农村环境污染控制区划方法与应用研究

针对农村环境污染发生的广域性、分散性和随机性等特征,采用清单分析法,核算了农村环境污染物COD、TN、TP排放量和排放浓度,并以环境污染敏感性评价和污染类型划分为研究基础,探讨了农村环境污染控制区划方法,提出了区划原则、分区方法和命名方法.采用上述方法,在江苏省进行了应用研究,将江苏省农村环境污染控制区共划分为3个一级区和13个二级区,对不同级区实施分区管理和分类控制,分区结果可以为农村环境污染控制决策提供科学依据.     

华北地区冬半年空气污染天气客观分型研究

利用2013—2016年冬半年ERA-interim再分析资料,以及同期空气污染资料、地面常规气象观测资料和探空资料,采用PCT (Principal Component Analysis in T-mode)客观分型方法对华北地区冬半年海平面气压场进行天气分型,并探究不同月份不同天气型对应的空气污染状况及污染气象参数分布特征,进而从污染气象学的角度揭示重污染潜势天气型的气候特征.结果表明:冬半年海平面气压场共对应9种天气类型,其中,5型(均压场型)、6型(高压内部型)和8型(高压后部型)为3种重污染潜势天气型,冬半年对应的PM_(2.5)均值浓度分别为144.11、136.99和148.26μg·m~(-3),而1型(T型高压前部型)和3型(低压底部型)为两种清洁天气型,冬半年对应的PM_(2.5)均值浓度分别为97.12和80.83μg·m~(-3);重污染潜势天气型对应的边界层结构呈现出稳定能量大、混合层厚度和通风系数小的大气层结稳定的静稳天气特征,其能够反映大气污染潜势;研究还发现,即使是同一天气型,其在不同月份对污染物的扩散影响也存在差异,因此,建议在今后的污染潜势天气型研究中分月份进行.本研究可为华北地区空气污染潜势预报及大气重污染预报预警的客观化、自动化提供科学依据和技术支持.     

基于景观结构的区域生态风险分析

以深圳市龙华镇为中心的324km²正方形区域为例,以1988～1996年期间的5幅景观遥感解译图为基本信息源,根据不同时段景观元素的结构特征,构造一个综合性生态风险指数,利用系统空间采样方法对生态风险指数进行变量空间化．通过对生态风险指数采样结果进行半变异函数分析和空间插值,可以解释生态风险的空间特征和内在形成机制．结果表明,1988～1996年,工作区内的生态风险强度不断增加,区域分布的不均匀性增大;南北方向为景观人为改造活动的主方向;沿梅澜和石龙两条高速公路沿线形成两条带状高生态风险区域,其中龙华镇中心及周围地区是急需采取保护性措施的重点区域．     

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。     

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.