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.     

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.     

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.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.     

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.     

城市河网区河流沉积物磷形态分布特征及释放贡献

以嘉兴城市河网区为研究区域,在调查区域水质和沉积物磷空间变化特征的基础上,分析沉积物中各形态磷的垂直与空间赋存状态,通过吸附释放参数计算EPC₀值,明确了沉积物-水界面磷迁移影响因素和沉积物中活跃磷素的释放风险和贡献.结果表明,研究区域水质氮磷污染较为严重,沉积物TP含量均值呈北部河网区>西部河网区>南部河网区,基于单因子指数法的生态风险评价为Ⅱ、Ⅲ和Ⅳ级的样本比例分别为10.91%、25.45%和63.64%.沉积物各形态磷整体呈现TP>IP>HCl-P>NaOH-P>OP,沉积物生物有效磷（BAP）整体呈现Olsen-P>AAP>WSP>RDP;在沉积物垂直剖面中,部分位点受到外界因素的强烈影响垂向波动较大,整体上是随深度的递增而减小,这与近年来研究区域接收的外源磷积累过程在加重有关.相关性分析表明,沉积物磷吸附容量（Q_max）与铁铝氧化物呈显著相关性 （p<0.05）;释放参数EPC₀与TP、IP与HCl-P呈显著相关性 （p<0.05）.结合BAP形态,研究区域河流沉积物NaOH-P、HCl-P、AAP、Olsen-P及WSP存在较高的释放潜力.南部河网区沉积物主要呈现磷“源”角色,西部和北部河网区部分区域沉积物充当磷“源”角色.南部河网区在长短时间尺度下的磷主要释放形态为NaOH-P、HCl-P和OP,北部河网区在长时间尺度下主要释放磷形态为NaOH-P、HCl-P,西部河网区在短时间尺度下主要释放形态以HCl-P为主,在长时间尺度下3种磷形态的释放风险仍然较高.     

因子回归和交互联合探索区域植被覆盖度的影响因素——以三江源地区为例

利用SPOT VEGETATION数据获取的归一化植被指数（NDVI）,分析三江源地区植被覆盖度（FVC）的空间异质性,围绕自然和人类活动因素,基于因子回归与交互作用联合探索自然因素和人为因素对三江源地区植被覆盖的影响.结果表明：（1）三江源地区植被覆盖度整体呈现明显的空间异质性;（2）总体上FVC空间分布的影响因素表现为自然环境因素>人类活动因素;（3）降水是影响三江源地区FVC的主要驱动因子,解释力达0.777;（4）因子交互发现：驱动解释系统呈现双因子增强,说明从系统的角度来看不存在独立起作用的因子,年降水量与其他因子的交互作用最强;（5）降水梯度影响了三江源地区FVC空间异质性的解释程度.随着降水增加,因子解释力趋稳,在降水量较多的三江源东部地区,FVC趋向于更易受高程和气温的影响;（6）数据结果亦验证了因子独立的全局最优筛选仅仅是模拟因变量特征的最优函数,其解释效果与因变量的驱动解释不能完全等同.     

基于空间尺度效应的西南地区植被NPP影响因子探测

植被净初级生产力(NPP)是评价陆地生态系统质量的重要参数,研究植被NPP时空演变特征及其驱动力对区域生态环境保护和可持续发展具有重大意义.基于MODIS NPP数据、气象数据、 DEM数据、人口密度数据、 GDP数据和土地利用类型数据,采用一元线性回归分析、 R/S分析和地理探测器模型,分析西南地区及其六大地貌单元植被NPP时空演变特征及未来变化趋势,探究植被NPP空间分异的影响因子.结果表明,2000～2020年西南地区植被NPP整体呈极显著上升趋势.地貌单元中,除青藏高原南部外,其余地貌单元植被NPP均表现为改善态势,其中四川盆地和云贵高原表现为极显著改善.西南地区的植被NPP变化斜率整体呈现“东高西低”的分布格局.西南地区及各地貌单元植被NPP呈上升趋势的区域面积均大于呈下降趋势的区域面积,但未来植被NPP变化趋势均以下降为主.地理探测器结果表明,除云贵高原植被NPP空间分异主要受气温影响外,海拔是西南地区及各地貌单元植被NPP空间分异的主导因子.交互探测结果表明,影响因子之间的交互作用均表现为双因子增强或非线性增强,其中,海拔∩温度对西南地区植被NPP空间分异的解释力最大.地...     

长江经济带二氧化碳净排放时空演变特征及脱钩效应

研究区域CO₂净排放,对“碳中和”战略的实现具有重要意义.以长江经济带为例,在揭示1999～2018年长江经济带CO₂净排放时空演变特征的基础上,分析长江经济带不同区域社会发展与CO₂净排放的脱钩效应,以期为差异化区域产业发展和碳减排路径提供支持.结果表明：(1)1999～2012年长江经济带CO₂排放量上升了2 244.23×10⁶ t,碳汇量在研究时间段增长了148.07×10⁶ t;(2)长江经济带呈现“变绿”趋势,2013～2018年中高碳汇量区域(NPP>800 g·m^-2,以C计)面积较1999～2012年上升了23.25%;(3)长江经济带下游经济社会发展与CO₂净排放脱钩效应较强,上、中和下游强脱钩城市占长江经济带强脱钩城市的比例分别为12%、34%和54%.     

人类活动净氮输入时空变化特征及其影响因素——以香溪河流域为例

为了阐明人类活动对三峡库区第一大支流香溪河流域氮输入的影响程度,基于人类活动净氮输入（NANI）模型估算了2001~2015年间香溪河流域乡镇水平的人类活动净氮输入,分析了氮输入的主要来源及其动态变化.结果表明：时间尺度上,香溪河流域人类活动净氮输入（NANI）因氮素施用的变化而呈现先上升后下降最后上升的趋势,但2015年相对2001年的NANI下降了143kg N/（km²·a）;空间尺度上,香溪河流域NANI整体呈现北低南高的分布格局,其中NANI输入强度差异较大的区域主要有昭君镇、峡口镇和黄粮镇.从人类活动净氮输入的组成上来看,氮肥施用仍然是最主要的来源（40.06%）,其次为大气氮沉降（29.98%）和食品/饲料净氮输入（27.75%）,作物固氮仅占净氮输入总量的2.21%.香溪河流域的NANI与人口密度和耕地面积比例极显著相关（P<0.001）;而NANI与河流氮输出的相关性不显著,香溪河流域河流氮输出占NANI的比例仅为24.28%.因此,可以通过减少氮素施用降低流域尺度氮素净输入量,但该流域NANI与河流氮输出无直接的响应关系.