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.     

Dynamic-risk-informed safety barrier management: An application to cost-effective barrier optimization based on data from multiple sources

An integrated approach for performance assessment and management of safety barriers in a systemic manner is needed concerning the prevention and mitigation of major accidents in chemical process industries. Particularly, the effects of safety barriers on system risk reduction should be assessed in a dynamic manner to support the decision-making on safety barrier establishments and improvements. A simulation approach, named Simulink-based Safety Barrier Modeling (SSBM), is proposed in this paper to conduct dynamic risk assessment of chemical facilities with the consideration of the degradation of safety barriers. The main functional features of the SSBM include i) the basic model structures of SSBM can be determined based on bow-tie diagrams, ii) multiple data (periodic proof test data, continuous condition-monitoring data, and accident precursor data) may be combined to update barrier failure probabilities and initiating event probabilities, iii) SSBM is able to handle uncertainty propagation in probabilistic risk assessment by using Monte Carlo simulations, and iv) cost-effectiveness analysis (CEA) and optimization algorithms are integrated to support the decision-making on safety barrier establishments and improvements. An illustrative case study is demonstrated to show the procedures of applying the SSBM on dynamic risk-informed safety barrier management and validate the feasibility of implementing the SSBM for cost-effective safety barrier optimization.     

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.     

中国主粮作物生物炭产量效应的Meta分析

为定量评估生物炭对主粮作物产量的影响,收集了公开发表的116篇相关文献,共866对数据,采用Meta分析法定量分析了生物炭对我国主粮作物产量的影响及其影响因子,同时构建结构方程模型(SEM)进一步解释了因子间的交互关系.结果表明,与不施用生物炭相比,生物炭施用后可改善主粮田土壤理化性质,提高主粮作物产量,平均增产率为8.77%.其中,当生物炭pH为7～8时,平均增产率最大,可达26.49%;其C/N<60时,平均增产率为13.73%,显著高于C/N≥60的平均增产率.将生物炭施入酸性或中性土壤中,更能发挥其增产效应.当施炭量为10～20 t·hm^-2时,小麦和玉米的平均增产率最大;施炭量为15～25 t·hm^-2时,水稻平均增产率最大.但是,不同施炭水平的水稻增产率相近,可考虑损失部分产量,适当减施以兼顾经济效益.此外,生物炭增产效应会随施用年限增加而不断减弱,一般3 a后增产不显著.SEM表明生物炭施用量不仅直接影响主粮作物产量,还通过改善土壤肥力间接影响主粮作物产量,而生物炭C/N和pH仅通过改善土壤肥力影响主粮作物产量.因此,今后...     

飞机实验室气候试验风险识别及分析

目的 研究飞机实验室气候试验过程中风险的识别及分析问题.方法 根据飞机实验室气候试验流程,针对试验设计、试验准备、试验实施和试验确认等阶段进行风险识别和分析.结果 及结论识别并分析出实验室气候试验各阶段的风险,并在国内首次某民机实验室气候试验中得到应用.     

大气监测时段优化布点技术研究

基于虚拟仪器技术的空气监测系统中电化学气体传感器价格昂贵且使用寿命短暂,文章利用多目标特征分析法对传感器监测时段进行布点,准确反映实时监测状况,减少传感器使用频率。     

台风“安比”对河北东南部地区一次O3污染影响的特征分析

利用欧洲中心ERA-Interim数据以及河北省环境监测站、气象站观测资料,对2018年7月22—24日台风"安比"影响河北前后的一次O_3污染过程的生成、维持、清除进行动力分析,结果如下:台风外围的副热带高压天气型结构有利于河北南部地区污染物的积聚,其演变对预报大气光化学污染具有重要的意义;台风降水对O_3污染有非常好的清除作用,降水量越大,清除效果越好;高压带内部的高温有利于光化学反应,有利于O_3污染的形成;近地层的弱风辐合场与下沉气流的双重作用对O_3污染形成有很好的贡献;旋转形变效应(ds)负值区与河北中南部O_3污染区域对应;动力条件促使湍流运动加强,对O_3污染的清除有很好的作用.通过相关系数检验发现石家庄、邢台5 cm土壤温度和臭氧小时浓度相关性较高(相关系数0.75～0.85),由于土壤温度增加速度低于地表温度增加速度,臭氧浓度增加速度也低于地表温度增加速度,因此5 cm土壤温度比地表温度更及时反映了臭氧光化学反应强度.     

室内空气中的气态多环芳烃的被动采样监测

利用自制被动采样装置,在2011年秋冬季对南京市部分地区室内空气中5种气态多环芳烃(PAHs)(萘、苊烯、苊、芴、菲)进行了为期100d的连续采样检测,被动采样器的采样速率为0.012m3/d,5种PAHs的回收率在63%～105%之间,方法检出限在1.1～2.4ng范围内。结果表明,南京市5处不同室内环境空气中萘的浓度最高,占总量的90%以上。室内环境空气中5种PAHs的总浓度为230～1564ng/m3。住宅内人体对5种PAHs的暴露速率为479～560ng/h。     

水源水库藻类功能群落演替特征及水质评价

为了解水源水库的藻类功能群落时空演替特征及水质变化,以李家河水库为例, 2018年9月～2019年6月对藻类及水质因子开展连续监测,采用功能类群划分方法对水库藻类进行了识别与分类,探讨了藻类功能群落与水质间关系,并结合WQI指数法进行水质评价.结果表明,本研究共获得藻类56种,隶属于4门28属,可划分为15个功能群类,其中优势藻类功能群落分别为B、 D、 G、 J、 L₀、Mp、 P、 W₁和X₁;李家河水库藻类结构呈现明显的季节性特征,混合期藻密度明显低于分层期,其中混合期的主要功能藻种为小球藻和小环藻,分层期的主要功能藻种为舟形藻和针杆藻.冗余分析(RDA)表明,水温、混合层深度和RWCS指数是驱动藻类演替的主要因子;WQI分析结果显示李家河水体水质为"良好",混合期水质略好于分层期.本研究指出扬水曝气系统可改变藻类功能群落的演替特征,有效改善水源水库水质,保障了饮水供水安全.