Multi-hazard failure assessment of atmospheric storage tanks during hurricanes

Hurricane as one of the most destructive natural hazards can make a devastating impact on the industrial equipment, especially atmospheric storage tanks, leading to the release of stored chemicals and disastrous safety and environmental issues. These catastrophic consequences are caused not only by strong winds but also by the torrential rainfall and inundating floods. The objective of this study is to present a risk-based methodology for assessing and reducing the vulnerability of atmospheric storage tanks to hurricanes. Considering the shell buckling, flotation, sliding, and roof sinking as dominant failure modes of atmospheric storage tanks during hurricanes, Bayesian network (BN) has been employed to combine the failure modes while considering their conditional dependencies. The probability updating feature of the developed BN was employed to indicate that the flood is the most critical hazard during hurricanes while the impact of wind and rainfall cannot be neglected. Extending the developed BN to an influence diagram, the cost-benefit filling of storage tanks with water prior to the advent of hurricanes was shown as a viable measure for reducing the damage probability. The results show that the proposed methodology can be used as an effective decision support tool for assessing and reducing the vulnerability of atmospheric storage tanks to natural hazards.     

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.     

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.     

Incident tree model and incident tree analysis method for quantified risk assessment: An in-depth accident study in traffic operation

Fault tree analysis (FTA) is a logically structured process that can help identify potential causes of system failure before the failures actually occur. However, FTA often suffers from a lack of enough probabilistic basic events to check the consistency of the logic relationship among all events through linkage with gates. Sometimes, even logic relationship among all events is difficult to determine, and failures in system operation may have been experienced rarely or not at all. In order to address the limitations, this paper proposes a novel incident tree methodology that characterizes the information flow in a system instead of logical relationship, and the amount of information of a fuzzy incident instead of probability of an event. From probability statistics to fuzzy information quantities of basic incidents and accident, we propose an incident tree model and incident tree analysis (ITA) method for identification of uncertain, random, complex, possible and variable characteristic of accident occurrence in quantified risk assessment. In our research, a much detailed example for demonstrating how to create an incident tree model has been conducted by an in-depth analysis of traffic accident causation. The case study of vehicle-leaving-roadway accident with ITA illustrates that the proposed methodology may not only capture the essential information transformations of accident that occur in system operation, but also determine the various combinations of hardware faults, software failures and human errors that could result in the occurrence of specified undesired incident at the system level even accident.     

基于RPN-BN的平台-隔水管系统遇内波风险分析

为保障内孤立波作用下的深水半潜式钻井平台-隔水管系统的安全，同时解决海洋平台系统设备失效数据的缺失问题，提出1种风险优先系数（RPN）与贝叶斯（BN）结合的定量风险分析方法。首先，基于故障树和安全屏障方法，建立平台-隔水管系统Bow tie模型和贝叶斯风险演化模型；其次，根据贝叶斯推断和风险优先系数中的事故发生频度估计，得到平台-隔水管系统失效事故的发生概率；最后，通过贝叶斯网络的逆向推理能力辨识内孤立波作用下引起平台-隔水管系统失效的主要风险节点，实现对平台-隔水管系统失效事故的定量风险分析。结果表明:RPN-BN法可应用于平台-隔水管系统遇内波的定量风险分析；加强对平台漂移量的控制，提高动力定位系统控制设备的可靠性可有效抵御内波对系统造成的影响。     

基于BN的FTA在通用航空风险评价中的应用

针对事故树分析法(FTA)在风险评价中的局限性,采用以事故树为基础建立的贝叶斯网络(BN)风险模型,对通用航空中的两机空中相撞事故进行分析和推理,对事故模型进行改进和修正时,注重基事件的多态性和事件间的逻辑合理性。根据贝叶斯推理得出的数据,找到了事故的主要致因。结果表明,基于BN的FTA既能向前预测顶事件的发生概率,又能向后诊断基本事件的后验概率,可以更好地对通用航空风险进行评价。     

Methodology for computer aided fuzzy fault tree analysis

Probabilistic risk assessment (PRA) is a comprehensive, structured and logical analysis method aimed at identifying and assessing risks of complex process systems. PRA uses fault tree analysis (FTA) as a tool to identify basic causes leading to an undesired event, to represent logical dependency of these basic causes in leading to the event, and finally to calculate the probability of occurrence of this event.To conduct a quantitative fault tree analysis, one needs a fault tree along with failure data of the basic events (components). Sometimes it is difficult to have an exact estimation of the failure rate of individual components or the probability of occurrence of undesired events due to a lack of sufficient data. Further, due to imprecision in basic failure data, the overall result may be questionable. To avoid such conditions, a fuzzy approach may be used with the FTA technique. This reduces the ambiguity and imprecision arising out of subjectivity of the data.This paper presents a methodology for a fuzzy based computer-aided fault tree analysis tool. The methodology is developed using a systematic approach of fault tree development, minimal cut sets determination and probability analysis. Further, it uses static and dynamic structuring and modeling, fuzzy based probability analysis and sensitivity analysis.This paper also illustrates with a case study the use of a fuzzy weighted index and cutsets importance measure in sensitivity analysis (for system probabilistic risk analysis) and design modification.     

Dynamic risk assessment using failure assessment and Bayesian theory

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit.     

Probabilistic analysis of aluminium production explosion accidents based on a fuzzy Bayesian network

Explosion accidents of molten aluminium in contact with water during aluminium production often occur and may cause injury and death. In this paper, a fuzzy Bayesian network (BN) was employed to probabilistically analyse the explosion accident of molten aluminium in contact with water. A fault tree-Bayesian network (FT-BN) model was established in the cause-effect analysis of the explosion accident, including three processes: electrolysis, molten aluminium transportation and aluminium casting. Fifty-three nodes were proposed in the model to represent the evolution process of the explosion accident from failure causes to consequences. Furthermore, the occurrence probabilities of basic events (BEs) were determined by expert judgement with weighted treatments based on fuzzy theory. By giving certain occurrence probabilities of each BE, the probability of an explosion accident was estimated. Subsequently, importance measures were assessed for each BE, which could reflect the impact on the occurrence of the top event (TE), and the final ranks were provided. The results indicate that using wet ladles and tools, water on the ground, breakage of the tap hole, damage to the casting mould, and leakage of circulating water are five main problems that cause explosion accidents. Safety advice was provided based on the analysis results. This study can help decision makers improve the safety management of aluminium production.     

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。     

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.     

Human error risk analysis in offshore emergencies

Human factors play an important role in the completion of emergency procedures. Human factors analysis is rooted in the concept that humans make errors, and the frequency and consequences of these errors are related to work environment, work culture, and procedures. This can be accounted for in the design of equipment, structures, processes, and procedures. As stress increases, the likelihood of human error also increases. Offshore installations are among the harshest and most stressful work environments in the world. The consequences of human error in an offshore emergency can be severe.A method has been developed to evaluate the risk of human error during offshore emergency musters. Obtaining empirical data was a difficult process, and often little information could be drawn from it. This was especially an issue in determining the consequences of failure to complete muster steps. Based on consequences from past incidents in the offshore industry and probabilities of human error, the level of risk and its tolerability are determined. Using the ARAMIS (accidental risk assessment methodology for industries) approach to safety barrier analysis, a protocol for choosing and evaluating safety measures to reduce and re-assess the risk was developed. The method is assessed using a case study, the Ocean Odyssey incident, to determine its effectiveness. The results of the methodology agree with the analysis of survivor experiences of the Ocean Odyssey incident.     

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.     

Safety study of an LNG regasification plant using an FMECA and HAZOP integrated methodology

A safety analysis was performed to determine possible accidental events in the storage system used in the liquefied natural gas regasification plant using the integrated application of failure modes, effects and criticality analysis (FMECA) and hazard and operability analysis (HAZOP) methodologies. The goal of the FMECA technique is the estimation of component failure modes and their major effects, whereas HAZOP is a structured and systematic technique that provides an identification of the hazards and the operability problems using logical sequences of cause-deviation-consequence of process parameters. The proposed FMECA and HAZOP integrated analysis (FHIA) has been designed as a tool for the development of specific criteria for reliability and risk data organisation and to gain more recommendations than those typically provided by the application of a single methodology. This approach has been applied to the risk analysis of the LNG storage systems under construction in Porto Empedocle, Italy. The results showed that FHIA is a useful technique to better and more consistently identify the potential sources of human errors, causal factors in faults, multiple or common cause failures and correlation of cause-consequence of hazards during the various steps of the process.     

基于贝叶斯网络的机械安全性评估模型研究

重点阐述了基于贝叶斯网络的机械安全性评估模型的建立过程,应用贝叶斯网络建立了开式压力机安全性评估模型,对冲手事故模型中的共因失效节点进行了说明,并最终计算得出开式压力机冲手事故的发生概率.计算结果分析表明:对于系统基本事件之间有共因和相关关系的系统,BN计算结果使得事故后果概率明显增大,设计者和管理者不能忽略共因和相关关系对系统风险的影响.     

A new look at release event frequencies

Within the context of a quantitative risk analysis (QRA), the two main constituents used to describe petrochemical risks are, and have always been, consequence and probability. The consequences of hazardous material accidents are easy to apprehend – if a hazard is realized it can injure people or cause fatalities, damage equipment or other assets, or cause environmental damage. Frequencies for these consequences, on the other hand, are not as easy to understand. Process safety professionals develop event frequencies by evaluating historical data and calculating incident rates, which represent, in the QRA context, how often a release of a hazardous material has occurred. Incident rates are further modified by probabilities for various hole sizes, release orientations, weather conditions, ignition timing, and other factors, to arrive at unique event probabilities that are applied in the QRA. This paper describes the development of incident rates from historical database information for various equipment types, as well as defining a methodology for assigning hole size probabilities from the same data, such that a hole size distribution can be assigned within each QRA study. The combination of total incident rates and a hole size distribution relationship can then serve as a foundation within the frequency side of many QRA studies.     

基于状态Petri网的矿区生态环境脆弱度动态评价方法

采用状态Petri网模型对矿区生态环境脆弱度进行动态评价,给出了状态Petri网中累积值、加权值、不变值和虚拟值4种不同状态值的计算及表示方法,提出了基于状态Petri网的矿区生态环境脆弱度计算方法。库所的累积值用于计算采矿过程中环境指标的总变化量。加权值主要用于脆弱度的计算。具有不变值的库所在相应的变迁被激发前后状态值均保持不变。没有实际意义的虚拟值在状态Petri网中仅起到逻辑或过渡作用。网结构描述了矿区生态环境脆弱度的组成结构,网系统的运行反映了生态环境脆弱化的状态变化过程,并且将脆弱度的计算融入了状态Petri网的运行中。在案例研究中,绘制了4种修复方案下脆弱度的变化情况曲线,表明该评价方法可以对矿区生态环境脆弱度进行动态评价。状态Petri网模型能将矿区生态环境脆弱化过程,以及人工修复和自然修复过程直观形象地展现出来,为预测矿区生态环境脆弱度的变化情况及制定相应的修复方案提供参考。该模型适用于矿山长期开采对生态环境造成破坏过程的建模及计算,而且对矿山的开采过程也不需要作特别的假设限制。     

Leadership self‐efficacy and managers' motivation for leading change

This study develops and tests a leadership model that focuses on managers' motivation for attempting the leadership of change. The construct of leadership self‐efficacy (LSE) is defined, and a measure comprising three dimensions (direction‐setting, gaining followers' commitment, and overcoming obstacles to change) is developed. Based on Bandura's (1986) social cognitive theory, the primary hypothesis is that high LSE managers will be seen by direct reports as engaging in more leadership attempts. Relationships are also proposed between LSE and several factors that are expected to influence this confidence judgment. Managers' organizational commitment and crisis perceptions are modelled as potential moderators of the relationship between LSE and leadership attempts. The model was tested through surveys distributed to managers (n = 150) and their direct reports (n = 415) in a real estate management company and an industrial chemicals firm. Positive relationships (p < 0.05) were found between the first two dimensions of LSE and managers' leadership attempts. An interaction effect involving organizational commitment was discovered for the LSE/overcoming obstacles dimension (p < 0.05). Several positive relationships were found between LSE dimensions and proposed antecedents, including self‐esteem (p < 0.05), subordinates' performance abilities (p < 0.05), and managers' job autonomy (p < 0.05). Copyright © 2002 John Wiley & Sons, Ltd.     

Dynamic probability assessment of urban natural gas pipeline accidents considering integrated external activities

Urban gas pipelines usually have high structural vulnerability due to long service time. The locations across urban areas with high population density make the gas pipelines easily exposed to external activities. Recently, urban pipelines may also have been the target of terrorist attacks. Nevertheless, the intentional damage, i.e. terrorist attack, was seldom considered in previous risk analysis of urban gas pipelines. This work presents a dynamic risk analysis of external activities to urban gas pipelines, which integrates unintentional and intentional damage to pipelines in a unified framework. A Bayesian network mapping from the Bow-tie model is used to represent the evolution process of pipeline accidents initiating from intentional and unintentional hazards. The probabilities of basic events and safety barriers are estimated by adopting the Fuzzy set theory and hierarchical Bayesian analysis (HBA). The developed model enables assessment of the dynamic probabilities of consequences and identifies the most credible contributing factors to the risk, given observed evidence. It also captures both data and model uncertainties. Eventually, an industrial case is presented to illustrate the applicability and effectiveness of the developed methodology. It is observed that the proposed methodology helps to more accurately conduct risk assessment and management of urban natural gas pipelines.