基于贝叶斯网络的复杂工程安全管理决策支持方法研究

为改进当前复杂工程安全风险管理中普遍存在偏重事前管理、过分依赖专家经验等不足,探讨复杂工程贝叶斯网络(BN)的构建流程,提出基于BN的安全管理辅助决策分析(DSA)方法,并以武汉越江地铁盾构隧道渗漏水事故为例进行实证研究。结果表明:利用BN能够融合多领域专家关于具体工程的实践经验,以有向图形式直观表达安全事故致因变量间的不确定性关系,在事前阶段运用正向推理技术预测事故发生概率以界定警情等级及早防范,事中运用重要度分析辨识关键致险因子以明确过程控制要点,事后运用反向推理技术快速诊断查明最可能致因组合,从而为复杂工程安全事故全过程管理提供实时辅助决策支持。     

基于熵权属性识别理论的管道第三方破坏可能性研究

为全面与贴近实际地保证评估结果的准确性,从管道沿线社会环境、居民、企业、地方政府、管道公司5个方面辨识导致第三方破坏发生的影响因素,根据第三方破坏风险评估管段划分原则对管道分段,对各评估指标按百分制赋予分值。根据所评管段各评估指标提供的信息,应用熵权属性识别理论,计算评估指标的权重和属性测度,基于有序分割类和属性识别准则,利用置信度准则综合评估管道发生第三方破坏的可能性。最后,计算出管道第三方破坏可能性的综合评分值。通过算例表明,沿线地方经济发达程度与建设活动,员工自身素质,居民、企业、地方政府同管道公司关系密切程度,管道遭受人为故意破坏时的响应速度的对第三方破坏发生的影响较大。属性识别结果表明,该区段管道发生第三方破坏的可能性偏高,管道公司应对影响因素予以重视和持续地关注。     

长三角游乐型主题公园客流时空分布特征及其影响因素分析——以上海欢乐谷、常州恐龙园、芜湖方特为例

以长三角三大大型游乐型主题公园为研究案例地,基于大样本资料,将数理统计与ArcGIS空间分析相结合,分析主题公园客流时空分布特征,并采用地理探测器探讨影响其旅游客流空间分布的主要因素。研究表明：（1）长三角主题公园客流空间分布整体呈现出“核心区（长三角）点轴辐射、外围区（环长三角）面状均质、边缘区点状分布”格局。长三角确立客流分布主体边界,省级行政区（除上海）勾勒出核心市场边界。（2）客流空间分布特征因时间段和案例地不同而存在差异。主题公园游客空间使用曲线符合基本型曲线特征,一般性时间客源呈近域型分布,而节假日特殊时期为广域型分布。（3）三大主题公园客源主体范围与形态存在一定异质性。上海欢乐谷与常州恐龙园客源市场形态较为相似,芜湖方特客源市场呈近似饼状分布。主题公园80%市场域的重叠区域较大,主题公园间存在一定空间竞争关系,客源市场分布受其所在城市等级和地位的影响。（4）主题公园客流量的影响因素在空间上发生不同程度和强度的变异。经济联系强度、居民人均可支配收入和交通距离是影响客流量的主导影响因素,但不同区域的主导驱动因素存在显著差异。两两因子交互作用,双线性或非线性地加强了对游客来源地空间分布的解释力。     

资源枯竭城市产业结构转型升级水平测度及其影响因素——基于24座地级市的面板数据

产业结构转型升级是资源枯竭城市实现可持续发展的必由之路,产业结构转型升级水平测度及其影响因素研究,则是精准制定产业转型政策的重要依据。基于2008—2017年我国24座资源枯竭地级市的面板数据,从方向、速率及水平三方面测算其产业结构转型升级状况,并运用固定效应模型探究产业结构转型升级的影响因素,结果表明：（1）我国多数资源枯竭城市主要向第三产业转型,少数城市注重培育多产融合的转型模式;（2）我国资源枯竭城市2013—2017年产业结构转型升级速率整体较2008—2012年快,转型升级速率呈加快趋势,但各地区及城市之间存在一定差异性;（3）我国资源枯竭城市产业结构高级化指数总体呈上升态势,产业结构不断优化升级,但整体水平始终低于全国平均水平,并呈东部较高、中西部和东北部较低的空间分异特征;（4）就业总人数、财政支出占GDP比例、社会消费品零售总额、城镇人口比例对资源枯竭城市产业结构转型升级呈正向影响效应,固定资产投资总额呈负向影响效应。     

阿什河流域养殖业废水对松花江水质影响及防治对策

通过选用纵向推流、横向扩散二维数学模型对阿什河流域畜禽养殖业废水排入松花江而对松花江水质的影响进行分析预测,在此基础上提出污染防治措施与对策。     

异烟酸-吡唑啉酮光度法测定水质中总氰化物的有关问题探讨

结合多年的工作经验,并阅读有关文献,对异烟酸-吡唑啉酮光度法测定水质中总氰化物的各种影响因素作了较为详尽的论述,并提出相应的解决方法。     

Modelling of heating furnace fire scenarios using fault tree analysis,a bayesian network,and a thermal transfer method for system reliability analysis

Energy production for oil companies requires high-performance equipment. However, after a certain period of use, this equipment could be damaged, causing numerous problems and defects, impeding production, and leading to incidents that may cause material and human losses. Therefore, it is necessary to master the performance parameters of each equipment, which are key indicators of its functionality and can indicate the possibility of reusing it after inspection and diagnosis. However, it is worth noting that the studied system is a vertical cylindrical furnace in Algeria's refinery. This paper describes a new hybrid approach, a methodology that evaluates system reliability by combining two studies. The first is based on the surety of the system's functioning through an analysis with a fault tree to identify the risks, followed by a quantitative analysis using a Bayesian method. The second study follows the thermal calculation of the equipment's performance parameters, namely: the thermal efficiency (η), heat transfer coefficient (h), and heat absorbed quantity (Q), using heat balance equations to quantify the heat absorbed quantity and identify the fluid flow regime inside the tube. The calculation considers three cases: The design case, the failure case, and the after renovation case. The originality of this work appears when the collected findings from the second study are fed into the first study's Bayesian Network (BN) to analyse the system's reliability using the calculated parameters. Then, BN is used to calculate the Probabilistic Importance Factors (PIFs) in order to identify the most impactful system components. Finally, a prediction analysis was conducted. The latter reveals that one of the furnace's major faults is the fumes accumulation inside the combustion chamber. Corrosion, overheating, coke formation inside the tubes, dumper deformation, and burner tip clogging all contribute to this fault.     

Bayesian networks based laboratory retrofitting towards inherent safety: A risk-based implementation framework

Over the years, a number of high-profile laboratory accidents involving severe injuries, fatalities, and economic losses have been reported, prompting a significant increase in efforts towards laboratory safety. However, the dominant safety measures rely excessively on add-on safeguards such as sprinklers and respirators and pay little attention to reducing the hazardous factors at their sources. This study introduced the inherent safety concept to minimize laboratory hazards and developed a dedicated implementation tool called Generic Laboratory Safety Metric (GLSM). The Traditional Laboratory Safety Checklist (TLSC) was first used to represent the safety indicators, and then the Precedence Chart (PC) and Bayesian Networks (BN) methods were used to reconcile the safety indicators to develop the GLSM. The developed GLSM was subsequently demonstrated through a case study of a university laboratory. The results revealed that the safety level increased from 2.44 to 3.52 after the risk-based inherently safer retrofitting, thus creating laboratory conditions with a relatively satisfactory safety level. This work presented a set of generic solutions to laboratory retrofitting towards inherent safety with a novel GLSM as the implementation tool. The proposed GLSM would contribute to risk quantification and identification of key risk factors for assigning targeted and fundamental safety measures to achieve inherently safer laboratories.     

Integration of fuzzy reliability analysis and consequence simulation to conduct risk assessment

Losses of containment within the natural gas network, located in most populated areas, could cause environmental damage, injuries, or even death. Accordingly, it is pivotal to adopt proper approaches to assess and mitigate the risk arising from potential losses. Within this context, it is required to exploit solid reliability and consequence analysis techniques. To this end, this paper presents a methodology established on the integration of a Fuzzy Bayesian Network and consequence simulation. The Bayesian Network is more flexible and realistic than classic approaches because it can consider conditional probabilities and prior information. Furthermore, Leaky Noisy-OR Gates are exploited to allow an easier filling of the Conditional Probability Tables. This task is performed through expert elicitation, adopting Intuitionistic Fuzzy Set Theory and Similarity Aggregation Method. Finally, the severity analysis is performed via a software, named Safeti, which provides an accurate evaluation of the consequences. To show the applicability of the framework, a pressure regulator of a Natural Gas Regulating and Metering Station is considered as case study. The proposed approach can assist asset managers in evaluating the risk arising from the operations, and, accordingly, it can guide them in making maintenance-related decisions to assure the safety of the operations.     

发酵生物制氢反应器研究进展

发酵法生物制氢受到广泛关注。在发酵细菌制氢过程中,反应器的设计和运行模式对产氢量和产氢速率具有重要影响。暗发酵生物制氢反应器主要包括序批式制氢反应器和连续流制氢反应器,其中序批式制氢反应器操作简单和容易控制,主要用于实验室研究;从经济性和实用性的角度考虑,连续流制氢反应器是实现工程应用的关键。文章综述了影响发酵制氢的影响因素:菌种准备、环境条件、营养条件和一些其他因素等。并分析了几种常用连续流发酵制氢反应器类型及其应用状况。