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城镇燃气管道事故动态贝叶斯网络情景推演及模拟 被引量:8

Deduction and simulation of DBN scenarios in urban gas pipeline accidents
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摘要 为防止城镇燃气管道泄漏造成的人员伤亡和财产损失,利用情景构建及情景推演理论,研究城镇燃气管道事故情景的演变路径,并探求事故的影响范围。以吉林松原“7·4”城市燃气管道泄漏爆炸事故为例,基于情景状态、应急目标、应急处置措施、外部环境影响等因素,建立城镇燃气管道事故动态贝叶斯网络模型,构建情景网络,计算情景概率,并分析事故演变规律。在情景构建与推演的基础上,利用FLACS软件对城镇燃气管道事故情景进行模拟分析。结果表明:减少城镇燃气管道事故伤亡及损失的最根本着眼点在于防止燃气管道泄漏,同时采取合理有效的应急措施有效改变事故的演变方向。研究成果可为城镇燃气管道的事后应急处置提供参考。 To prevent the casualties and property losses caused by urban gas pipeline leakage,the scenario evolution paths of urban gas pipeline accidentswere studied using the scenario construction and deduction theory,and the influence range of the accidents was explored.With the“7.4”gas pipeline leakage and explosion accident in Songyuan,Jilin as an example,a dynamic Bayesian network model of urban gas pipeline accident was established based on the situational state,emergency target,emergency disposal measures,external environmental impact and other factors.In this way,the scenario network was constructed,the scenario probability was calculated,and the accident evolution law was analyzed.In addition,simulation analysis was performed with FLACS software for the scenario of urban gas pipeline accident based on scenario construction and deduction.The results show that:the casualties and losses of urban gas pipeline accidents can be reduced by fundamentally focusing on the prevention of gas leakage.Meanwhile,reasonable and effective emergency measures should be taken to effectively change the evolution direction of accident.Conclusively,the research results could provide reference for the emergency disposal of urban gas pipelines after accidents.
作者 徐后佳 帅健 杨敬东 孟伟 XU Houjia;SHUAI Jian;YANG Jingdong;MENG Wei(College of Safety and Ocean Engineering,China University of Petroleum(Beijing);Shenzhen Gas Corporation Ltd;Zhejiang Energy Gas Group Co.Ltd.)
出处 《油气储运》 CAS 北大核心 2022年第8期900-907,共8页 Oil & Gas Storage and Transportation
基金 国家自然科学基金资助项目“基于应变的管道环焊缝断裂评估方法研究”,51874324。
关键词 城镇燃气管道 情景构建 情景推演 动态贝叶斯网络 事故后果分析 urban gas pipelines scenario construction scenario deduction dynamic Bayesian network accident consequence analysis
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