Evaluation on consequences prediction of fire accident in emergency processes for oil-gas storage and transportation by scenario deduction

This paper takes the safety in emergency processes as the starting point, from the perspective of scenario deduction, to study the consequences of fire accidents for oil-gas storage and transportation. Through the statistical analysis of actual accident cases, 19 frequently occurring basic scenarios in emergency processes are summarized. The scenario evolution paths of fire accidents for oil-gas storage and transportation are given by analyzing the evolution law of the accident development. Fuzzy numbers are introduced to express experts' qualitative judgment on accident scenarios. The empirical probabilities of scenario nodes are obtained by defuzzification calculation, and the state probability of each scenario node is calculated by using the dynamic Bayesian network joint probability formula. Under the comprehensive consideration about the probability statistics of actual accident cases, the critical scenario nodes on the evolution path and their final scenario probabilities are jointly determined to realize the optimization of the scenario evolution path. By constructing the correlation between the optimized scenario evolution path and the accident consequences, an accident consequence prediction model is established. The occurrence probability of accident consequences is calculated by the defuzzification method and dynamic Bayesian network. The accuracy of the consequence prediction model is verified by the July 16 Dalian's Xingang Harbor oil pipeline explosion accident. The research results provide scientific basis for helping decision makers to make the effective emergency measures that are most conducive to the rapid elimination of accidents and reducing the severity of accident consequences.     

基于动态贝叶斯网络的石化装置火灾事故动态风险分析

为揭示石油炼化装置事故风险动态特性和事故情景演变路径,在对石化装置进行风险因素分析的基础上构建石化装置火灾事故故障树,基于贝叶斯网络非常规突发事故的演变过程,构建情景演变下的动态贝叶斯网络模型,在综合考虑应急措施的基础上,利用MATLAB软件和联合概率公式计算出各种事故场景的状态概率.以丙烯精馏装置火灾事故为例,结果表...     

隧道突泥涌水情景构建及演化状态预测模型

为实现对隧道突泥涌水事故的有效管控,基于情景分析理论,采用演化路径网络和演化矩阵模型,针对隧道突泥涌水灾害进行情景推演预测。将隧道突泥涌水情景要素归类为孕灾环境、致灾因子、驱动要素和承灾体,研究各要素的属性参数;基于承灾体损失将隧道突泥涌水情景分为6级,分析灾害情景的演化规律;建立灾害全过程的演化路径网络,构建预测灾害情景状态的演化矩阵模型,提出情景状态等级的概率分布算法。将该模型应用于雅叶高速公路折多山隧道,结果表明：该方法可准确地模拟隧道突泥涌水全过程的演化路径及情景状态,预测结果有助于隧道建设单位摸清既有能力和明确防灾任务,为其应急能力建设提供重要依据。     

Scenario deduction of Natech accident based on dynamic Bayesian network: A case study of landslide accident in a liquor storage tank area in Guizhou Province,China

Industrial technical accidents caused by natural disasters are defined as Natech accidents, such as earthquakes and landslides, which can cause tremendous damage to industrial storage tanks, and lead to accidental leakage and even serious fire and explosion accidents. In this study, a landslide-induced storage tank accident model under earthquake disasters was proposed, and the relationship between landslide mass impact and target impact resistance was taken into account. Also, tank failure and the formation of the pool fire were considered to be the consequences of the Natech accident. Through scenario deduction, the dynamic process of landslide Natech was transformed qualitatively into a disaster chain network diagram composed of a scenario state, a disaster-causing factor and emergency management. The Bayesian network was used to learn and deduce the parameters of the network diagram, and in this process, the prior probability and conditional probability of nodes were obtained primarily by Monte Carlo simulation, and by an improved expert scoring method based on the fuzzy set theory. Through visualization software, the sensitivity analysis of landslide Natech was achieved. Finally, a case study of a liquor storage tank area in Guizhou Province, China was carried out, and the results show that a large amount of hazardous material leakage caused by buckling is key to the formation of pool fire accidents, and several prevention measures for earthquake-induced landslide Natech was proposed according to the sensitivity analysis.     

基于电力历史应急大数据的应急情景规则分析与发现方法研究

为了解决电力突发事件演化过程难于预测,进而导致事件应急缺少针对性,较为被动的问题,提出在现有应急预案指导、事故模拟仿真、事故案例经验总结等方法基础上,引入基于电力历史大数据分析与预测的应急情景规则分析与发现方法,将电力应急突发事件情景构建的结果与相关大数据预测模型（汇总统计、分类与预测）相结合,提出科学、合理且具有良好操作性的情景演化规则的获取方法与技术路线,为进一步提高复杂电力突发事件的应急处置与指挥能力提供技术支撑。     

城域突发事故灾害发生机理探索

城域突发事故灾害发生机理涉及事故灾害致因、演化过程、发展规律3个主要方面;从典型事故致因理论入手,探索城域突发事故灾害致因有本质原因、基础原因和直接原因;事故致因要素之间的综合作用是城域突发事故灾害发生机理研究的关键,可考虑建立与之对应的数学模型,运用突变理论,对演化过程进行分析,指出城域突发事故灾害演化是一个流变-突变过程,并满足尖点突变模型,引发因素和控制因素两个参数决定事故状态的变化;总结城域突发事故灾害的发展规律,指出城域突发事故灾害沿链条传递,并呈现一定周期性,研究成果可为事故灾害的预防与控制提供借鉴。     

危化品事故情景推演网络构建及关键节点分析*

为有效应对危化品事故发生与演变时存在的动态性及不确定性问题,基于事故演变过程中的关键情景状态及对应孕灾环境、应急活动和处置目标等要素,引入动态贝叶斯网络方法,构建危化品事故情景推演网络,并结合复杂网络知识,对孕灾环境和应急活动2类事故影响因素节点进行敏感性和重要度分析。结果表明:情景推演网络计算的情景节点概率符合事故发生的实际情况,能够对危化品事故在不同孕灾环境和应急救援下的演变路径进行推演,并且分析得到燃烧物、消防力量、火场环境和建筑密度等关键影响因素节点,研究结果可为事故处理提供应急辅助决策支持。     

城市燃气管网泄漏事故分析知识图谱构建及应用研究*

为深入认识燃气管网泄漏事故的发生发展机理,提高事故分析预测的自动化、智能化、数字化水平,利用知识图谱对燃气管网泄漏事故进行研究。在事故案例分析的基础上,从人-物-环-管的角度对燃气泄漏过程以及火灾爆炸次生事故的相关实体进行归纳梳理,对实体间的逻辑关系和非逻辑关系进行辨识,并对实体的属性进行分类,进而构建出较为全面的燃气管网泄漏事故知识图谱。在此基础上,搭建BP神经网络模型,基于已知实体或属性状态,预测相关联其他实体或属性的状态。研究结果表明:燃气管网知识图谱能够有效展示燃气管网泄漏事故发展的动态过程及相关要素,结合BP神经网络能够有效预测事故的发展路径及相关状态,从而提高燃气管网泄漏事故的分析预测水平与效率。     

基于动态贝叶斯网络的民航突发事件情景分析研究

航空运输重大突发事件的应急准备与处置存在问题,应急救援效果不甚理想。建立了基于灾害体、受灾体、孕灾环境以及抗灾体四要素的突发事件情景分析模型,并运用动态贝叶斯网络模型,构建了基于关键时间节点的突发事件情景演化模型。基于上述模型,以某航班降落时起落架故障的典型事件为例进行模型构建与分析,分析结果验证该模型运用于民航突发事件情景分析与评估的合理性和可行性。该模型为提高民航突发事件应急准备与处置能力提供了新的思路与方法。     

突发事件下安全舆情的传播与演变——以江苏响水天嘉宜化工有限公司“3.21”特别重大爆炸事故为例

为明确突发事件安全舆情传播与演变路径,提高企业防控突发事件安全舆情传播风险能力,降低衍生灾害发生概率,在文献分析及事故致因理论基础上,构建突发事件安全舆情传播与演变过程的系统动力学（SD）模型,并运用Vensim软件对以江苏响水天嘉宜化工有限公司“3.21”特别重大爆炸事故为例进行仿真实验,模拟安全舆情传播与演变动态过程。结果表明:突发事件安全舆情传播与演变主要受事件自身、媒体、相关企业监管层、相关企业执行层4个主体的共同影响,其中事件自身因素起效时间最早,相关企业监管层影响作用最大、维持时间最持久,相关企业执行层影响最直接。     

商业综合体灾害链风险评估研究

为了对商业综合体灾害链进行风险研究,基于链式风险评估模型的建模方法,构建商业综合体灾害链风险评估模型,并应用复杂网络结构对商业综合体灾害链的演化过程进行表征,综合灾害事件的致灾率、灾害损失程度、灾害链的脆弱度3个指标得到每条灾害链的风险度量值,最后以泸州摩尔玛商场爆炸事件为实例进行评估,辨识出每条灾害链的风险值。研究结果表明:该模型与实际情况具有较高的吻合性,为商业综合体次生或衍生灾害的防控提供了可靠的理论依据。     

基于灰色-物元模型的煤矿瓦斯爆炸风险评估

为了明确风险因素间的作用路径并提出科学可行的瓦斯爆炸灾害风险评估方法,首先基于3类危险源理论梳理分析以往典型瓦斯爆炸案例,识别并提取14个影响因素;然后通过集成灰色系统理论和物元可拓模型,构建灰色-物元评估模型;最后以山西省某煤矿的实际调研数据为例,计算各影响因素的权重和关联函数,梳理风险因素致灾的逻辑关系,得出该矿井2个具体工作面的瓦斯爆炸风险等级。研究表明:1号工作面中的平均瓦斯涌出量、煤尘爆炸指数、采掘机械化水平与2号工作面中的瓦斯体积分数均处于较不安全级别,2个工作面的综合风险等级分别为一般安全与较安全级别。     

城镇-森林交界域火灾灾害链网络模型构建及风险分析

为确定城镇—森林交界域火灾灾害演化关系及风险特征,预防灾害事件发生,从157个火灾案例中提取43个典型灾害事件,运用灾害链和复杂网络理论,构建城镇-森林交界域火灾灾害链演化模型。使用Python工具对灾害链网络中关键节点、关键边和最短路径分析。结果表明:化工厂爆炸、加油加气站爆炸、城镇火灾、污染水质、污染空气及城市生命线损毁是关键节点,山洪—道路损毁、河流泥沙淤积—山洪、堤坝决堤—山洪、堰塞湖—山洪、森林火灾—城镇火灾是关键边。研究结果可为城镇-森林交界域火灾的防控、断链减灾提供科学依据,具有较高的现实意义。     

基于HFACS的化工企业火灾爆炸事故人因分析*

为研究化工企业火灾爆炸事故的主要人为因素,以63起火灾爆炸事故案例为样本,构建人为因素分析与分类系统（HFACS）模型,进行火灾爆炸事故人为因素分类统计与分析,并利用卡方检验和比值比分析HFACS模型上下层级间的因果关系。结果表明:HFACS模型中上下层级人为因素间存在显著的因果关系,层级1中的“不良的组织氛围”及“组织过程漏洞”和层级2中“监督不充分”在HFACS模型中可以显著增大事故发生的可能性,且“资源管理漏洞”、“不良的组织氛围”、“组织过程漏洞”→“监督不充分”→“人员因素”→“违规”是引发事故的关键路径,并根据HFACS模型中引发事故的关键路径及其人为因素的主要表现形式,提出针对性的化工企业火灾爆炸事故预防措施。     

灾害演化网络时序性风险评估模型

为探究灾害演化过程中风险的时变特征和时序性风险评估的可行性,采用灾害演化理论和方法,综合考虑灾害链式效应、灾害后果时变性和政府干预等因素,首先,建立由灾害损失度、灾害发生概率、灾害直接风险和灾害综合风险共4个子模型构成的灾害演化网络时序性风险评估模型;然后,通过数值算例验证该模型的可行性;最后,分析所提模型的优越性.研...     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

基于改进飞蛾扑火算法的应急资源调度研究*

为解决多灾害点应急资源调度问题,构建不同路况下运输消耗成本最低、装车时间最少的多目标应急资源调度模型,基于双自适应因子改进飞蛾扑火算法,克服传统飞蛾扑火算法存在的局部最优而早熟收敛、种群多样性低而全局寻优性能差的问题,提高算法的寻优性能和求解精度,并求解应急资源调度问题,获得各灾害点高效应急资源配置方案。研究结果表明:与经典飞蛾扑火优化（MFO）算法以及基于Levy飞行的飞蛾扑火优化（LMFO）算法相比,基于双自适应因子的改进MFO（DAMFO）算法模型求解精度更高,研究结果可为制定合理高效的应急资源调度方案提供依据。     

基于贝叶斯网络的化工园区地震次生灾害情景分析

为了研究化工园区内发生地震灾害后的事故演化过程,利用事故链模型对地震次生灾害演化过程进行分析,并将其转化为贝叶斯网络,确定各节点的变量与状态取值范围;通过相关文献及专家经验判断分析,获得贝叶斯网络中各节点的条件概率;依据贝叶斯网络的推理策略,比较不同地震烈度下,各化工事故发生的后验概率值,并探讨应急救援的及时性对地震次生灾害发生概率的影响,从关键要素遏制化工事故的发生,采取针对性的应对措施,尽可能的降低化工事故,有效的降低人员伤亡及财产损失。     

Integrated dynamic risk assessment of buried gas pipeline leakages in urban areas

In urban areas, buried gas pipeline leakages could potentially cause numerous casualties and massive damage. Traditional static analysis and dynamic probability-based quantitative risk assessment (QRA) methods have been widely used in various industries. However, dynamic QRA methods combined with probability and consequence are rarely used to evaluate gas pipelines buried in urban areas. Therefore, an integrated dynamic risk assessment approach was proposed. First, a failure rate calculation of buried gas pipelines was performed, where the corrosion failure rate dependent on time was calculated by integrating the subset simulation method. The relationship between failure probability and failure rate was considered, and a mechanical analysis model considering the corrosion growth model and multiple loads was used. The time-independent failure rates were calculated by the modification factor methods. Next, the overall evolution process from pipeline failures to accidents was proposed, with the accident rates subsequently updated. Then, the consequences of buried gas pipeline accidents corresponding to the accident types in the evolution process were modeled and analyzed. Finally, based on the above research, dynamic calculation and assessment methods for evaluating individual and social risks were established, and an overall application example was provided to demonstrate the capacity of the proposed approach. A reliable and practical theoretical basis and supporting information are provided for the integrity and emergency management of buried gas pipelines in urban areas, considering actual operational conditions.     

基于事故案例的Natech事件演化模式及风险分析*

为探究非巨灾型Natech事件演化模式并降低发生风险,基于164个事故案例,归纳出6种典型演化路径,构建非巨灾型Natech事件演化模式分析框架,并基于突发事件关联网络,分析影响Natech事件风险的水平关键节点及潜在发生路径。结果表明:暴雨灾害是对全局影响力最强的节点,与人为异动的协同作用最显著;电力事故Natech链路径较长;水污染事故是较容易被自然灾害或首发事故触发的事件。