爆炸超压场景下危化品社会风险对允许规划人口密度的影响分析*

为研究危化品重大危险源基于社会风险基准的规划管控影响,采用我国标准规定的定量风险评价方法,TNT当量炸药简化方法,针对最大TNT当量炸药、事故发生总累计频率、人口密度分布控制参数等不同工况条件,对比分析国土开发强度的允许人口密度受社会风险约束影响的变化规律。研究结果表明:人口密度指数分布控制参数Nk与Nb的允许取值随最大TNT当量炸药和事故发生总累计频率降低而提高;确定工况下,允许总人口规模受最大TNT当量炸药影响很小;在最大TNT当量炸药大于100 t或事故发生总累计频率小于1×10-8次/a时可按最大事故场景进行规划控制分析。     

深埋水平岩层隧道开挖稳定性分析及控制*

针对深埋高地应力水平岩层掌子面开挖稳定性及支护结构失效问题,以大峡谷隧道为工程背景,通过现场测试、室内试验、数值模拟等方法,探究深埋高地应力水平岩层失稳机理及控制措施。研究结果表明:坚硬岩体被节理面切割后,在高地应力作用下容易发生挤压破碎,破碎岩体遇水发生软化,导致掌子面发生大范围塌方,初支和超前支护失效;隧道开挖后岩层发生不均匀沉降,浅部岩层最先发生弯折破坏,层内块体错动滑移,继而向上方岩层发展,并伴随层间分离和层内裂隙发育,最终形成宏观破裂面;提出的台阶法、2 m开挖进尺、砼喷层、双层小导管、提高初支强度的整体优化控制措施,可有效提高现场支护效果。     

基于Logistic回归的燃气管网风险因素重要度分析方法研究

为确定燃气管网风险评估的关键风险因素,以A省各地市燃气管网为研究对象,基于燃气专家经验确定燃气管线风险等级,提出基于Logistic回归的燃气管网风险因素重要度分析方法。采用样本增强及随机抽样的方式,选取400个均衡样本作为管网评估数据输入,通过因子分析方法对其进行降维,得到3个公共因子并作为一级指标反向构建燃气管网风险评估指标体系;利用有序多分类Logistic回归方法,根据回归系数绝对值大小对风险因素进行重要度排序。研究结果表明：外界环境对燃气管网风险的贡献程度相对较高,管道自身因素和巡检养护次之。研究结果可为城市燃气风险防控提供理论依据和方法参考。     

Experimental study of humidity influence on triboelectric charging of particle-laden duct flows

Electrostatic charge on powders arises during pneumatic transport due to particle–particle and particle–surface interactions via triboelectrification. This is a potential threat to the safety of industrial production and the source of numerous fires and dust explosions in the past. Triboelectric charges are affected by environmental conditions, such as ambient temperature and relative humidity. In this work, we experimentally investigated the influence of ambient humidity on the particle charge of gas–solid flows in a square-shaped duct. Monodisperse PMMA particles are fed into a fully developed airflow in a PMMA duct and then pass through a metallic duct section. The charge of particles is measured at the outlet of the metallic duct via a Faraday cup. By measuring the electrostatic charge under various environmental conditions, we observed that the electrostatic charge first increases with the humidity and then decreases when the humidity becomes higher.     

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.