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.     

贵阳市道路灰尘和土壤重金属来源识别比较

通过对89个城市土壤样和78个道路灰尘样中重金属含量的对比,利用多元统计方法识别研究区的元素来源.结果显示道路灰尘元素含量一般高于土壤.就均值而言,灰尘中Hg、Cd、Pb、Cu、Cr含量超过土壤中相应元素含量,灰尘中Zn含量与土壤中Zn含量相当,只有灰尘中As含量略低于土壤.灰尘中元素含量都高于中国和贵州表层土壤背景值,土壤中元素含量除Pb外都高于中国和贵州表层土壤背景值.Cd,Cr,Pb,Hg,Cu和Zn含量较高主要是受人为因素的影响.贵阳市道路灰尘和土壤中8种元素有着不同的来源.相关分析、主成分分析表明交通排放等人为因素是重金属主要来源,外来客土也是重金属重要来源;灰尘Pb主要来源于交通排放和钢铁厂;而土壤Cr具有复合污染的特征,主要来源于外来客土.     

乌梁素海冰盖中微塑料的分布特征及其与盐度、叶绿素a的响应关系

目前关于淡水湖泊冰盖中微塑料的分布特征研究还鲜见报道.为阐明乌梁素海冰盖中微塑料的赋存特征以及其与冰盖盐度、叶绿素a浓度之间的响应关系,通过野外采样、显微镜观察、傅里叶红外光谱测定和相关性分析等方法,对冰封期乌梁素海冰盖中微塑料的丰度、颜色、形状和种类进行鉴定,通过相关性分析探求微塑料在冰盖中的分布特征及其与盐度、叶绿...     

不同碳源模式下酵母菌PNY2013同步脱氮除磷的应用研究

针对首次分离得到的一株具有同步脱氮除磷新功能的热带假丝酵母（Candida tropicalis） PNY2013,通过生理及动力学特征,连续流运行操作及其在含糖类工业废水中的应用3个环节,探讨了不同碳源模式下PNY2013同步脱氮除磷的特性.结果表明：PNY2013以葡萄糖、乙醇及乙酸为唯一碳源时均生长良好,其最大比增长速率μ_max分别为0.1327、0.1252及0.1115 h^-1,其同步脱氮除磷率分别可达100%、80%、100%（NH₄⁺-N）及93%、95%、98%（PO₄^3--P）.3种碳源下PNY2013同步脱氮除磷的最佳条件基本接近为：温度30℃,pH=8.0,溶解氧0~2 mg·L^-1,C/N=200∶5左右.PNY2013同步脱氮除磷的长期连续运行条件下的实验进一步表明,以葡萄糖为碳源条件下,进水NH₄⁺-N及PO₄^3--P浓度分别达400及80 mg·L^-1时,两者去除率均接近100%.与这种超强能力相比,以乙醇及乙酸为碳源条件下,进水NH₄⁺-N及PO₄^3--P浓度分别达100及20 mg·L^-1时,两者的去除率也可达60%~80%（NH₄⁺-N）及40%（PO₄^3--P）,显示出相当的同步脱氮除磷能力.在以模拟制糖废水、淀粉加工废水、啤酒废水、味精废水这4种典型含糖工业废水为碳源条件下,除淀粉加工废水外PNY2013均能有效去除COD、NH₄⁺-N和PO₄^3--P,其中,制糖、啤酒、制药废水中的COD去除率分别可达40%、89%、96%,NH₄⁺-N去除率分别为85%、94%、76%,PO₄^3--P去除率均为90%.即使在40000 mg·L^-1（制糖）及12500 mg·L^-1（啤酒）的高COD条件下,PNY2013也均具有稳定的NH₄⁺-N和PO₄^3--P去除效果,显示出良好的同步脱氮除磷应用前景.     

太湖底泥疏浚的水环境质量风险性分析

采用基于因子分析的主成分分析法将五里湖的16个水质参数概括为5个主成分,研究了各主成分以及其综合主成分在疏浚前后的变化,并以此为依据就底泥疏浚对水环境质量的影响进行了评价.结果表明,疏浚过程中,五里湖疏浚区的综合主成分得分最高,为1.15,水质综合排名在最后,水体色度和污染指数(F1)也达到最高值5.0;同时对照区综合主成分得分以及水体污染和色度指数(F1,F2)也相应升高,水质质量明显下降;这说明疏浚运作具有促进表层沉积物发生再悬浮和扩散作用,以及沉积物中内源性营养盐和重金属离子的释放作用.采用Monte Carlo随机采样法,利用平衡分配模型模拟和预测了疏浚过程中悬浮沉积物中重金属离子释放的阈值,结果表明疏浚过程中大部分重金属离子由悬浮沉积物向水体中释放的风险性较高,可信度达到90%以上,尤其是重金属离子Ni²⁺以及非金属污染物As释放的风险性较大,其可能的释放量分别分布在各自地表水环境质量标准EQS的28.6%～+∞之间和34%～+∞之间,说明底泥疏浚具有导致五里湖水体环境质量短期恶化的风险.此外,Monte Carlo随机采样分析方法应用也许可为湖泊底泥疏浚生态风险评价提供一条新的途径.