隧道救援站通风网络优化设计及人员疏散研究*

为确定铁路隧道救援站最佳通风结构，对烟道布置提出合理建议，采用计算流体力学（CFD）的方法，对铁路隧道救援站通风网络进行优化设计。同时，采用模拟计算的方法对救援站人员疏散进行模拟分析，确定救援横通道布置及防护门开度设置的合理性。研究结果表明:在同等通风参数工况设置下，采用多节点排烟竖井结构后各救援横通道流量分配更均匀，救援站压力平衡性更好，通风效率可提升15%；通过疏散模拟证实，在长560 m的紧急救援站范围内设置10条疏散横通道，横通道设1.7 m宽的逃生门能够满足疏散要求。     

基于统计学方法的南方某市管网水水质特征研究

为综合解析南方某市管网末梢水水质的时空变化特征、识别主要响应指标及指标间相关性,以该市近4年供水管网末梢水水质监测数据为研究对象,利用主成分分析法和对应分析法对管网末梢水质进行了特征研究。主成分分析法评价结果表明:该市管网末梢水年度总体水质存在差异,其中以2016年为最佳;夏秋季节更易发生水质异常现象,5~9月应加强管网末梢水水质管控;区域上看已完成深度处理改造的梅林水厂供水覆盖区管网末梢水水质最优,南山水厂供水覆盖区管网末梢水水质最差;水质指标硫酸盐、氯化物、硬度之间,Fe、浊度、总氯之间具有相对其他水质指标更高的相关性。对应分析法结果表明:管网末梢水水质主要响应指标可重点关注Fe、浊度、耗氧量、总氯和硫酸盐;其中Fe、浊度、耗氧量呈正相关性强,上述指标与硫酸盐、总氯呈负相关性。     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.     

公路网规划环境影响评价中的困难和不确定性分析

首先分析了公路网规划环境影响评价中的困难,即评价方法的不成熟和获取资料的困难;然后重点分析了评价中的公路网规划方案的不确定、环境信息的不确定、空间信息的不确定和环境影响程度的不确定;最后提出了 4种解决困难和降低不确定的方法,即制定公路网规划环境影响评价技术导则,使用基于情景分析的预测方法,广泛开展公众参与以及通过多方协作的方式开展环境影响评价工作.     

改善太仓城区水环境原型调水实验研究及模型建立

为达到改善太仓城区水环境的目的,于2004年4月21日进行了调水实验.调水历时14 h,期间向城区调水21.8万m3.通过对监测点采样,得到水量、水质实测数据.根据实验数据,建立了符合太仓河网的水量水质数学模型,分析了从长江引水对太仓城区水环境的改善程度.同时对不同引水方案情况下城区水环境改善效果,以及截污和清淤对水环境的影响做了分析.研究表明,引水只能短期改善水环境;要根治水环境,必须从源头抓起,进行截污.     

基于网络层次分析法的石油化工设施安全风险评估

为评估石油化工设施的安全风险,提出了一种基于网络层次分析法的安全风险评估模型。在构建石油化工设施安全风险网络层次分析模型的基础上,采用Saaty标度法对安全风险参数进行量化,利用SD软件对安全风险参数进行排序。仿真结果表明,网络层次分析法考虑了安全风险参数之间的相互作用和相互影响,能改进基于线性组合关系的递阶层次分析模型的不足,评估结果可为石油化工企业制定安全风险管理措施提供决策依据。     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

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 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.     

基于视频分析的空管员违规行为识别方法*

为利用视频数据对空管员违规行为进行智能化分析,降低不安全事件发生率,提出2阶段的违规行为识别模型（AR-ResNeXt）,基于实地调研构建空管员视频数据集,利用最小化动态多实例学习损失函数和中心损失函数,获得违规行为检测的判别特征表示,结合异常回归网络和ResNeXt网络,完成对空管员违规行为的时序区间检测与动作分类。研究结果表明:AR-ResNeXt模型在自制数据集中,其帧级AUC达到82.9%,分类准确率达到87.8%,可准确识别空管员发生违规行为的时序区间并进行分类,研究结果可为保障空中交通安全奠定基础。