Article(id=1149743085887861095, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149743083069288795, articleNumber=1003-3033(2024)06-0173-08, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.06.1848, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1702915200000, receivedDateStr=2023-12-19, revisedDate=1710950400000, revisedDateStr=2024-03-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1752049712869, onlineDateStr=2025-07-09, pubDate=1719504000000, pubDateStr=2024-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752049712869, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752049712869, creator=13701087609, updateTime=1752049712869, updator=13701087609, issue=Issue{id=1149743083069288795, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='6', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752049712197, creator=13701087609, updateTime=1756468919644, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1168278582599098697, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149743083069288795, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1168278582599098698, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149743083069288795, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=173, endPage=180, ext={EN=ArticleExt(id=1149743086949020023, articleId=1149743085887861095, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Correlation and traceability analysis of hazardous chemical explosion accidents based on Bayesian network, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to improve the effectiveness of hidden danger investigation,a reasoning model for hidden danger of explosive accidents of hazardous chemicals was proposed based on BN. First,according to the theory of combustion and explosion and expert experience,the main types of hidden dangers affecting the explosion of hazardous chemicals were determined,and BN model of explosion,leakage and ignition source was constructed. The prior and conditional probabilities of hidden dangers were determined by triangular fuzzy numbers. Then,Bayesian forward reasoning was used to calculate the probability of explosions of hazardous chemicals. Combined with reverse reasoning,the hidden dangers that were most likely to lead to explosion accidents were identified,and the formation of hidden risk factors was traced back. Finally,Bayes sensitivity analysis method was used to determine the key hazards affecting the explosion of hazardous chemicals and the correlation between the accident hazards,and it was verified by an explosion case of oil storage tank. The results show that the explosion probability of the storage tank calculated by causal reasoning is about 4.7%. Equipment overpressure and electric spark have the highest posterior probability,which is the most likely to lead to the explosion of the storage tank. The reason can be traced back to the failure to inspect the storage tank regularly or the failure to use explosion-proof equipment. Electrical spark and valve or flange damage are the key hidden dangers of oil storage tank explosion,and the accident hidden danger is related to the chain of "valve or flange damage → abnormal oil gusher → leakage exceeding limit → fire source (electrical spark) → explosion".

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为提高隐患排查工作实效,基于贝叶斯网络(BN),提出一种危化品爆炸事故隐患推理模型。首先,依据燃烧爆炸理论和专家经验,确定影响危化品爆炸的主要隐患类别,构建爆炸、泄漏超限、点火源的BN模型,并采用三角模糊数厘定隐患的先验和条件概率;然后,利用贝叶斯正向推理计算危化品爆炸发生概率,结合反向推理,查明最可能导致爆炸事故发生的隐患并追溯形成隐患的风险因素;最后,应用贝叶斯敏感性分析方法,确定影响危化品爆炸的关键隐患及事故隐患间的关联,并通过储油罐爆炸案例进行验证。研究结果表明:以因果推理方式计算出的储油罐爆炸风险概率约为4.7%;设备超压和电火花两者后验概率最大,是最可能导致储油罐爆炸的隐患,追溯其原因是未定期检查储罐或未使用防爆设备;电火花和阀门或法兰损坏是储油罐爆炸的关键隐患,事故隐患关联以致患链“阀门或法兰损坏→油品异常涌出→泄漏超限→遇火源(电火花)→发生爆炸”的形式呈现。

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姜子建 (1999—),男,北京人,硕士研究生,主要研究方向为系统安全工程、危险化学品安全。E-mail:

周荣义 副教授

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Study on the assistant analysis for accident risk management based on fuzzy system and BN[J]. Safety and Environmental Engineering, 2015, 22(3):105-110., articleTitle=Study on the assistant analysis for accident risk management based on fuzzy system and BN, refAbstract=null)], funds=[Fund(id=1168181757850427998, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, awardId=52074118, language=CN, fundingSource=国家自然科学基金资助(52074118), fundOrder=null, country=null), Fund(id=1168181757892371039, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, awardId=20A192, language=CN, fundingSource=湖南省教育厅重点项目(20A192), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1168181753488351762, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, xref=1, ext=[AuthorCompanyExt(id=1168181753496740371, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, companyId=1168181753488351762, language=EN, country=null, province=null, city=null, 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tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, companyId=1168181753660318232, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 地下空间防火防爆材料与装备湖南省工程研究中心,湖南 湘潭 411201)])], figs=[ArticleFig(id=1168181756294341190, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Fig.1, caption=Framework of analysis process, figureFileSmall=mf6TYbXKiDYcC4CvrHorEw==, figureFileBig=EbNsGmEV1Dm0upjb+Y2OKQ==, tableContent=null), ArticleFig(id=1168181756344672839, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=图1, caption=分析过程框架, figureFileSmall=mf6TYbXKiDYcC4CvrHorEw==, figureFileBig=EbNsGmEV1Dm0upjb+Y2OKQ==, tableContent=null), ArticleFig(id=1168181756395004488, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Fig.2, caption=Storage tank explosion conditions, figureFileSmall=RGtkNWhjk6TS4UlM6Fny1g==, figureFileBig=xzco9ZsC+2J8EvG65haO+A==, tableContent=null), ArticleFig(id=1168181756441141833, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=图2, caption=储油罐爆炸发生条件, figureFileSmall=RGtkNWhjk6TS4UlM6Fny1g==, figureFileBig=xzco9ZsC+2J8EvG65haO+A==, tableContent=null), ArticleFig(id=1168181756504056394, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Fig.3, caption=BN of oil storage tank explosion, figureFileSmall=rU3W9a3AnMbsul6Dwzfq8w==, figureFileBig=juqmNJTLPfaXAh69dCBVOw==, tableContent=null), ArticleFig(id=1168181756575359563, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=图3, caption=储油罐爆炸BN, figureFileSmall=rU3W9a3AnMbsul6Dwzfq8w==, figureFileBig=juqmNJTLPfaXAh69dCBVOw==, tableContent=null), ArticleFig(id=1168181756629885516, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Fig.4, caption=BN of oil storage tank explosion under normal condition, figureFileSmall=x7NwziE3iZeM6Lf2vgLsbQ==, figureFileBig=LhsBNFJUNKq25PyHK7wq/w==, tableContent=null), ArticleFig(id=1168181756688605773, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=图4, caption=正常状态下储油罐爆炸BN, figureFileSmall=x7NwziE3iZeM6Lf2vgLsbQ==, figureFileBig=LhsBNFJUNKq25PyHK7wq/w==, tableContent=null), ArticleFig(id=1168181756743131726, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Fig.5, caption=BN of oil storage tank explosion, figureFileSmall=82V3dAgCVnEDNRg2vsoQcg==, figureFileBig=o5LnE9OxWu98wC1wLnoWkw==, tableContent=null), ArticleFig(id=1168181756789269071, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=图5, caption=储油罐发生爆炸时BN, figureFileSmall=82V3dAgCVnEDNRg2vsoQcg==, figureFileBig=o5LnE9OxWu98wC1wLnoWkw==, tableContent=null), ArticleFig(id=1168181756839600720, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Fig.6, caption=Posterior probability of hazard, figureFileSmall=Ww19vZ6bqnnTpmyfawgmig==, figureFileBig=QwTOmYlM7YPQs1Gx4dtMJA==, tableContent=null), ArticleFig(id=1168181756898320977, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=图6, caption=隐患类别后验概率, figureFileSmall=Ww19vZ6bqnnTpmyfawgmig==, figureFileBig=QwTOmYlM7YPQs1Gx4dtMJA==, tableContent=null), ArticleFig(id=1168181756982207058, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Fig.7, caption=Sensitivity of hazard, figureFileSmall=0UEimfXtXNyzi4jQ0VbIxw==, figureFileBig=ZD6ZtZ+1w4MMNuuYq0nxTQ==, tableContent=null), ArticleFig(id=1168181757036733011, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=图7, caption=隐患类别敏感度, figureFileSmall=0UEimfXtXNyzi4jQ0VbIxw==, figureFileBig=ZD6ZtZ+1w4MMNuuYq0nxTQ==, tableContent=null), ArticleFig(id=1168181757095453268, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Table 1, caption=

Fuzzy number form

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 模糊语言 三角模糊数
1 非常高 (0.83,1,1)
2 (0.67,0.83,1)
3 较高 (0.5,0.67,0.83)
4 中等 (0.33,0.5,0.67)
5 较低 (0.17,0.33,0.5)
6 (0,0.17,0.33)
7 非常低 (0,0,0.17)
), ArticleFig(id=1168181757149979221, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=表1, caption=

模糊数形式

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 模糊语言 三角模糊数
1 非常高 (0.83,1,1)
2 (0.67,0.83,1)
3 较高 (0.5,0.67,0.83)
4 中等 (0.33,0.5,0.67)
5 较低 (0.17,0.33,0.5)
6 (0,0.17,0.33)
7 非常低 (0,0,0.17)
), ArticleFig(id=1168181757221282390, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Table 2, caption=

Hidden danger and description of oil storage tank explosion

, figureFileSmall=null, figureFileBig=null, tableContent=
因素 根节点 描述 状态
物料控制
设施F1
阀门或法兰损坏B1 阀门受力变形、法兰螺丝紧偏等 是、否
阀门或法兰密封失效B2 法兰密封垫片破裂、老化失弹、垫片偏装等 是、否
罐体破裂B3 未严格控制充装系数,储罐内温度升高
体积膨胀,引发储罐或阀门管道破裂
是、否
管道破裂B4 是、否
物料超量充装B5 不适当、可接受、合适
设备缺陷或安装不当B6 有缺陷的焊缝和防腐处理不到位的部位发生腐蚀 不适当、可接受、合适
物料监测
措施F2
安全阀未起跳B7 装置故障导致其未起跳、起跳压力失控、起跳高度不够等 经常、偶尔、无
自控联锁系统失效B8 联锁系统均采用电信号控制,供电系统
出现故障等会导致其失效
经常、偶尔、无
压力表、液位计损坏B9 仪表本身质量问题,或对仪表的故障诊断方法有误 经常、偶尔、无
超压爆炸F3 设备超压B10 操作失误或工艺条件异常,油品在储罐内气化 是、否
明火B11 罐区内违章动火 是、否
电火花(静电)B12 电气故障,或使用非防爆电器(防静电装置失效) 是、否
雷击B13 避雷装置故障,接地端损坏 是、否
高温表面B14 热量积聚使得油品温度持续上升,最终形成火源 是、否
摩擦撞击火花B15 使用铁制工具或穿带钉鞋 是、否
), ArticleFig(id=1168181757300974167, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=表2, caption=

储油罐爆炸隐患类别及描述

, figureFileSmall=null, figureFileBig=null, tableContent=
因素 根节点 描述 状态
物料控制
设施F1
阀门或法兰损坏B1 阀门受力变形、法兰螺丝紧偏等 是、否
阀门或法兰密封失效B2 法兰密封垫片破裂、老化失弹、垫片偏装等 是、否
罐体破裂B3 未严格控制充装系数,储罐内温度升高
体积膨胀,引发储罐或阀门管道破裂
是、否
管道破裂B4 是、否
物料超量充装B5 不适当、可接受、合适
设备缺陷或安装不当B6 有缺陷的焊缝和防腐处理不到位的部位发生腐蚀 不适当、可接受、合适
物料监测
措施F2
安全阀未起跳B7 装置故障导致其未起跳、起跳压力失控、起跳高度不够等 经常、偶尔、无
自控联锁系统失效B8 联锁系统均采用电信号控制,供电系统
出现故障等会导致其失效
经常、偶尔、无
压力表、液位计损坏B9 仪表本身质量问题,或对仪表的故障诊断方法有误 经常、偶尔、无
超压爆炸F3 设备超压B10 操作失误或工艺条件异常,油品在储罐内气化 是、否
明火B11 罐区内违章动火 是、否
电火花(静电)B12 电气故障,或使用非防爆电器(防静电装置失效) 是、否
雷击B13 避雷装置故障,接地端损坏 是、否
高温表面B14 热量积聚使得油品温度持续上升,最终形成火源 是、否
摩擦撞击火花B15 使用铁制工具或穿带钉鞋 是、否
), ArticleFig(id=1168181757397443160, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Table 3, caption=

Fuzzy prior probability of node B7

, figureFileSmall=null, figureFileBig=null, tableContent=
专家编号
及结果
状态和概率
经常(B7-1) 偶尔(B7-2) 无(B7-3)
专家1 (0,0,0.17) (0,0,0.17) (0.67,0.83,1)
专家2 (0,0,0.17) (0,0.17,0.33) (0.67,0.83,1)
专家3 (0,0.17,0.33) (0,0.17,0.33) (0.83,1,1)
专家4 (0,0,0.17) (0,0,0.17) (0.5,0.67,
0.83)
模糊概
P ~ i j
(0,0.042 5,
0.21)
(0,0.085,
0.25)
(0.667 5,
0.832 5,
0.957 5)
标准概
P i j
0.07 0.1 0.83
), ArticleFig(id=1168181757460357721, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=表3, caption=

节点B7的模糊先验概率

, figureFileSmall=null, figureFileBig=null, tableContent=
专家编号
及结果
状态和概率
经常(B7-1) 偶尔(B7-2) 无(B7-3)
专家1 (0,0,0.17) (0,0,0.17) (0.67,0.83,1)
专家2 (0,0,0.17) (0,0.17,0.33) (0.67,0.83,1)
专家3 (0,0.17,0.33) (0,0.17,0.33) (0.83,1,1)
专家4 (0,0,0.17) (0,0,0.17) (0.5,0.67,
0.83)
模糊概
P ~ i j
(0,0.042 5,
0.21)
(0,0.085,
0.25)
(0.667 5,
0.832 5,
0.957 5)
标准概
P i j
0.07 0.1 0.83
), ArticleFig(id=1168181757523272282, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=EN, label=Table 4, caption=

Conditional probability of node "F2" ( F 2 B 7 B 8 B 9 )

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), ArticleFig(id=1168181757586186843, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=表4, caption=

节点“F2”的条件概率 P ( F 2 B 7 B 8 B 9 )

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Comparison of research results

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模型 分析结果
隐患 风险因素
文献[15] 充装过量、阀门或法兰泄漏、人的误操作、未使用防爆电器或防爆电器损坏等
文中 阀门或法兰损坏、设备超压和电火花(静电) 长时间未检查或现阶段所用阀门选型不当、法兰螺丝安装不当、过量储存、未使用防爆设备或防爆电器损坏、员工工作中与带电体接触等
), ArticleFig(id=1168181757732987485, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149743085887861095, language=CN, label=表5, caption=

研究结果对比

, figureFileSmall=null, figureFileBig=null, tableContent=
模型 分析结果
隐患 风险因素
文献[15] 充装过量、阀门或法兰泄漏、人的误操作、未使用防爆电器或防爆电器损坏等
文中 阀门或法兰损坏、设备超压和电火花(静电) 长时间未检查或现阶段所用阀门选型不当、法兰螺丝安装不当、过量储存、未使用防爆设备或防爆电器损坏、员工工作中与带电体接触等
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基于贝叶斯网络的危化品爆炸事故隐患关联与溯源分析
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姜子建 1, 2 , 周荣义 1, 2, 3 , 石云霄 1 , 刘灿 1 , 杨璧帆 1 , 郑时求 1
中国安全科学学报 | 安全工程技术 2024,34(6): 173-180
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中国安全科学学报 | 安全工程技术 2024, 34(6): 173-180
基于贝叶斯网络的危化品爆炸事故隐患关联与溯源分析
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姜子建1, 2 , 周荣义1, 2, 3, 石云霄1, 刘灿1, 杨璧帆1, 郑时求1
作者信息
  • 1 湖南科技大学 资源环境与安全工程学院,湖南 湘潭 411201
  • 2 火灾爆炸防控与应急技术湖南省普通高等学校重点实验室,湖南 湘潭 411201
  • 3 地下空间防火防爆材料与装备湖南省工程研究中心,湖南 湘潭 411201
  • 姜子建 (1999—),男,北京人,硕士研究生,主要研究方向为系统安全工程、危险化学品安全。E-mail:

    周荣义 副教授

Correlation and traceability analysis of hazardous chemical explosion accidents based on Bayesian network
Zijian JIANG1, 2 , Rongyi ZHOU1, 2, 3, Yunxiao SHI1, Can LIU1, Bifan YANG1, Shiqiu ZHENG1
Affiliations
  • 1 School of Resource & Environment and Safety Engineering,Hunan University of Science and Technology,Xiangtan Hunan 411201,China
  • 2 Key Laboratory of Fire and Explosion Prevention and Emergency Technology in Hunan Province,Xiangtan Hunan 411201,China
  • 3 Hunan Engineering Research Center for Fire and Explosion Prevention Materials and Equipment in Underground Spaces,Xiangtan Hunan 411201,China
出版时间: 2024-06-28 doi: 10.16265/j.cnki.issn1003-3033.2024.06.1848
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为提高隐患排查工作实效,基于贝叶斯网络(BN),提出一种危化品爆炸事故隐患推理模型。首先,依据燃烧爆炸理论和专家经验,确定影响危化品爆炸的主要隐患类别,构建爆炸、泄漏超限、点火源的BN模型,并采用三角模糊数厘定隐患的先验和条件概率;然后,利用贝叶斯正向推理计算危化品爆炸发生概率,结合反向推理,查明最可能导致爆炸事故发生的隐患并追溯形成隐患的风险因素;最后,应用贝叶斯敏感性分析方法,确定影响危化品爆炸的关键隐患及事故隐患间的关联,并通过储油罐爆炸案例进行验证。研究结果表明:以因果推理方式计算出的储油罐爆炸风险概率约为4.7%;设备超压和电火花两者后验概率最大,是最可能导致储油罐爆炸的隐患,追溯其原因是未定期检查储罐或未使用防爆设备;电火花和阀门或法兰损坏是储油罐爆炸的关键隐患,事故隐患关联以致患链“阀门或法兰损坏→油品异常涌出→泄漏超限→遇火源(电火花)→发生爆炸”的形式呈现。

危化品爆炸  /  贝叶斯网络(BN)  /  事故隐患  /  三角模糊数  /  后验概率

In order to improve the effectiveness of hidden danger investigation,a reasoning model for hidden danger of explosive accidents of hazardous chemicals was proposed based on BN. First,according to the theory of combustion and explosion and expert experience,the main types of hidden dangers affecting the explosion of hazardous chemicals were determined,and BN model of explosion,leakage and ignition source was constructed. The prior and conditional probabilities of hidden dangers were determined by triangular fuzzy numbers. Then,Bayesian forward reasoning was used to calculate the probability of explosions of hazardous chemicals. Combined with reverse reasoning,the hidden dangers that were most likely to lead to explosion accidents were identified,and the formation of hidden risk factors was traced back. Finally,Bayes sensitivity analysis method was used to determine the key hazards affecting the explosion of hazardous chemicals and the correlation between the accident hazards,and it was verified by an explosion case of oil storage tank. The results show that the explosion probability of the storage tank calculated by causal reasoning is about 4.7%. Equipment overpressure and electric spark have the highest posterior probability,which is the most likely to lead to the explosion of the storage tank. The reason can be traced back to the failure to inspect the storage tank regularly or the failure to use explosion-proof equipment. Electrical spark and valve or flange damage are the key hidden dangers of oil storage tank explosion,and the accident hidden danger is related to the chain of "valve or flange damage → abnormal oil gusher → leakage exceeding limit → fire source (electrical spark) → explosion".

hazardous chemical explosion  /  Bayesian network (BN)  /  hidden danger of accident  /  triangular fuzzy number  /  posterior probability
姜子建, 周荣义, 石云霄, 刘灿, 杨璧帆, 郑时求. 基于贝叶斯网络的危化品爆炸事故隐患关联与溯源分析. 中国安全科学学报, 2024 , 34 (6) : 173 -180 . DOI: 10.16265/j.cnki.issn1003-3033.2024.06.1848
Zijian JIANG, Rongyi ZHOU, Yunxiao SHI, Can LIU, Bifan YANG, Shiqiu ZHENG. Correlation and traceability analysis of hazardous chemical explosion accidents based on Bayesian network[J]. China Safety Science Journal, 2024 , 34 (6) : 173 -180 . DOI: 10.16265/j.cnki.issn1003-3033.2024.06.1848
近年来,由企业安全隐患或者管理疏忽造成的事故屡见不鲜,化工类企业危险系数较大,其生产安全更不容忽视。据统计,2016—2020年,我国共发生化工和危险化学品(简称危化品)事故929起,其中,爆炸事故占总事故起数的34.4%;爆炸事故死亡人数占总死亡人数的33.0%,爆炸事故是导致人员伤亡最为严重的事故类型[1]。因此,研究危化品爆炸事故的隐患关联并溯源,对于有效预防此类事故,减少爆炸事故人员伤亡,具有重要意义。
诸多学者对此展开了系统研究,如裴甲坤等[2]利用改进的尖点突变模型,分析化工事故致因过程,得出化工爆炸事故是由固有危险源和不安全因素综合作用引起的;刘庆龙等[3]应用改进的模糊Petri网,建立正己烷缓冲罐火灾爆炸事故风险动态传播模型,并融合了多源异构数据,计算出安全联锁系统失效对事故发生影响最大;周荣义等[4]提出一种基于集对分析理论的危化品事故风险评估模型,进一步识别出风险因素的动态变化情况及薄弱环节;WU Chao等[5]构建了基于信息流的事故致因模型,并以天津港火灾爆炸事故为案例进行实证分析。陈伟珂[6]、JANG Wei[7]、付净[8]等根据危化品爆炸的特点,将事故致因模型和事故原因分析方法相结合,构建了全新的事故原因分析框架,以完整地展现出事故致因链条及其演变历程。还有一些学者[9-10]采用“情景-应对”型决策范式分析危化品爆炸事故情景的产生及风险因素的耦合,运用贝叶斯网络(Bayesian Network,BN)推理出事故发展趋势和事故成因机制。上述研究大多从危化品爆炸事故致因的风险因素出发,主要考虑事故整体的情景演化,而对风险因素转化为隐患再到事故发生这一连锁链中的中间环节隐患有所忽略,缺乏考虑演化过程中隐患对事故发展的影响。实际上,事故的发生首先是由隐患导致的,而隐患的形成是基于风险因素制定的管控措施失效的结果,因此事故发生与隐患形成有直接关联,所以忽略隐患这个中间环节而对危化品爆炸事故分析未充分体现其爆炸形成过程的客观性,不利于作出事故预防决策[11]
故此,笔者拟基于BN,探究危化品爆炸“事故-隐患”的关联性,并进行隐患的追本溯源分析,以期为危化品爆炸事故预防提供新思路,提高企业隐患排查工作实效。
BN作为一种进行不确定性推理的理论模型,可充分利用先验信息和专家经验知识,以图形的形式直观表达出随机变量间的因果关系并进行概率推理计算[12],因此,适用于描述危化品爆炸事故与隐患之间的关联,并且在对隐患出现概率推理计算方面具有显著优势。
BN是一个由节点和有向边组成的有向无环图,其中,节点代表随机变量,有向边表示节点间的关联强度。一般地,对于一个包括 n个节点的BN,假设网络中的变量为 X 1 X 2 X n,其联合概率P可以表示为:
P ( X 1 X 2 X n ) = i = 1 n P ( X i π i )
式中 π i X i父节点集。则 X i的边缘概率为:
P ( X i ) = 1,2 i - 1 P ( X 1 X 2 X n )
基于BN的危化品爆炸事故隐患关联与溯源分析过程分为隐患识别、BN模型构建、模糊概率计算和结果分析这4个阶段,如图1所示。
隐患识别的目的是在风险管控措施失效情况下,找出可能导致事故发生的隐患。首先,获取相关先验知识,包括危险化学品安全管理规范及相关文献中对危化品爆炸影响因素的分析。其次,依据燃烧爆炸理论和专家经验列出主要隐患类别。最后,结合专家知识确定危化品爆炸事故与隐患之间的因果关系。
选择合适的节点是构建危化品爆炸事故BN的前提。筛选某一种危化品在某一生产过程中可能导致爆炸事故发生的隐患,根据此类化学品的特性将信息不完整、出现概率极小以及关联性不强的隐患剔除,将确定的主要隐患类别作为BN节点,并构建具有多层次结构的BN。
采用模糊理论与专家调查相结合的方法,确定事故模型先验和条件概率。选用三角模糊数作为专家的语言描述,分别为非常高、高、较高、中等、较低、低、非常低,进而将语言描述转化为三角模糊概率,语言描述对应的三角模糊数见表1
若专家人数为 q P ~ i j k表示第 k位专家对第 i节点在第 j状态下发生概率的语言描述所转化的三角模糊数, P ~ i j k = ( a ~ i j k m ~ i j k b ~ i j k ) ( k = 1,2 q ),其中, a m b分别为可能性最小值、最可能的值和可能性最大值; P ~ i j表示第 i节点在第 j状态下综合专家的意见[13],具体公式为:
P ~ i j = P ~ i j 1 P ~ i j 2 P ~ i j q q = ( a ~ i j m ~ i j b ~ i j )
因三角模糊概率不利于贝叶斯推理计算,故采用均值面积法将其转化为去模糊值[13],具体公式为:
P i = a ~ i j + 2 m ~ i j + b ~ i j 4
式中 P i为去模糊值。
为使同一节点各状态下概率之和为1,需进行归一化处理,得到标准概率[14]:
P i j = P ' i j 1 m P ' i j
式中: P i j为归一化处理后的概率; P ' i j为归一化处理前的概率。
1) 因果推理与事故发生概率计算。因果推理(正向推理)是在已知隐患类别的状态下,依据条件概率分布,计算风险事件发生概率,用 P ( T )表示。具体公式如下:
P ( T = S t ) = P ( X i = x j ) × P ( T = S t X i = x j )
式中: S t为风险事件的第 t状态 ( t = 1,2 k ); P ( X i = x j )为根节点的联合概率; P ( T = S t X i = x j )为BN的条件概率表。
2) 诊断推理与隐患溯源。诊断推理(反向推理)是在已知风险事件发生状态下,依据条件概率分布,计算隐患类别后验概率。第 i个根节点后验概率可表示为 P ( X i = x j T = S t ),具体公式如下:
P ( X i = x j T = S t ) = P ( X i = x j ) P ( T = S t X i = x j ) P ( T = S t )
分析比较各节点后验概率值的大小,查明最可能导致危化品爆炸事故发生的隐患,并对这些隐患溯源,分析形成隐患的原因,提出新的措施加以管控,有助于减少隐患,预防事故。
3) 敏感性分析与事故隐患关联分析。在危化品爆炸BN模型中,以爆炸事故发生为目标节点,通过敏感性分析寻找关键性隐患,进而对事故作情景推演,得到爆炸事故发生与关键隐患之间的关联,并以致患链的形式呈现。致患链即最可能导致危化品爆炸发生的关键隐患路径,能最直观地表示事故与隐患之间的关联,针对导致危化品爆炸的关键致患链优先加以控制,以有效降低危化品爆炸发生的概率。
以文献[15]中的储油罐爆炸事故为例进行分析。
文中建模主要考虑泄漏超限和点火源这两者,因为氧气浓度极易达到,故在储油罐爆炸BN中,爆炸的父节点即储罐泄漏油蒸气浓度达到爆炸极限和点火源,如图2所示。
储油罐泄漏主要发生在焊接点、接口、法兰、阀门盘根附件连接处,其中,物料控制设施方面的隐患类别主要有阀门损坏、法兰密封失效、管道或罐体破裂、物料超量充装、设备缺陷或安装不当等;物料监测措施方面的隐患类别主要有自控联锁系统失效、压力表或液位计损坏,安全阀未起跳等。此外,还要考虑由储罐超压导致罐体发生物理爆炸后油品泄漏的特殊情况。引发储油罐爆炸的点火源主要有电火花(静电)、高温表面、摩擦撞击火花等。爆炸隐患类别及描述见表2,储油罐爆炸BN如图3所示。
邀请4位专家,厘定所建立的储油罐爆炸BN模型根节点的先验概率,对各节点在不同状态下的发生概率进行语言描述并通过式(3)得到模糊概率。根据式(4)进行三角模糊数向概率值的转化计算,并通过式(5)得到归一化后的标准概率。限于篇幅,文中以安全阀未起跳B7节点为例,4位专家的语言描述及经过计算得到的模糊先验概率见表3。 根据节点间的因果关系,由专家结合储油罐爆炸实际情况给出节点间的条件概率。限于篇幅仅列出节点“安全阀未起跳”、“自控联锁系统失效”、“压力表、液位计损坏”状态下节点“物料监测措施”的条件概率,见表4
基于构建的危化品爆炸BN模型,根据节点的先验和条件概率,利用GeNIe软件进行因果推理。在正常状态下,根据式(6)计算得到储油罐爆炸发生概率为0.047,不发生爆炸概率为0.953,建立的推理模型如图4所示。
假设储油罐爆炸已发生(P(储油罐爆炸)=1),根据式(7)计算得到各节点后验概率,分析比较各节点后验概率值的大小,查明最可能酿成储油罐爆炸事故的隐患类别,结果如图5图6所示。
图6可知:在储油罐爆炸事故已发生的情况下,阀门或法兰损坏P(B1-1)、设备超压P(B10-1)和电火花P(B12-1)三者后验概率值最大,说明储油罐爆炸往往是由这3个隐患导致的。
阀门或法兰损坏的原因可追溯到长时间未对储罐阀门、法兰进行检查或现阶段所用阀门选型不当、法兰螺丝安装不当等风险因素。设备超压的情况最可能是由于物料超量充装造成的(P(B5-1)后验概率较大),其主要原因可追溯到充装前未设置风险告知卡致使员工误操作。出现电火花(静电)的原因可追溯到未使用防爆设备,也可能是罐体防静电装置失效或员工工作中与带电体接触等。
因此,在隐患排查过程中,要着重排查储罐阀门、法兰的状态及罐区防爆电器的设置、运行情况;检查罐体接地装置安装情况,如接地电阻是否超标、引下线是否损坏、接地端是否损坏;也要检查员工是否按照操作规程进行作业,作业中是否穿戴防静电服、是否与带电体接触等。
基于构建的危化品爆炸BN,选择储油罐爆炸事故为目标节点,由GeNIe软件推理计算各节点间的敏感度,如图7所示。其中,阀门或法兰损坏、物料超量充装和电火花(静电)3个隐患类别敏感度最大,说明它们是导致储油罐爆炸事故发生的关键隐患。
综上,导致其发生爆炸的致患链共2条,路径1为物料超量充装→设备超压→监测措施失效→超压物理爆炸→储罐泄漏油蒸气浓度达到爆炸极限→遇电火花(静电)→发生爆炸;路径2为阀门或法兰损坏→油品异常涌出→监测措施失效→遇电火花(静电)→发生爆炸。因此,应针对这2条关键致患链优先加以隐患排查,以有效降低储油罐爆炸发生的概率。
为验证模型方法的准确性和优越性,将文中研究结果与文献[15]研究结果进行对比,结果见表5
表5可知:文献[15]未考虑隐患这一中间环节,而文中通过对“事故-隐患”关联分析可以准确得出油罐爆炸事故发生的关键隐患是阀门或法兰损坏、设备超压和电火花(静电),结果更加切合实际,推理性和可信性更强。同时,对建立在严谨逻辑推理之上的油罐爆炸事故隐患进行溯源分析,得到的风险因素比文献[15]的结果更加系统、全面和具可操作性,既可有效避免风险辨识与防控的遗漏,又有助于指导现场储罐设备的设计、运行和维护管理。文献[15]将阀门或法兰泄漏作为风险因素,而文中将阀门或法兰泄漏进行了溯源,发现导致阀门或法兰出现泄漏的风险因素是长时间未对其进行检查或所用阀门选型不当、法兰螺丝安装不当等,该结果更贴近实操和运行,可为储罐设备的运营和管理提供更加具体和切实可行的依据,方便采取点对点的应对措施。
1) 引入隐患,构建基于BN的危化品爆炸事故隐患推理模型,该模型不仅可以在特定情形下确定事故隐患关联性,反映隐患演变为事故的过程,还可以追溯形成隐患的风险因素,进而弥补只考虑风险因素所得结果不够客观的缺陷。
2) 贝叶斯诊断推理追溯形成储油罐爆炸事故隐患的风险因素是未定期检查储罐或未使用防爆设备等;敏感性分析得出事故隐患关联以致患链“阀门或法兰损坏→油品异常涌出→泄漏超限→遇火源(电火花)→发生爆炸”的形式呈现。
  • 国家自然科学基金资助(52074118)
  • 湖南省教育厅重点项目(20A192)
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2024年第34卷第6期
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doi: 10.16265/j.cnki.issn1003-3033.2024.06.1848
  • 接收时间:2023-12-19
  • 首发时间:2025-07-09
  • 出版时间:2024-06-28
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  • 收稿日期:2023-12-19
  • 修回日期:2024-03-21
基金
国家自然科学基金资助(52074118)
湖南省教育厅重点项目(20A192)
作者信息
    1 湖南科技大学 资源环境与安全工程学院,湖南 湘潭 411201
    2 火灾爆炸防控与应急技术湖南省普通高等学校重点实验室,湖南 湘潭 411201
    3 地下空间防火防爆材料与装备湖南省工程研究中心,湖南 湘潭 411201
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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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