蒙特卡罗算法和 FSEM,提出了一种用于储罐区火灾多米诺事故的快速实时概率计算方法。该方法使用蒙特卡罗算法模拟所有事故场景,并根据 FSEM 得到协同作用下的多起级联火灾多米诺事故的演化,然后计算实时失效概率和风险。该方法通过综合分析事故传播路径,避免了大量计算的问题,有利于快速有效地分析储罐区的火灾风险,并制定相应的风险降低措施。通过案例研究证明了所提方法的有效性和优越性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e75/9778777/f83a4f965dd8/ijerph-19-16483-g001.jpg

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