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Article(id=1149739130256540051, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149739129056969102, articleNumber=1003-3033(2024)03-0162-09, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.03.1155, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1694620800000, receivedDateStr=2023-09-14, revisedDate=1702828800000, revisedDateStr=2023-12-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048769772, onlineDateStr=2025-07-09, pubDate=1711555200000, pubDateStr=2024-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048769772, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048769772, creator=13701087609, updateTime=1752048769772, updator=13701087609, issue=Issue{id=1149739129056969102, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='3', 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=1752048769486, creator=13701087609, updateTime=1756468931593, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1168278632783950282, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149739129056969102, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1168278632783950283, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149739129056969102, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=162, endPage=170, ext={EN=ArticleExt(id=1149739130466255252, articleId=1149739130256540051, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Multi-level disaster chain deduction analysis and case application of hazardous chemical accidents, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To enhance the risk prevention and control capabilities of hazardous chemical factories and support emergency decision-making in case of accidents，a multi-level deduction model for the disaster chain was proposed. Furthermore，three categories of key factors (such as thermal radiation，toxic gases，and overpressure) affecting hazardous chemical accidents were considered in the model. Based on the fluid diffusion model and Probit model，the fire probability and sequence simulation algorithm of hazardous chemicals container and the ignition time estimation algorithm of hazardous chemicals explosion were proposed，respectively. Then，the quantitative analysis of the combustion and explosion evolution process in hazardous chemical accidents was performed. For the case of a resin chemical production company in Guangdong province，a disaster chain caused by hazardous chemical leakage accidents was developed to analyze the evolution time and probability of each node. The results indicated that the proposed deduction model can effectively analyze the evolution process of the actual hazardous chemical disaster chain，predict the probability and time of accident nodes，and provide fundamental knowledge for the safety layout of hazardous chemical plants.

, correspAuthors=Fei WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Qinglun HE, Wenyu JIANG, Fei WANG, Xin LI, Zhi WANG), CN=ArticleExt(id=1149739137193919031, articleId=1149739130256540051, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=危化品事故多层级链式推演模型及案例分析, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为提升危化品企业的风险防控能力，支撑事故发生后迅速地进行应急决策研判，基于灾害链理论，从热辐射、毒气、超压3类危化品事故关键致灾因子出发，提出灾害链多层级链式推演模型。采用流体扩散模型和Probit模型，分别构建危化品容器起火概率及顺序预测算法和危化品爆炸点火时刻估计算法，实现对危化品事故中燃烧和爆炸演化过程的定量计算分析。并以广东省某树脂生产厂为研究案例，建立1条由危化品泄漏事故引发的灾害链，推演分析灾害链各节点的演化时间和发生概率。分析结果表明：灾害链推演模型能有效定量化分析实际危化品厂区事故的演化过程、预测事故节点发生概率及时间，并在一定程度上帮助检验危化品厂区布局安全。

, correspAuthors=王飞, authorNote=null, correspAuthorsNote=
** 王飞(1979—)，男，湖北武汉人，博士，副教授，主要从事安全工程、灾害建模和深度学习等方面的研究。E-mail：。
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=PNhzV0TAwiG9k4WjZvxX4g==, magXml=Y27jPI2jb4Xg6KJ6Br6txQ==, pdfUrl=null, pdf=udXo4sNBTMk8WqoOZEgAMw==, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=yado96XSp3vpvrVSuUMSCg==, mapNumber=null, authorCompany=null, fund=null, authors=

何青伦 (1999—)，男，云南保山人，硕士研究生，主要研究方向为地面塌陷、事故建模、灾害链等。E-mail：。

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何青伦 (1999—)，男，云南保山人，硕士研究生，主要研究方向为地面塌陷、事故建模、灾害链等。E-mail：。

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何青伦 (1999—)，男，云南保山人，硕士研究生，主要研究方向为地面塌陷、事故建模、灾害链等。E-mail：。

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LNG储罐爆炸和火灾危险性定量分析[J]. 石油化工安全环保技术, 2014, 30(2):36-40., articleTitle=LNG储罐爆炸和火灾危险性定量分析, refAbstract=null), Reference(id=1168130325810000701, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, doi=null, pmid=null, pmcid=null, year=2014, volume=30, issue=2, pageStart=36, pageEnd=40, url=null, language=null, rfNumber=[22], rfOrder=31, authorNames=ZHANG Jiguo, YU Qijie, journalName=Petrochemical Safety and Environmental Protection Technology, refType=null, unstructuredReference=ZHANG Jiguo, YU Qijie. Quantitative analysis of explosion and fire hazards in liquefied natural gas tank[J]. 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journalId=1146031787341344770, articleId=1149739130256540051, language=EN, label=Tab.1, caption=

Overpressure and failure probability of each tank

, figureFileSmall=null, figureFileBig=null, tableContent=
槽罐
编号		 与爆炸中心的
距离/m		 承受超压/
kPa		 失效概率
R02 12.68 48.78 0.9
V02 9.45 90.18 1
R03 7.22 158.28 1
V03 6 233.05 1
R04A 12.7 48.38 0.9
R04B 16.3 29 0.86
R05 19.97 18.88 0
R06 23.76 13.13 0
R08 2.5 1452.3 1
R09 6.5 197.15 1
R10 10.5 72.36 0.99
), ArticleFig(id=1168130322462946069, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, language=CN, label=表1, caption=

各槽罐超压及失效概率

, figureFileSmall=null, figureFileBig=null, tableContent=
槽罐
编号		 与爆炸中心的
距离/m		 承受超压/
kPa		 失效概率
R02 12.68 48.78 0.9
V02 9.45 90.18 1
R03 7.22 158.28 1
V03 6 233.05 1
R04A 12.7 48.38 0.9
R04B 16.3 29 0.86
R05 19.97 18.88 0
R06 23.76 13.13 0
R08 2.5 1452.3 1
R09 6.5 197.15 1
R10 10.5 72.36 0.99
), ArticleFig(id=1168130322525860630, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, language=EN, label=Tab.2, caption=

Thermal radiation value of each path and failure time of tank group exposed to thermal radiation

, figureFileSmall=null, figureFileBig=null, tableContent=
传播路径 热辐射值/
(kW·m-2)		 t f/min
1→2 23.99 9
1→3 17.2 13.1
1→4 32.04 6.5
12→3 21.84 10
12→4 34.54 6
13→2 28.63 7.38
13→4 36.23 5.66
14→2 26.49 8.05
14→3 21.39 10.25
123→4 38.73 5.25
124→3 26.03 8.22
132→4 38.73 5.25
134→2 31.13 6.71
142→3 26.03 8.22
143→2 31.13 6.71
), ArticleFig(id=1168130322681049879, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, language=CN, label=表2, caption=

热辐射作用下各路径热辐射值及罐组失效时间

, figureFileSmall=null, figureFileBig=null, tableContent=
传播路径 热辐射值/
(kW·m-2)		 t f/min
1→2 23.99 9
1→3 17.2 13.1
1→4 32.04 6.5
12→3 21.84 10
12→4 34.54 6
13→2 28.63 7.38
13→4 36.23 5.66
14→2 26.49 8.05
14→3 21.39 10.25
123→4 38.73 5.25
124→3 26.03 8.22
132→4 38.73 5.25
134→2 31.13 6.71
142→3 26.03 8.22
143→2 31.13 6.71
), ArticleFig(id=1168130322790101784, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, language=EN, label=Tab.3, caption=

Probability and time of failure sequence for each tank group

, figureFileSmall=null, figureFileBig=null, tableContent=
罐组失效顺序 出现次数 概率
1→234 1 180 0.118
1→243 2 132 0.213
1→324 896 0.089 6
1→342 1 340 0.134
1→423 2 658 0.265 8
1→432 1 794 0.179 4
), ArticleFig(id=1168130322886570777, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, language=CN, label=表3, caption=

各罐组失效顺序概率及时间

, figureFileSmall=null, figureFileBig=null, tableContent=
罐组失效顺序 出现次数 概率
1→234 1 180 0.118
1→243 2 132 0.213
1→324 896 0.089 6
1→342 1 340 0.134
1→423 2 658 0.265 8
1→432 1 794 0.179 4
), ArticleFig(id=1168130322991428378, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, language=EN, label=Tab.4, caption=

Summary of disaster chain triggering conditions

, figureFileSmall=null, figureFileBig=null, tableContent=
节点序号 触发条件 能否
触发
S2 S1+火源+立即点火 能
S3 S1+液池扩大+爆炸蒸气云形成+
火源+延迟点火		 能
S4 S1+液池扩大+蒸气挥发+无火源 能
S5 S1+液池扩大+蒸气挥发+无火源 能
S6 S1+S2+人员位于损伤范围内 能
S7 S1+S2 能
S8 S3+建筑物达到失效超压 能
S9 S3+人员位于损伤范围内 能
S10 S2或S12+S13 能
S11 S3+设备达到失效超压 能
S12 S2 能
S13 S12 能
S14 S13+部分未燃烧 能
S15 S3+碎片+泄漏+点燃 能
S16 S15 能
S17~S25 S3+超压+碎片+泄漏+点燃 否
S26 S3+超压+碎片+大量泄漏+高热 否
), ArticleFig(id=1168130323058537243, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149739130256540051, language=CN, label=表4, caption=

灾害链触发条件汇总

, figureFileSmall=null, figureFileBig=null, tableContent=
节点序号 触发条件 能否
触发
S2 S1+火源+立即点火 能
S3 S1+液池扩大+爆炸蒸气云形成+
火源+延迟点火		 能
S4 S1+液池扩大+蒸气挥发+无火源 能
S5 S1+液池扩大+蒸气挥发+无火源 能
S6 S1+S2+人员位于损伤范围内 能
S7 S1+S2 能
S8 S3+建筑物达到失效超压 能
S9 S3+人员位于损伤范围内 能
S10 S2或S12+S13 能
S11 S3+设备达到失效超压 能
S12 S2 能
S13 S12 能
S14 S13+部分未燃烧 能
S15 S3+碎片+泄漏+点燃 能
S16 S15 能
S17~S25 S3+超压+碎片+泄漏+点燃 否
S26 S3+超压+碎片+大量泄漏+高热 否
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危化品事故多层级链式推演模型及案例分析
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何青伦 1, 2 , 姜文宇 1, 2 , 王飞 1, 2, ** , 李鑫 3 , 王智 3
中国安全科学学报 | 安全工程技术 2024,34(3): 162-170
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中国安全科学学报 | 安全工程技术 2024, 34(3): 162-170
危化品事故多层级链式推演模型及案例分析
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何青伦1, 2 , 姜文宇1, 2, 王飞1, 2, ** , 李鑫3, 王智3
作者信息
  • 1 清华大学 工程物理系，北京 100084
  • 2 清华大学深圳国际研究生院 安全科学与技术研究所，广东 深圳 518000
  • 3 佛山市城市安全研究中心，广东 佛山 528000

    • 何青伦 (1999—)，男，云南保山人，硕士研究生，主要研究方向为地面塌陷、事故建模、灾害链等。E-mail：。

通讯作者:

** 王飞(1979—)，男，湖北武汉人，博士，副教授，主要从事安全工程、灾害建模和深度学习等方面的研究。E-mail：。
Multi-level disaster chain deduction analysis and case application of hazardous chemical accidents
Qinglun HE1, 2 , Wenyu JIANG1, 2, Fei WANG1, 2, ** , Xin LI3, Zhi WANG3
Affiliations
  • 1 Department of Engineering Physics，Tsinghua University，Beijing 100084，China
  • 2 Institute of Safety Science and Technology，Tsinghua Shenzhen International Graduate School，Shenzhen Guangdong 518000，China
  • 3 Foshan Urban Safety Research Center，Foshan Guangdong 528000，China
出版时间: 2024-03-28 doi: 10.16265/j.cnki.issn1003-3033.2024.03.1155
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摘要
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为提升危化品企业的风险防控能力，支撑事故发生后迅速地进行应急决策研判，基于灾害链理论，从热辐射、毒气、超压3类危化品事故关键致灾因子出发，提出灾害链多层级链式推演模型。采用流体扩散模型和Probit模型，分别构建危化品容器起火概率及顺序预测算法和危化品爆炸点火时刻估计算法，实现对危化品事故中燃烧和爆炸演化过程的定量计算分析。并以广东省某树脂生产厂为研究案例，建立1条由危化品泄漏事故引发的灾害链，推演分析灾害链各节点的演化时间和发生概率。分析结果表明：灾害链推演模型能有效定量化分析实际危化品厂区事故的演化过程、预测事故节点发生概率及时间，并在一定程度上帮助检验危化品厂区布局安全。

危化品事故  /  灾害链  /  链式推演  /  蒙特卡罗  /  Probit模型

To enhance the risk prevention and control capabilities of hazardous chemical factories and support emergency decision-making in case of accidents，a multi-level deduction model for the disaster chain was proposed. Furthermore，three categories of key factors (such as thermal radiation，toxic gases，and overpressure) affecting hazardous chemical accidents were considered in the model. Based on the fluid diffusion model and Probit model，the fire probability and sequence simulation algorithm of hazardous chemicals container and the ignition time estimation algorithm of hazardous chemicals explosion were proposed，respectively. Then，the quantitative analysis of the combustion and explosion evolution process in hazardous chemical accidents was performed. For the case of a resin chemical production company in Guangdong province，a disaster chain caused by hazardous chemical leakage accidents was developed to analyze the evolution time and probability of each node. The results indicated that the proposed deduction model can effectively analyze the evolution process of the actual hazardous chemical disaster chain，predict the probability and time of accident nodes，and provide fundamental knowledge for the safety layout of hazardous chemical plants.

hazardous chemical accidents  /  disaster chain  /  chain deduction  /  Monte Carlo  /  Probit model
何青伦, 姜文宇, 王飞, 李鑫, 王智. 危化品事故多层级链式推演模型及案例分析. 中国安全科学学报, 2024 , 34 (3) : 162 -170 . DOI: 10.16265/j.cnki.issn1003-3033.2024.03.1155
Qinglun HE, Wenyu JIANG, Fei WANG, Xin LI, Zhi WANG. Multi-level disaster chain deduction analysis and case application of hazardous chemical accidents[J]. China Safety Science Journal, 2024 , 34 (3) : 162 -170 . DOI: 10.16265/j.cnki.issn1003-3033.2024.03.1155
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0 引言
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化学工业是我国基础产业和支柱产业之一，产业规模的扩大化和集约化已成为化工工业发展的主要趋势。由于危化品厂区设施布置密集的特点，在发生事故时往往会产生后果严重的灾害链式效应。如2015年天津港危化品事故产生的灾害链效应[1]，接连发生2次爆炸，伴随着数10个火点的火灾蔓延，造成了极其惨重的人员伤亡和经济损失。研究危化品厂区灾害链演化过程，可由初始事件预测后续灾害的变化时间和概率情况，对辅助应急救援决策具有重要意义。同时，也能够识别厂区潜在风险、评估布局结构的合理性，从而预防灾害链发生。
灾害链演化分析落地应用的关键在于灾害链构建的准确性，以及定量分析灾害链各节点时间概率变化过程。明确灾害链成链机制，有利于快速、准确地完成灾害链构建。史培军[2]认为，灾害链是一种灾害引发一系列其他灾害的现象，提出孕灾环境—致灾因子—承灾体的灾害发生理论框架，为灾害成链提供理论基础。基于灾害链成链机制，ZHENG Liang等[3]提出一种利用学术数据提取灾害信息的方法，能够完成灾害链的快速构建；JIANG Wenyu等[4]基于灾害系统理论搭建了灾害模型服务链管理系统，根据需求自适应完成灾害网络创建。在灾害链概率推演方面，主要方法有贝叶斯模型、复杂网络、Probit模型等推演方法。孙逸林等[5] 基于贝叶斯网络分析燃气管道泄漏爆炸灾害链，得到事故中关键的灾害节点及致因链；陈国华等[6]采用复杂网络分析灾害链网络风险和其结构特征，提出灾害链的网络结构特征对风险影响程度的量化方法。此外，模糊数学、神经网络法也经常用于计算灾害链的条件概率。如WANG Junjie等[7]利用人工神经网络计算地震造成的建筑损害灾害链概率，并结合地理信息化技术可视化展示受损区域。在时间推演方面，常用方法有仿真建模以及蒙特卡罗模拟，如ZHENG Xinxin等[8]利用Fluent软件仿真模拟危化品厂由甲烷泄漏引发爆炸的灾害链演化情况；CHEN Chao等[9]基于蒙特卡罗的迭代算法绘制出了超压和热辐射作用下储罐区各时间节点的灾害演化图像。然而，当前研究主要针对特定灾种进行场景建模及数学分析，较少从灾害链全局演化过程进行完整的推演分析，缺乏危化品厂区的实证落地应用。
鉴于此，笔者拟提出一种用于危化品厂区灾害链演化过程分析的推演模型，并以广东省某危化品厂区为例，分析其可能发生的灾害链路径，并推演事故节点演化全过程，以期为危化品风险防控提供重要的辅助决策信息，提升厂区安全生产能力。
1 灾害链多层级链式推演模型
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以危化品厂区常见的致灾因子为分类依据，构建热辐射灾害传播、超压灾害传播、毒气扩散灾害传播3种链式推演模型，如图1所示。该模型主要考虑危化品事故常见的3种致灾因子灾害传播作用，以人员、建筑物、设备等作为承灾体分析事故影响，结合灾害链的成链机制和危化品厂区空间布局进行危化品事故推演。
1.1 热辐射灾害传播链式推演
1) 热辐射值模拟。为准确分析火灾发生后相邻区域、设备所受到的热辐射值，采用事故后果模拟软件ALOHA进行模拟[10]，通过设置模拟地的自然条件，如海拔、风速、温度、湿度、天气等参数，以及设置模拟对象属性参数，如储罐容积、管道半径、开放液池尺寸、储存的危化品属性等。当设置为池火热辐射时，模拟得到热辐射区域、燃烧速率、特定点位的热辐射值变化情况等结果，为后续容器失效时间计算提供数据支撑。
2) 失效概率计算。危化品厂区中设有储罐、储料桶、输送管道、反应槽等设备，设备受到热辐射影响而导致失效或事故升级存在特定的升级阈值(如9.5或15 kW/m2)[11]。在设备受到的热辐射值超过升级阈值的情况下，热辐射对这些设备的影响具有时间累积效应，即失效概率将随着时间延长而增大。失效概率计算如下式[12]：
p f = 1 - e x p - t 1 t f
式中： p f为目标单元起火概率； t 1为设备接收热辐射的累计总时间，min; t f为目标单元(容器)失效时间，min。通过COZZANI等[13]提出的热辐射作用下的容器 t f公式进行计算：
y = 9.25 - 1.85 l n ( t f )
l n ( t f ) = - 1.128 l n Q - 2.667 × 10 - 5 V + 9.877
式中：y为设备损伤概率对应的正态偏差；Q为热辐射强度，kW/m2;V为容器体积，m3。
3) 热辐射作用下失效时间模拟。危化品厂区中的罐区、仓库等区域存在大量储存危化品的设备、容器，若其中某一个设备发生泄漏并引起池火，其他设备将受到该设备所产生的热辐射。考虑联合热辐射效应，当有新的设备失效起火后，剩余设备受到的热辐射值为目前起火设备热辐射值的总和，所以剩余容器失效的过程涉及到起火顺序的问题。起火顺序不同，后续设备失效概率也不同，设备起火时间也不同。
采用蒙特卡罗方法分析热辐射传播过程。假设设备在失效后立即形成池火，且对周围的热辐射值达到稳定，在此基础上建立热辐射作用下设备失效模拟算法分析设备传热和失效过程，算法步骤如下：
步骤1:初始化迭代次数N=1，时间t=0。
步骤2:输入模拟值Q、V，动态计算目标单元(容器)失效时间 t f，得到式(1)， p f作为随机变量参与计算。
步骤3:生成0~1范围内均匀分布的随机数R与随机变量 p f，并比较判定，若R> p f，则认为设备正常，t=t+Δt，继续循环。反之则认为设备失效，记录下失效容器状态和序号，更新目前失效设备所产生的Q，重新计算 t f以及式(1)，t=t+Δt，继续循环，直到所有设备失效。
步骤4:迭代次数N=N+1，继续重复上述步骤直到迭代上限。
1.2 毒气灾害传播链式推演
1)扩散范围模拟。毒气扩散使用ALOHA软件进行模拟，其内置了重气扩散模型和高斯扩散模型，模拟挥发有害气体的扩散区域。
2)人员损伤范围确定。ALOHA软件内置应急响应计划指南(Emergency Response Planning Guidelines，ERPGs)指标分析有毒气体对人员的损伤范围[14]，根据人体暴露在毒气中的健康状态划分毒气浓度区域：ERPG-1表示在低于该浓度的毒物环境中暴露1h，不会对人产生不利影响，通常取毒气体积分数为5×10-5;ERPG-2表示位于该浓度及以上环境中1h，将对人产生不可恢复的严重健康危害或影响人员逃生行动的症状，通常取毒气体积分数为3×10-4;ERPG-3表示位于该浓度及以上环境中1h，将威胁到人的生命安全，通常取毒气体积分数为1×10-3。依据该标准得出毒气扩散后人员的受伤区域。
1.3 超压灾害传播链式推演
1) 爆炸超压计算。危险化学品泄漏时间、泄漏速度、泄漏量都对事故发生和后果有着重要的影响。容器、管道中液体泄漏速率 W 0通过伯努利方程进行计算[15]，挥发的可燃蒸气质量根据泄漏总质量守恒进行计算。依据伯努利方程计算出泄漏速率经过t时间后，漏出液体总质量W为：
W = W 0 × t 2 = W f + W l
式中： t 2为泄漏时间，s; W f为蒸气云中燃烧物的总质量，kg; W l为泄漏的液池里燃料质量，kg。危险化学品泄漏后，其液体挥发与空气形成爆炸性混合物，其闪蒸的部分可用下式进行计算[15]：
F = 1 - e x p [ - C Δ T L ]
W f = 2 × F × W l
式中：F为蒸发系数；C为燃烧物的平均比热，kJ/(kg·K); ΔT为当前环境压力下容器内温度与燃烧物沸点的温差，K;L为燃烧物的汽化潜热，kJ/kg。爆炸产生的超压，可用TNT当量法进行计算：
W T N T = α × W f × H f H T N T
式中： W T N T为蒸气云的TNT当量，kg;α为蒸气云爆炸的效率因子； H f为可燃蒸气的燃烧热，kJ/kg; H T N T为TNT的爆炸热，一般取4.52×103 kJ/kg。将 W T N T的值代入下式的回归方程中，得到冲击波超压和距离的函数关系[16]：
P s = 7.1 × 10 5 × D W T N T 1 / 3 - 2.09
式中： P s为目标容器的静态超压，Pa;D为据爆炸点的距离，m。
2) 爆炸时刻估计。为使用TNT当量法计算爆炸超压，需确定空气中可燃蒸气的质量，而可燃蒸气的质量与扩散时间(即点火时间)密切相关。为解决上述问题，提出一种基于蒙特卡罗模拟点火效应的方法估计点火时刻，算法步骤如下：
步骤1:初始化迭代次数N=1，时间t=0。
步骤2:输入控制方程。爆炸蒸气云随时间变化由式(4)—式(6)联立得到：
W 0 × t = W l + W f = ( 1 + 2 F ) ρ l π R 0 2 δ
式中： ρ l为泄漏液体密度，kg/m3; R 0为泄漏液池半径，m;δ为可燃液体在水泥地扩散的厚度，取0.01m。蒸气云燃爆概率随时间变化关系为[17]：
p e = 1 - e x p ( - ω t )
式中： p e为蒸气云燃爆概率； ω为点火源有效因子，其值与点火源的状态、数量等有关，通常静止状态下的设备、车辆充当点火源时，取0.001 8。
步骤3:生成0~1范围内均匀分布的3个随机数X、Z、R，用以随机生成点火源空间坐标(X、X、Z)以及燃爆概率。
步骤4:控制方程中 R 0和 p e作为随机变量，当满足点火源坐标位于 R 0的半球区域内且R < p e时，认为燃爆发生，循环结束；反之则认为不满足燃爆条件，t=t+Δt，继续循环，直至发生燃爆。
步骤5:迭代次数N=N+1，继续重复上述步骤直到迭代上限。
3) 失效概率计算。危化品厂区中设有储罐、储料桶、输送管道、反应槽等设备，设备受到超压影响而导致失效或事故升级存在特定的升级阈值[18]。当超压大于该升级阈值时，根据Probit模型求解其失效概率[13]：
y = - 18.96 + 2.44 l n ( P s )
p d = 1 σ 2 π ∫ - ∞ y - 5 e x p - u - μ σ 2 2 d u
式中： p d为超压作用下目标单元失效概率；μ和σ分别为正态分布的均值和方差，由于是标准正态分布，所以μ = 0，σ = 1。
2 危化品厂区灾害链实证案例分析
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2.1 危化品厂区概况
选择广东省某树脂生产公司作为研究对象，该公司是一家危险化学品生产企业，其产品主要有聚氨酯粘合剂、氯丁酚醛胶粘剂、橡胶水、酸酯胶粘剂。生产过程涉及到各个区域的协同工作以及较多化学品的运输和使用，重点危险场所主要包括甲类卧式罐区、C栋甲类生产车间、E栋仓库等，如图 2所示。这些区域储存有大量的甲类火灾危险性化学品，存在因误操作或设备失效等原因导致的泄漏风险。泄漏挥发物与空气易形成爆炸性混合物，遇火将发生爆炸燃烧，危及周边区域。厂区如图2所示。
2.2 厂区灾害链构建
依据灾害链成链机制，当致灾因子与承灾体作用后会产生3种结果[19]:①致灾因子发生变化，如作用时间、空间、强度等；②承灾体发生变化，如承灾体破坏、变异等情况；③产生新的致灾因子，作用于下一个承灾体，从而产生灾害链。根据厂区布置和危化品储存情况，当C栋生产车间发生初始泄漏事故时，灾害链影响范围最广，具体灾害链构建如图 3所示。
2.3 灾害链节点演化推演
当C栋生产车间甲苯输送管道发生泄漏后，以点火时间为分类依据，可分为2条主链路：①主链1表达式为{S1→S4、S5、(S3→S6、S7、S8、S9、(S11→S13→S14、S6、S7、S10))}。该链路在甲苯泄漏后延迟点火，此时形成爆炸性蒸气云，从而引发蒸气云爆炸(Vapor Cloud Explosion，VCE)。超压将导致周边管道、建筑受损、人员伤亡，相邻反应槽失效并引发池火及后续污染。②主链路2简化表达式为{S1→S2→S6、S7、S10、(S12→S13→S14、S6、S7、S10)}。该条链路为泄漏后立即点燃(在形成爆炸混合物之前为立即点燃)，泄漏液池周围产生池火并以热辐射的形式引起周围罐体起火失效，造成排水道污染。2条主链路可进一步细分为多条分支链路，链路中各节点包含热辐射、超压、毒气扩散3类致灾因子所引发的灾害。
1) 超压作用相关节点推演。超压作用场景主要体现在主链路1中。设置情景C栋车间甲苯输送管道泄漏，泄漏孔为圆形，直径5 mm，设在R08反应槽上方，如图 4所示。泄漏口上方液面高度h取管径0.45 m，容器内介质压力取管道工作压力0.6 MPa 。依据推演模型，先进行点火时刻模拟。据式(9)、 式(10)计算得到控制方程：
t = 47.518 × R 0 2
p e = 1 - e x p ( - 0.003 t )
将式(14)和式(15)代入蒙特卡罗爆炸时刻估计算法，设时间步为1s，迭代次数N=105，计算出平均、最大与最小的点火时刻分别为498、1 396和32 s。由平均点火时刻计算得到泄漏量 w l=188.24 kg，爆炸性混合物中甲苯质量 w f =120.48 kg。代入式(7)、式(8) 可推导不同槽罐受到的爆炸超压，再代入Probit模型计算得到各槽罐失效概率，见表 1。
由表1可知：除R04B槽罐失效概率为0.866，以及R05、R06未达到失效阈值外，其余槽罐均可认为在点火瞬间失效，形成区域池火，之后致灾因子转换为热辐射作用。此时C栋仓库里，除失火的槽罐还有一个半径为4.13 m的池火区域(图 4)。若R04B罐失效，利用ALOHA软件模拟此时剩余槽罐受到的热辐射值，R05处在池火区域内，R06为141.4 kW/m2，代入式(3)计算得R06罐tf=1.2 min，失效概率为1。认为在1.2 min后R06、R05一定失效，实际失效时间可能远小于1.2 min，在几秒内便可能失效。若R04B罐没有失效，则R06为54.95 kW/m2，R05为84.4 kW/m2，R04B在池火区域内。R04B燃烧充分之后，则类似上述情况，约2.2 min后R06、R05一定失效。由式(7)、式(8)计算得到爆炸对B栋仓库产生36.3 kPa超压，参照超压损害对照表[20]，该压力会对B栋仓库造成墙体破坏，而其储存有乙类火灾危险化学品，可能会引发新的灾害链反应。而计算得到的爆炸对A栋仓库和D栋仓库产生超压分别为5.06和16.6 kPa，对 2仓库基本无影响，所以S17、S25及后续灾害均不会发生。
2) 毒气作用相关节点推演。毒气作用场景主要仅体现在主链路1。由于C栋生产车间为密闭车间，空气流通较少，故甲苯泄漏后会在泄漏点呈半球形气团向外扩散形成蒸气云[21]。输入泄漏口直径5 mm，近似风速0.5m/s，泄漏量300 kg，ALOHA软件提示模拟浓度较小无法显示，更改泄漏口径为500 mm，模拟结果如图 5所示。从图5可以看出，泄漏处的毒物浓度远远低于ERPG-1所规定的值，因此，该链条中毒气扩散对人的影响极小，一定时间后与空气混合形成爆炸混合物，遇点火源即发生爆炸，使致灾因子转换成超压作用，引发后续灾害。
3) 热辐射作用相关节点推演。热辐射作用场景主要体现在主链路2和主链路1后续B栋仓库燃烧热辐射传播过程，此处仅分析说明主链路2。初始泄漏情况同上，假定在爆炸时刻前1s发生点火，形成池火燃烧。为方便计算，将10个槽罐组合分为5组(图 4)。利用ALOHA软件模拟罐组燃烧时互相之间的热辐射作用值，代入式(3)可得罐组失效时间tf，见表2;传播路径“ABC→D”表示罐组ABC池火热辐射引发罐组D失效，从而代入式(1)成为概率控制方程，再利用蒙特卡罗算法模拟罐组失效顺序和失效概率，设置时间步为1s，迭代次数N=105，计算结果见表3。
从表 2可以看出，罐组5和6在任何情况下都无法超过失效阈值15 kW/m2，因此，认为这2个区域不会因热辐射而失效。ALOHA软件模拟得到的最大燃烧速率为23.7 kg/min，而单个槽罐至少有1.83 t可燃烧的危化品，计算得到时间远远大于表2中 t f时间，所以不考虑燃尽的情况。
当池火形成后，失火顺序为罐组4→罐组2→罐组3概率最大，罐组2→罐组4→罐组3失火顺序概率略低一些。由于和池火中心距离的原因，罐组4第2个起火的概率最大。起火顺序为1、4、2、3时，各罐组的起火时间分别0，29.3，50.1，74.5s。
4) 全过程灾害链推演。依据上述推演结果，总结灾害链各节点的触发条件以及实际推演过程中的节点触发情况，见表4。本条灾害链由C栋车间甲苯泄漏为初始事件，后续发生爆炸、燃烧、扩散等衍生灾害，影响涉及到周边区域，北侧B栋仓库由此产生衍生燃烧事故。由事故统计概率，取5 mm孔径泄漏概率为4×10-5[22]，点火源在任意时刻、位置点火的概率取1×10-5[23]，得到概率时间演化，如图 6所示。
2.4 对策及建议
通过灾害链推演可知：该厂灾害链某一链路完整发生的概率较低，主要原因在于泄漏和点火的发生可能性较小，但由于后续事故节点发生的条件概率高、事故后果严重，因此，该厂区整体风险较高。
该厂区灾害链发生的主要原因为C栋生产车间本身危险性较大，且处于厂区中心位置，发生事故容易危及周边建筑，尤其是B栋仓库，2个厂房间距偏短。车间内部可能的点火源较多，一旦发生泄漏，很有可能引发严重的灾害链事故。B栋仓库单个存储桶容量过大，物料桶在受到爆炸冲击起火后，对周围产生的热辐射已经超过容器失效阈值，容易产生新的衍生灾害(图 6中链7、链8)。
针对这些问题，厂区应重新规划C栋生产车间的位置，加大其与周围建筑的防火间距，如条件允许，尽量设置在厂区边缘位置或全年最小频率风上风向。B栋仓库应选用单个容量较小的容器储存危化品，能有效避免偶发的泄漏、破损事件导致VCE发生的概率，较少的泄漏量也有利于降低闪燃概率，从而避免热辐射作用下的衍生灾害产生。
3 结论
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1) 构建灾害链多层级链式推演模型，基于蒙特卡罗方法提出热辐射作用下失效时间模拟、点火时刻估计算法，该方法可以较好地解决灾害推演中危化品容器受热失效顺序时间以及爆炸事件发生时刻估算问题。
2) 以实际危化品厂区为研究对象完整的推演分析了厂区灾害链全局演化过程，得到节点触发的条件、概率、时间，结果表明：推演模型可实现实际危化品厂区灾害链推演落地应用，为应急决策提供支撑。
3) 未来研究还需要在灾害链建模方面考虑更为全面的影响因素，如进一步考虑灾害发生后消防措施、应急措施、安装装置作用情况下的事故演化进程，做到更为精确化的灾害链建模推演。
基金
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  • 广东省重点研发项目(2019B111104001)
  • 深圳市学科布局项目(JCYJ20180508152055235)
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2024年第34卷第3期
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doi: 10.16265/j.cnki.issn1003-3033.2024.03.1155
  • 接收时间：2023-09-14
  • 首发时间：2025-07-09
  • 出版时间：2024-03-28
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  • 收稿日期：2023-09-14
  • 修回日期：2023-12-18
基金
广东省重点研发项目(2019B111104001)
深圳市学科布局项目(JCYJ20180508152055235)
作者信息
    1 清华大学 工程物理系，北京 100084
    2 清华大学深圳国际研究生院 安全科学与技术研究所，广东 深圳 518000
    3 佛山市城市安全研究中心，广东 佛山 528000

通讯作者:

** 王飞(1979—)，男，湖北武汉人，博士，副教授，主要从事安全工程、灾害建模和深度学习等方面的研究。E-mail：。
参考文献
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/CN/10.16265/j.cnki.issn1003-3033.2024.03.1155
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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