基于贝叶斯网络的养老院火灾风险评估方法研究

为预防养老院火灾事故,结合事故树法(FTA)和贝叶斯网络法(BN),建立了一套养老院火灾风险定量评估模型。首先,采用事故树法建立潜在的养老院火灾事故场景;其次,考虑到养老院火灾事故场景中不确定因素的影响,将事故树模型转化为贝叶斯网络模型,并结合有人员伤亡的养老院火灾事故发生发展实际优化模型;最后,以某市养老院为例,结合调研、文献及统计数据确定先验概率及条件概率,并用GENIE 2.0软件实现贝叶斯图形化,分析验证该模型逻辑可行性。结果表明:通过该模型和方法,不仅可以预测养老院火灾事故中各场景发生发展概率,还能对各风险因素敏感度和最大致因链进行分析,提高了风险因素辨识和评价的准确性,可以为养老院火灾风险分析和防控提供参考。     

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.     

一种新型危险化工工艺安全评估方法的设计

在安全生产过程中,建设项目风险评价在我国经过几十年的发展,无论在模型亦是方法上均有所进步,但对化工项目进行危险化工工艺风险等级评价的研究并不多见。本文按照危险化工工艺表征涉及的影响因素,在参考了日本劳动省"六阶段"的定量评价表以及危险度评价法并对15种危险化工工艺定性分析的基础上,提出了一种基于危险度评价法的更为全面的危险化工工艺辨识方法。该评价方法可用于确定危险化工工艺的风险等级。为化工企业工艺危险的实时评估和安全管理提供技术支撑和科学途径。     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.     

A new fault detection method for non-Gaussian process based on robust independent component analysis

Conventional fault detection method based on fast independent component analysis (FastICA) is sensitive to outliers in the modeling data and thus may perform poorly under the adverse effects of outliers. To solve such problem, a new fault detection method for non-Gaussian process based on robust independent component analysis (RobustICA) is proposed in this paper. A RobustICA algorithm which can effectively reduce the effects of outliers is firstly developed to estimate the mixing matrix and extract non-Gaussian feature called independent components (ICs) by robust whitening and robust determination of the maximum non-Gaussian directions. Furthermore, a monitoring statistic for each extracted IC is constructed to detect process faults. Simulations on a simple example of the mixing matrix estimation and a fault detection example in the continuous stirred tank reactor system demonstrate that the RobustICA achieves much higher estimation accuracy for the mixing matrix and the ICs than the commonly used FastICA algorithm, and the RobustICA-based fault detection method outperforms the conventional FastICA-based fault detection method in terms of the fault detection time and fault detection rate.     

A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

INTRODUCTION: Focusing on people and organizations, this paper aims to contribute to offshore safety assessment by proposing a methodology to model causal relationships. METHOD: The methodology is proposed in a general sense that it will be capable of accommodating modeling of multiple risk factors considered in offshore operations and will have the ability to deal with different types of data that may come from different resources. Reason's "Swiss cheese" model is used to form a generic offshore safety assessment framework, and Bayesian Network (BN) is tailored to fit into the framework to construct a causal relationship model. The proposed framework uses a five-level-structure model to address latent failures within the causal sequence of events. The five levels include Root causes level, Trigger events level, Incidents level, Accidents level, and Consequences level. To analyze and model a specified offshore installation safety, a BN model was established following the guideline of the proposed five-level framework. A range of events was specified, and the related prior and conditional probabilities regarding the BN model were assigned based on the inherent characteristics of each event. RESULTS: This paper shows that Reason's "Swiss cheese" model and BN can be jointly used in offshore safety assessment. On the one hand, the five-level conceptual model is enhanced by BNs that are capable of providing graphical demonstration of inter-relationships as well as calculating numerical values of occurrence likelihood for each failure event. Bayesian inference mechanism also makes it possible to monitor how a safety situation changes when information flow travel forwards and backwards within the networks. On the other hand, BN modeling relies heavily on experts' personal experiences and is therefore highly domain specific. IMPACT ON INDUSTRY: "Swiss cheese" model is such a theoretic framework that it is based on solid behavioral theory and therefore can be used to provide industry with a roadmap for BN modeling and implications. A case study of the collision risk between a Floating Production, Storage and Offloading (FPSO) unit and authorized vessels caused by human and organizational factors (HOFs) during operations is used to illustrate an industrial application of the proposed methodology.     

基于Bayesian网络的建筑火灾疏散条件安全性评估方法*

为了对建筑火灾疏散条件安全性进行评估,基于Bayesian网络对疏散条件重要构成要素及评估方法逻辑推理过程进行研究探讨。结果表明:评估网络结构、根节点、中间节点及目标节点之间存在因果关联关系;研究得出根节点先验概率与量化节点条件概率表设定方法;Bayesian网络将风险评估与人工智能分析方法相结合,实现对建筑火灾疏散条件的安全性评估,并可用于识别高风险建筑。     

A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment

In highly complex industries, capturing and employing expert systems is significantly important to an organization's success considering the advantages of knowledge-based systems. The two most important issues within the expert system applications in risk and reliability analysis are the acquisition of domain experts' professional knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition processes. The members of an expert panel regularly represent different experiences and knowledge. Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in current expert system applications in probabilistic risk assessment could not accurately represent the increasingly complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Z-numbers to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate the application and feasibility of the proposed methodology.     

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.     

Methodology for risk assessment of COVID-19 pandemic propagation

This paper proposes a methodology to perform risk analysis of the virus spread. It is based on the coupling between CFD modelling of bioaerosol dispersion to the calculation of probability of contact events. CFD model of near-field sneeze droplets dispersion is developed to build the SARS-CoV-2 effect zones and to adequately capture the safe distance. The most shared classification of droplets size distribution of sneezes was used.Droplets were modeled through additive heating/evaporation/boiling laws and their impact on the continuous phase was examined. Larger droplets move behind the droplet nuclei front and exhibit greater vertical drop due to the effect of gravity. CFD simulations provided the iso-risk curves extension (i.e., the maximum distance as well as the angle) enclosed by the incident outcome effect zone. To calculate the risk indexes, a fault tree was developed and the probability of transmission assuming as of the top event “COVID-19 infection” was calculated starting from the virus spread curve, as main base case. Four phases of virus spread evolution were identified: initiation, propagation, generalised propagation and termination. For each phase, the maximum allowable close contact was computed, being fixed the values of the acceptable risk index. In particular, it was found that during the propagation case, the maximum allowable close contacts is two, suggesting that at this point lockdown should be activated. The here developed methodology could drive policy containment design to curb spread COVID-19 infection.     

An extension to Fuzzy Fault Tree Analysis (FFTA) application in petrochemical process industry

Fault Tree Analysis (FTA) is an established technique in risk management associated with identified hazards specific to focused fields. It is a comprehensive, structured and logical analysis method aimed at identifying and assessing hazards of complex systems. To conduct a quantitative FTA, it is essential to have sufficient data. By considering the fact that sufficient data is not always available, the FTA method can be adopted into the problems under fuzzy environment, so called as Fuzzy Fault Tree Analysis (FFTA). This research extends FFTA methodology to petrochemical process industry in which fire, explosion and toxic gas releases are recognized as potential hazards. Specifically, the case study focuses on Deethanizer failure in petrochemical plant operations to demonstrate the proposed methodology. Consequently, the study has provided theoretical and practical values to challenge with operational data shortage in risk assessment.     

火灾风险评价的随机过程模型

运用事故树分析法和随机过程的马尔克夫模型，建立了评价火灾风险的动态模型．运用该模型能够计算某一系统特定地点起火后风险机率的时间分布，并能够指导消防设计和消防管理。     

Dynamic accident modeling for high-sulfur natural gas gathering station

Dynamic accident modeling for a gas gathering station is implemented to prevent high-sulfur natural gas leakage and develop equipment inspection strategy. The progress of abnormal event occurring in the gas gathering station is modeled by the combination of fault tree and event sequence diagram, based on accident causal chain theory, i.e. the progress is depicted as sequential failure of safety barriers, then, the occurrence probability of the consequence of abnormal event is predicted. Consequences of abnormal events are divided into accidents and accident precursors which include incidents, near misses and so on. The Bayesian theory updates failure probability of safety barrier when a new observation (i.e. accident precursors or accidents data) arrives. Bayesian network then correspondingly updates failure probabilities of basic events of the safety barriers with the ability of abductive reasoning. Consequence occurrence probability is also updated. The results show that occurrence probability trend of different consequences and failure probability trend of safety barriers and basic events of the safety barriers can be obtained using this method. In addition, the critical basic events which play an important role in accidents occurrence are also identified. All of these provide useful information for the maintenance and inspection of the gas gathering station.     

石油化工企业过程安全管理评估系统的设计与应用研究

为了提升国内石油化工企业过程安全管理水平,借鉴国内外过程安全管理经验和优秀实践,提出了一种应用于石油化工企业过程安全管理的评估思路和方法。根据石油化工行业特点,构建了由16个要素组成的过程安全管理评估体系框架。同时,介绍了该评估体系的评估准则设计、评估方法设计,以及评估系统的应用。实践表明,此石油化工企业过程安全管理评估系统,既能评估石油化工企业过程安全管理绩效,又能指导企业设计和运行一套过程安全管理系统。     

3-Parameters SPW technique: A new method for evaluation of target safety integrity level

Introduced by IEC-61508 standard, safety integrity levels (SIL) have been used for assessing the reliability of safety instrumented functions (SIF) for protection of the system under control in abnormal conditions. Different qualitative, semi-qualitative and quantitative methods have been proposed by the standard for establishing target safety integrity levels amongst which “Risk Graph” has gained wide attention due to its simplicity and easy-to-apply characteristics. However, this method is subject to many deficiencies that have forced industry men and experts to modify it to fit their demands. In this paper, a new modification to risk graph parameters has been proposed that adds more flexibility to them and reduces their subjective uncertainties but keeps the method as simple as before. Three parameters, namely severity (S), hazard avoidance probability (P), and demand rate (W) are used instead of former four parameters. Hence, the method is named SPW. The outcome results of this method can be directly converted to probability of failure on demand (PFD) or risk reduction factor (RRF). The proposed method has been tested on an example case that has been studied before with conventional risk graph and LOPA techniques. The results show that new method agrees well with LOPA and reduces costs imposed by conservative approximations assumed during application of conventional risk graph.     

Safety assessment of natural gas storage tank using similarity aggregation method based fuzzy fault tree analysis (SAM-FFTA) approach

Fault tree analysis (FTA) is an important method to analyze the failure causes of engineering systems and evaluate their safety and reliability. In practical application, the probabilities of bottom events in FTA are usually estimated according to the opinions of experts or engineers because it is difficult to obtain sufficient probability data of bottom events in fault tree. However, in many cases, there are many experts with different opinions or different forms of opinions. How to reasonably aggregate expert opinions is a challenge for the engineering application of fault tree method. In this study, a fuzzy fault tree analysis approach based on similarity aggregation method (SAM-FFTA) has been proposed. This method combines SAM with fuzzy set theory and can handled comprehensively diverse forms of opinions of different experts to obtain the probabilities of bottom events in fault tree. Finally, for verifying the applicability and flexibility of the proposed method, a natural gas spherical storage tank with a volume of 10,000 m³ was analyzed, and the importance of each bottom event was determined. The results show that flame, lightning spark, electrostatic spark, impact spark, mechanical breakdown and deformation/breakage have the most significant influence on the explosion of the natural gas spherical storage tank.     

贫数据条件下海底电缆故障概率评估方法*

为解决贫数据引起海底电缆失效概率评估的不确定性影响,实施有效的海底电缆故障风险管理,提出1种耦合模糊集理论、层次贝叶斯分析（HBA）和贝叶斯网络的海底电缆失效概率评估方法,识别海底电缆失效致因因素,梳理各因素之间的关联关系,并采用贝叶斯网络(BN)构建海底电缆失效模型;根据数据源特点将电缆失效因素分为数据完全缺失和具有稀少的先兆数据,采用模糊集理论(FST)计算完全没有可用数据的失效致因发生概率,通过HBA估计有稀少数据失效致因的发生概率;以失效致因发生概率为输入,通过贝叶斯网络实现海底电缆失效概率的动态评估。研究结果表明:FST-HBA-BN方法可以解决基本风险因素的数据稀缺问题,量化评估海底电缆失效概率,研究结果可为贫数据条件下的电缆失效风险管理提供支撑。     

A method for determining critical events during large disasters of production platforms

This article presents a calculation-based methodology to determine the dominant event class in each of the phases of disasters being analysed, and to address the question of whether different disasters have similarities at crucial times in each phase of the disaster. Our approach is based on event network analysis. Disasters can be modelled using block diagrams and multiphase process trees. We propose trees in this article can be used as a tool for modelling phases of a disaster. The starting point for developing these models was fault tree analysis used for modelling the reliability structure of complex systems. This study demonstrates the possibility of using dual fault trees to describe the process as opposed to the structure. In our analyses, we examined four major disasters of production platforms that occurred in the last 50 years: Ixtoc I, Piper Alpha, Petrobras 36 and Deep Water Horizon. The course of each of these disasters has been described, the basic events of these disasters have been isolated, and assigned to event classes. The hierarchical importance of events was determined using the Birnbaum reliability measure, Birnbaum structural measure, Fussell-Vesely measure, criticality measure and improvement potential. For each phase of the analysed disasters, event importance is ranked, and the most important events that contributed to the phase are identified. General principles on the analysed disasters and the methodology used are also discussed.     

基于改进层次分析法的火灾高危单位消防安全评估

开展消防安全评估对提升火灾高危单位消防安全管理水平和维护公共安全具有重要意义。根据火灾高危单位的特点,给出了消防安全评估指标体系,包括建筑防火、消防设备设施和消防安全管理3项一级指标,建筑整体布局、安全疏散系统、消防给水系统、自动灭火系统、防排烟系统、电气防火系统、其他设备及器材、日常管理、应急制度及培训教育、电器燃气消防器材管理10项二级指标,建筑类别、安全出口、室外消防水、自动喷水灭火系统、风机、消防电源及其配电、防火门窗、消防安全管理制度、应急制度、电器管理等54项三级指标。在现场调研和理论分析的基础上,利用改进的层次分析法计算得到某火灾高危单位消防安全评估指标体系各指标的权重,建立了火灾高危单位消防安全评估模型,对其火灾危险性进行了评估。     

Building urban gas process safety management (UG-PSM) system: Based on root cause analysis with 160 urban gas accidents in China

Urban safety is significantly impacted by the complexity of urban gas accidents. Although China has put forward the goal of “zero fatalities in accidents” for urban gas, unfortunately, the root causes of the unsound safety culture and imperfect safety management system of urban gas enterprises remain unresolved. Therefore, statistical analysis and 24Model analysis of 160 urban gas accidents in mainland China were performed to investigate the proximate causes of the accidents and the current situation of urban gas safety. The CCPS's risk-based process safety elements were used to identify potential deficiencies in the current urban gas process safety management (UG-PSM). During the analysis, it was observed that insufficient implementation of concealed danger investigation and rectification accounted for 76%, which is the primary proximate cause of urban gas accidents. Stakeholder outreach is the most under-represented competency in urban gas safety management. Based on the results, a novel framework for the UG-PSM system was constructed, and the theory of urban gas safety management was further improved. Furthermore, we evaluated the matching degree between UG-PSM elements and the existing measures of the government, urban gas associations, third-party organizations, and urban gas enterprises. Finally, we analyzed the feasibility, challenges, and development directions of the UG-PSM system. This study establishes a foundation for researchers and practitioners in the future research and practice of urban gas process safety and provides theoretical guidance for preventing high-incidence accidents of urban gas in China.