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.     

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.     

Modelling of BP Texas City refinery accident using dynamic risk assessment approach

Process industries involve handling of hazardous substances which on release may potentially cause catastrophic consequences in terms of assets lost, human fatalities or injuries and loss of public confidence of the company. In spite of using endless end-of-the-pipe safety systems, tragic accidents such as BP Texas City refinery still occur. One of the main reasons of such rare but catastrophic events is lack of effective monitoring and modelling approaches that provide early warnings and help to prevent such event. To develop a predictive model one has to rely on past occurrence data, as such events are rare, enough data are usually not available to better understand and model such behavior. In such situations, it is advisable to use near misses and incident data to predict system performance and estimate accident likelihood. This paper is an attempt to demonstrate testing and validation of one such approach, dynamic risk assessment, using data from the BP Texas City refinery incident.Dynamic risk assessment is a novel approach which integrates Bayesian failure updating mechanism with the consequence assessment. The implementation of this methodology to the BP Texas City incident proves that the approach has the ability to learn from near misses, incident, past accidents and predict event occurrence likelihood in the next time interval.     

Uncertainty reduction for improved mishap probability prediction: Application to level control of distillation unit

At all levels, the understanding of uncertainty associated with risk of major chemical industrial hazards should be enhanced. In this study, a quantitative risk assessment (QRA) was performed for a knockout drum in the distillation unit of a refinery process and then probabilistic uncertainty analysis was applied for this QRA. A fault tree was developed to analyze the probability distribution of flammable liquid released from the overfilling of a knockout drum. Bayesian theory was used to update failure rates of the equipment so that generic information from databases and plant equipment real life data are combined to gain all available knowledge on component reliability. Using Monte Carlo simulation, the distribution of top event probability was obtained to characterize the uncertainty of the result. It was found that the uncertainty of basic event probabilities has a significant impact on the top event probability distribution. The top event probability prediction uncertainty profile showed that the risk estimation is improved by reducing uncertainty through Bayesian updating on the basic event probability distributions. The whole distribution of top event probability replaces point value in a risk matrix to guide decisions employing all of the available information rather than only point mean values as in the conventional approach. The resulting uncertainty guides where more information or uncertainty reduction is needed to avoid overlap with intolerable risk levels.     

Scenario deduction of Natech accident based on dynamic Bayesian network: A case study of landslide accident in a liquor storage tank area in Guizhou Province,China

Industrial technical accidents caused by natural disasters are defined as Natech accidents, such as earthquakes and landslides, which can cause tremendous damage to industrial storage tanks, and lead to accidental leakage and even serious fire and explosion accidents. In this study, a landslide-induced storage tank accident model under earthquake disasters was proposed, and the relationship between landslide mass impact and target impact resistance was taken into account. Also, tank failure and the formation of the pool fire were considered to be the consequences of the Natech accident. Through scenario deduction, the dynamic process of landslide Natech was transformed qualitatively into a disaster chain network diagram composed of a scenario state, a disaster-causing factor and emergency management. The Bayesian network was used to learn and deduce the parameters of the network diagram, and in this process, the prior probability and conditional probability of nodes were obtained primarily by Monte Carlo simulation, and by an improved expert scoring method based on the fuzzy set theory. Through visualization software, the sensitivity analysis of landslide Natech was achieved. Finally, a case study of a liquor storage tank area in Guizhou Province, China was carried out, and the results show that a large amount of hazardous material leakage caused by buckling is key to the formation of pool fire accidents, and several prevention measures for earthquake-induced landslide Natech was proposed according to the sensitivity analysis.     

基于动态贝叶斯网络的石化装置火灾事故动态风险分析

为揭示石油炼化装置事故风险动态特性和事故情景演变路径,在对石化装置进行风险因素分析的基础上构建石化装置火灾事故故障树,基于贝叶斯网络非常规突发事故的演变过程,构建情景演变下的动态贝叶斯网络模型,在综合考虑应急措施的基础上,利用MATLAB软件和联合概率公式计算出各种事故场景的状态概率.以丙烯精馏装置火灾事故为例,结果表...     

储气库注采管柱螺纹失效动态风险评估*

为解决储气库注采管柱螺纹失效问题,识别注采管柱螺纹失效致因与后果,基于蝴蝶结和贝叶斯网络方法构建注采管柱螺纹动态失效风险分析模型,采用模糊集理论计算模型变量先验概率,并评估注采管柱失效后果概率,从而推断注采管柱螺纹失效关键致因因素;引入先兆数据,评估注采管柱螺纹动态失效风险态势。结果表明:气体中携带固体颗粒、上螺纹速度过快、注采温度高、地层断裂等13个因素对螺纹失效风险影响较大;螺纹失效概率逐渐增大,螺纹失效后果也越来越严重,需要监控螺纹失效关键致因以降低螺纹失效的风险。     

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.     

Identification, analysis and dissemination of information on near misses: A case study in the construction industry

Near misses are well-known for providing a major source of useful information for safety management. They are more frequent events than accidents and their causes may potentially result in an accident under slightly different circumstances. Despite the importance of this type of feedback, there is little knowledge on the characteristics of near misses, and on the use of this information in safety management. This article proposes guidelines for identifying, analyzing and disseminating information on near misses in construction sites. In particular, it is proposed that near misses be analyzed based on four categories: (a) whether or not it was possible to track down the event; (b) the nature of each event, in terms of its physical features (e.g. falling objects); (c) whether they provided positive or negative feedback for the safety management system; and (d) risk, based on the probability and severity associated with each event. The guidelines were devised and tested while a safety management system was being developed in a healthcare building project. The monitoring of near misses was part of a safety performance measurement system. Among the main results, a dramatic increase in both the number and quality of reports stands out after the workforce was systematically encouraged to report. While in the first 4 months of the study – when the workforce was not encouraged to report – there were just 12 reports, during the subsequent 4 months – when the workforce was so encouraged – there were 110 reports, all of them being analyzed based on the four analytical categories proposed.     

An extended HAZOP analysis approach with dynamic fault tree

An extended hazard and operability (HAZOP) analysis approach with dynamic fault tree is proposed to identify potential hazards in chemical plants. First, the conventional HAZOP analysis is used to identify the possible fault causes and consequences of abnormal conditions, which are called deviations. Based on HAZOP analysis results, hazard scenario models are built to explicitly represent the propagation pathway of faults. With the quantitative analysis requirements of HAZOP analysis and the time-dependent behavior of real failure events considered, the dynamic fault tree (DFT) analysis approach is then introduced to extend HAZOP analysis. To simplify the quantitative calculation, the DFT model is solved with modularization approach in which a binary decision diagram (BDD) and Markov chain approach are applied to solve static and dynamic subtrees, respectively. Subsequently, the occurrence probability of the top event and the probability importance of each basic event with respect to the top event are determined. Finally, a case study is performed to verify the effectiveness of the approach. Results indicate that compared with the conventional HAZOP approach, the proposed approach does not only identify effectively possible fault root causes but also quantitatively determines occurrence probability of the top event and the most likely fault causes. The approach can provide a reliable basis to improve process safety.     

基于HCNAM-BN的化工过程爆炸事故致因链分析

为了从源头上预防化工过程爆炸事故,依据风险耦合理论,探讨了各风险因子非线性耦合演化为爆炸事故的机理,构建了层次耦合网络分析模型（HCNAM）;从多因素风险耦合角度分析了国内外44起典型化工过程爆炸事故,统计了各风险因子之间的耦合概率并进行了耦合致因重要度分级;采用耦合概率与二态分布相结合的条件概率分布,将层次耦合网络分析模型转化为贝叶斯网络,并对氯乙烯单体槽爆炸性混合气体爆炸事故进行了应用研究。结果表明:91种双因子耦合风险状态中,47种呈现弱耦合致因特性;7种因子双耦合形成风险的概率较大;基于HCNAM-BN模型分析事故,可有效辨识事故最可能致因因素,获取各事故致因链的发生概率并确定事故网络关键节点。     

基于贝叶斯网络的化工园区地震次生灾害情景分析

为了研究化工园区内发生地震灾害后的事故演化过程,利用事故链模型对地震次生灾害演化过程进行分析,并将其转化为贝叶斯网络,确定各节点的变量与状态取值范围;通过相关文献及专家经验判断分析,获得贝叶斯网络中各节点的条件概率;依据贝叶斯网络的推理策略,比较不同地震烈度下,各化工事故发生的后验概率值,并探讨应急救援的及时性对地震次生灾害发生概率的影响,从关键要素遏制化工事故的发生,采取针对性的应对措施,尽可能的降低化工事故,有效的降低人员伤亡及财产损失。     

Operational risk assessment: A case of the Bhopal disaster

Accidental releases of hazardous chemicals from process facilities can cause catastrophic consequences. The Bhopal disaster resulting from a combination of inherently unsafe designs and poorly managed operations is a well-known case. Effective risk modeling approaches that provide early warnings are helpful to prevent and control such rare but catastrophic events. Probability estimation of these events is a constant challenge due to the scarcity of directly relevant data. Therefore, precursor-based methods that adopt the Bayesian theorem to update prior judgments on event probabilities using empirical data have been proposed. The updated probabilities are then integrated with consequences of varying severity to produce the risk profile.This paper proposes an operational risk assessment framework, in which a precursor-based Bayesian network approach is used for probability estimation, and loss functions are applied for consequence assessment. The estimated risk profile can be updated continuously given real-time operational data. As process facilities operate, this method integrates a failure-updating mechanism with potential consequences to generate a real-time operational risk profile. The real time risk profile is valuable in activating accident prevention and control strategies. The approach is applied to the Bhopal accident to demonstrate its applicability and effectiveness.     

基于飞行剖面的航空器试飞活动空管风险研究

为明确空中交通管理风险对航空器适航试飞活动不安全事件的影响,首先,依据航空器试飞科目绘制相应飞行剖面;其次,基于试飞活动飞行剖面,分析管制单位试飞保障流程,提取管制运行风险对试飞活动的影响因素;采用事故树分析法(FTA)分析事故发展过程,将事件和逻辑关系映射至贝叶斯网络(BN),依据国内外民航空管不安全事件分类统计结果...     

The safety barometer: How safe is my plant today? Is instantaneously measuring safety level utopia or realizable?

During the last decade, serious accidents have continued to occur in the process industry. Apparently the scenarios of various undesired events leading to those accidents are still not sufficiently controlled. The key question is how potentially hazardous situations develop, what processes form the basis for this development, and how to control them? Safety level is not static but depends on many risk factors that change in presence and intensity over location and time. Safety level is dependent not only on technical process parameters that have immediate effects on the ‘frequency’ or probability of catastrophic consequences, but also depends on equipment integrity degradation, operational and management quality, attitudes, and cultural processes which may change over a prolonged time. The time and human interaction aspects make dynamic risk assessment complex. This paper will outline a conceptual approach using in addition to the regular process parameter signals received, also weak and slowly changing signals from various safety indicators, enabling to keep track of the risk factors. In theory this could lead to obtaining an instantaneous safety level ‘measure’ making possible forecast alarming for an imminent event to occur. Such concept could be regarded as a ‘writing’ safety barometer, or barograph. However, there are quite a number of problems to be solved which in the paper will be discussed.     

Risk assessment of rare events

Rare events often result in large impacts and are hard to predict. Risk analysis of such events is a challenging task because there are few directly relevant data to form a basis for probabilistic risk assessment. Due to the scarcity of data, the probability estimation of a rare event often uses precursor data. Precursor-based methods have been widely used in probability estimation of rare events. However, few attempts have been made to estimate consequences of rare events using their precursors. This paper proposes a holistic precursor-based risk assessment framework for rare events. The Hierarchical Bayesian Approach (HBA) using hyper-priors to represent prior parameters is applied to probability estimation in the proposed framework. Accident precursor data are utilized from an information theory perspective to seek the most informative precursor upon which the consequence of a rare event is estimated. Combining the estimated probability and consequence gives a reasonable assessment of risk. The assessed risk is updated as new information becomes available to produce a dynamic risk profile. The applicability of the methodology is tested through a case study of an offshore blowout accident. The proposed framework provides a rational way to develop the dynamic risk profile of a rare event for its prevention and control.     

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.     

Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Among the various techniques used for safety analysis of process systems, bow-tie (BT) analysis is becoming a popular technique as it represents an accident scenario from causes to effects. However, the BT application in the dynamic safety analysis is limited due to the static nature of its components, i.e. fault tree and event tree. It is therefore difficult in BT to take accident precursors into account to update the probability of events and the consequent risk. Also, BT is unable to represent conditional dependency. Event dependency is common among primary events and safety barriers. The current paper illustrates how Bayesian network (BN) helps to overcome these limitations. It has also been shown that BN can be used in dynamic safety analysis of a wide range of accident scenarios due to its flexible structure. This paper also introduces the application of probability adapting in dynamic safety analysis rather than probability updating. A case study from the U.S. Chemical Safety Board has been used to illustrate the application of both BT and BN techniques, with a comparison of the results from each technique.     

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.     

The importance of near miss reporting to further improve safety performance

Dealing with accidents implies that such events have in common the potential to affect people and the environment in a significant way. Therefore, all parties involved in industrial risk management processes, i.e. industry, regulatory authorities, public as well as scientific and technical institutions, are well aware of the importance of considering and analysing such type of events for the purposes of accident prevention. Also, the methods of Quantitative Risk Assessment (QRA) have large experience in numerically expressing the various degrees of risk related to accidents. On the other hand, the topic of including `near misses' (i.e. any event which could have escalated to an accident) in safety management systems with the aim to prevent major accidents and the occurrence of similar events in the future is relatively new. Although its importance has more and more been recognised in the last few years, it is not yet a commonly accepted fact that near miss reporting and investigation of near misses should be an integral part of a safety management system in industrial facilities. In the European Council's new `Seveso II Directive' 96/82/EC, there is—in addition to the mandatory requirements of major accident reporting—an explicit recommendation to report near misses to the Commission's Major Accident Reporting System (MARS) on a voluntary basis. In this paper, examples of current experience in the chemical industry with the collection and analysis of data on near misses are presented and discussed with regard to industry-wide conclusions. In addition to this more qualitative discussion, quantitative arguments are put forward regarding the impact of near misses on risk estimates derived from QRA.