基于UHBs的输气管道高后果区泄漏燃爆事故分析

为探究输气管道高后果区中人的不安全行为（Unsafe Human Behaviors,UHBs）对输气管道泄漏燃爆事故发生的影响,结合模糊Bow-tie模型和贝叶斯网络对输气管道泄漏燃爆事故进行分析。构建基于T-S模糊故障树的输气管道泄漏燃爆模糊Bow-tie模型,并转化为贝叶斯网络;从人的不安全行为发生的可能性出发,将不同等级高后果区划分为不同等级人口敏感区;利用专家经验评判法得到不同等级人口敏感区基本事件的先验概率和中间事件的条件概率表;运用贝叶斯网络双向推理算法求解模糊Bow-tie模型。结果表明:随着地区人口敏感等级的提高,输气管道泄漏燃爆事故发生的概率随之增大,发现导致输气管道失效泄漏事故发生的最主要原因为施工破坏,失效原因与EGIG分析的结果基本相符,验证该方法在高后果区输气管道泄漏燃爆事故分析上的可行性,可为输气管道高后果区的安全管理提供决策依据。     

扰动法在风网解算中的应用研究*

为简化矿井通风复杂风网的解算,提出将扰动法引入到风网解算中,并介绍扰动法的原理、风网解算的基本定律;给出常见的固定风压、固定风量和给定风机性能函数3种风机工况下的扰动方程矩阵表达式及详细的计算过程;对所提风网解算方法进行实例验证。结果表明:扰动法作为固定运算次数的近似方法,在工程实际允许的误差范围内,相比于以牛顿法为代表的迭代法耗时少、不依赖初值,是1种简单实用的风网解算方法。     

智能物联网在环境在线监控中的应用研究

目前的环境在线监控网络,虽然已经具备了物联网的一些基本特征,并且具备一定环境反应、故障诊断以及应急处理能力,但智能化程度普遍不高,仅停留在了简单的被动式反应,没有实现真正意义上与多智能体技术的结合,所以对于环境监测数据利用还不够充分,深度和广度还有待扩展。本文提出了一种多智能技术与环境在线监控物联网结合的设计方法,以及由此产生的对环境保护工作的提升作用。     

广州城市电网工频电磁环境影响分析及建议

高压输变电工程进入城市中心地带是发展的趋势,而工频电磁环境是居民关注的问题,根据目前广州城市电网建设的现状,对广州城区110 kV、220 kV各类型变电站、多种形式的输电线路产生的工频电磁环境影响进行分析,对比国家现行标准限值,评价广州城市电网工频电磁环境影响的总体情况,认识电网的未来发展趋势,并结合输变电电磁环境影响特点提出相应广州电网建设的意见与建议。     

利用深层地下水防治大骨节病分析

大骨节病是一种骨关节变形地方性慢性病。该病病因复杂,以生物地球化学和地球化学生态学理论为指导,在研究区进行水文地质环境调查,并对饮水水样分析对比,发现病区饮水水源低矿化度、高腐殖酸是大骨节病患病的重要因素,据此在病区开展改水工程,探讨利用深层地下水改善病区饮水水质和提高居民生活水平的有效性,为大骨节病致病水源假说提供相关的理论依据,同时为病区综合防治大骨节病提供相关经验。     

Method to map human and infrastructure vulnerability using CNN land cover: Case study of floating tank explosion at petrochemical plants of LaemChabang,Thailand

Industrial storage tanks, used to store flammable materials in the petrochemical industry, can induce potential fire and explosion under specific conditions. Therefore, it is necessary to map the population and environment vulnerability, and, to develop procedures for emergency responses in order to reduce potential casualties. In order to achieve this, Convolutional Neural Networks (CNN) are used in this study using 6 classes: floating tank, forest, house, road, wasteland and water. Datasets are built for a total of approximately 1.4 million tiles with a resolution of 0.33m/pixel and their size are optimized in function of the class. The 6 associated CNN models are built and optimized to classify each class. The validation of the models shows that, with the exception of road and wasteland where the precision is only 73% and 89% respectively, the other 4 classes have a value higher than 95%. Post-processing is performed on each prediction before aggregating these results to obtain the land cover. For the floating tank class, a 5 step post-processing is used based on a Density-Based Spatial Clustering of Applications with Noise algorithm (DBCAN) after which blast simulation is applied and effects on people, buildings and trees are obtained through 4 steps. Finally, the petrochemical site of LaemChabang in Thailand is used as study case. Except for the road class that is difficult to detect, land cover is well performed. Human casualties and surface of damaged buildings are finally estimated demonstrating the usefulness of the tool to be used for the emergency planning of industrial disasters.     

Research on the prediction model of hazardous chemical road transportation accidents

In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions.     

基于置信规则的村镇应急避难场所暴雨灾害链风险诊断*

为解决村镇应急避难场所面临暴雨灾害时的风险问题,基于故障树和置信规则库推理方法,提出暴雨灾害链和村镇应急避难场所功能破坏链相结合的场所避难功能失效风险诊断模型。根据事故致因理论推理灾害节点变量,通过故障树描述灾害链,运用关联规则从历史灾害数据中挖掘规则,建立置信规则库系统,构建村镇应急避难场所功能失效风险诊断模型,并以四川省某寄宿制学校为例进行模型验证。研究结果表明:该模型可实现不同证据组合下村镇应急避难场所功能失效风险的诊断推理;实例的模型诊断结果与实际情况吻合,证实该模型能够科学地诊断村镇应急避难场所面临暴雨等恶劣自然条件时存在的风险,可为村镇应急避难场所规划设计和应急管理提供理论支撑。     

基于BP神经网络的义水河水环境质量评价研究

水质质量涉及多个影响因子,因子间大多具有非线性相关性,因此,水质评价是一个具有多因子的非线性系统。人工神经网络具有非线性映射的特点,能够有效地处理非线性相关问题,将其应用于水质评价中能够获得更为准确的信息。文章以溶解氧、高锰酸盐指数、化学需氧量、氨氮、总磷、石油类等6个指标作为评价因子,通过构造BP网络模型对义水河水质进行评价,分析了义水河4个断面的水质状况,并与单因子评价法结果进行了比较分析。