基于模糊聚类关联分析法的煤与瓦斯突出程度分析

运用模糊聚类分析方法对煤与瓦斯突出的样本集合进行分类,建立了不同突出程度的模糊模式.用关联分析确定待分析样本与模式的关联程度,以此预测预报样本的煤与瓦斯突出危险程度.实例分析表明,与模糊聚类分类后、将模式与待预报样本组成新样本集合进行聚类分析并以此分类结果进行预报的方法相比,这种预报方法不仅可靠程度高,而且能定量描述待报样本与模式的亲和程度.     

1985-2005年中国城市水源地突发污染事件不完全统计分析

对1985-2005年中国城市水源地突发污染事件进行了统计分析,以从总体上揭示这些事件的发生规律及其对城市水源地和供水安全的危害.采用文献检索方法,通过对1985-2005年<中国环境报>、<人民日报>、新华网等报刊和网络的检索,从发生日期、地点、污染物种类、事件简况4个方面统计中国城市水源地突发污染事件,经整理和筛选后共列出102起.这些突发污染事件的分析结果表明:1)中国城市水源地突发污染事件总体上呈数量逐年增多,危害增大的趋势;2)化学品和污水是主要污染物;3)河道交通事故和工厂泄漏事故是主要风险源;4)突发污染事件一般都造成了比较严重的经济社会影响,但缺乏相应的应急管理机制和应急部门;5)3个典型突发污染事件造成重大经济社会损失并引发社会高度关注,凸现了构建中国城市水源地突发污染事件应急机制的重要性和迫切性.     

1993-2003年特大火灾发生规律、特征及原因分析

火灾作为一种灾害现象,其发生与发展具有随机性和确定性的双重特性.火灾统计分析是研究火灾随机性规律的一种有效方法,它通过总结、整理和分析大量的原始火灾资料,归纳出火灾发生的统计性规律.本文从1993-2003年我国火灾形势着手,联系火灾形势发展过程中的火灾起数、火灾中人员伤亡和直接财产损失等大量数据,重点分析了特大火灾的原因及类别特征,总结了特大火灾发生发展的规律和趋势,为正确认识和预测我国火灾形势、做好特大火灾的预防工作提供了参考.     

2006年7-8月国内安全事故统计分析

统计了2006年7-8月国内发生的各种安全事故279起,包括矿业事故、交通事故、爆炸事故、火灾、毒物泄露和中毒及其他事故.统计表明,在这些事故中,交通事故最多,占55.91%,其次是矿业事故(22.22%)、爆炸事故(9.32%)、其他事故(5.73%)、毒物泄露和中毒(4.66%)、火灾(2.15%).279起事故共死亡1 253人,伤1 519人,死亡人数的百分比分别为交通事故52.27%、矿业事故26.74%、爆炸事故11.65%、其他事故4.79%、火灾2.39%、泄露中毒2.15%;受伤人数的百分比分别为交通事故53.59%、泄露中毒17.38%、爆炸事故14.42%、矿业事故10.73%、其他事故2.63%、火灾1.25%.     

3S技术在地震研究领域应用的研究进展

随着3S(RS、GIS、GPS)技术的快速发展,其在地震领域上的应用已引起国内外研究机构和学者的广泛关注。以Web of Science核心数据集为文献源,检索2000-2019年3S技术应用于地震领域的文献,利用Citespace可视化分析软件进行文献分析,通过探究研究主题及研究热点的演变趋势,总结分析了当前研究前沿。研究结果表明,3S技术应用于地震领域涉及到了多个交叉学科及方向,体现出地震领域的研究具有广泛性和综合性的特点;通过聚类分析,发现可划分为8个知识群组,形成了以"地震案例"与"板块运动"为主的两条演化路径;关键词分析表明"合成孔径雷达应用"、"模型反演","滑动分布研究"成为近几年的研究热点,并趋向于多样化的趋势发展。     

新安江流域土地利用结构对水质的影响

以新安江上游流域为研究区域,用该流域2010年5月TM遥感影像图作为底图,通过实地野外调查获取了新安江流域的土地利用图.运用ArcGIS的水文、空间分析功能,将流域划分为8个子流域,并分析各子流域的土地利用结构.根据2010年1～12月的水质监测数据,分析TN、TP、高锰酸盐指数、NH4+-N、粪大肠菌群的时空变化特征,以及与土地利用结构之间的定量关系.结果表明,TN和NH4+-N有明显的时间变化特征,为枯时期>丰水期>平水期,而其他几种指标没有明显的时间变化.在空间上,整体上呈现出渔梁、浦口污染最为严重.流域内土地利用结构与水质之间的相关关系表现为:耕地、水体、建筑用地起源作用,林地、草地起汇作用.在年度上看,耕地对TN、NH4+-N、高锰酸盐指数影响最大,草地对TP影响最大;不同水期上,枯水期和丰水期,对各指标影响最大的土地利用类型为耕地,在平水期,对TN、TP、粪大肠菌群影响最大的土地利用类型分别为耕地、草地、林地.     

基于物质流分析的钾素流动与循环研究

我国钾矿资源匮乏,自给率低,主要的使用和流动过程集中在种植业活动中.本研究利用物质流分析方法对2009年我国种植业生产和消费过程中钾素的流动和循环过程进行了解析.结果表明,农田土壤钾素平均亏损量达到50.4 kg·hm-2,大量钾素由陆生生态系统进入水生生态系统,造成资源流失.伴随降雨径流进入水环境的钾素达231.2万t·a-1,占当年化学钾肥施用量的40.97%.生活污水排放是钾素进入水体的另一主要途径,城市和农村区域的年排放量分别为67.1万t·a-1和54.7万t·a-1,占进入水环境钾素总量的19.00%和15.50%.其中通过城市污水处理厂排放进入地表水环境中钾素为50.5万t·a-1,占城市区域排放总量的75.25%.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.     

A data-driven narratives skeleton pattern recognition from accident reports dataset for human-and-organizational-factors analysis

Accidents in the process industry involve several interacting factors, including human and organizational factors (HOFs). A long-standing obstacle to HOFs analysis is lack of data. Accident reports are an essential data source to learn from the past and contain HOFs-related data, but they are usually unstructured text in a not standardized format. Some studies have explored the extraction of information automatically from accident reports based on Natural Language Processing (NLP) techniques. However, they were not dedicated to HOFs. Risk communication is considered an essential pillar in safety and risk science. This research develops a HOFs-focused risk communication framework based on the NLP techniques that can support risk assessment and mitigation. The proposed approach automatically extracts the target groups oriented “Who, When, Where, Why” (4Ws) information from accident reports.This framework was applied to explore the eMARS database. The results show that the “4Ws” skeleton of narratives has appreciated performance in pattern recognition and holistic information analysis. The graphical representation interfaces are designed to display the features of HOFs-related accidents, which can better be communicated to the sharp-end operators and decision-makers.     

A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.