Risk assessment of chemical process considering dynamic probability of near misses based on Bayesian theory and event tree analysis

With the development of modern automatic control systems, chemical accidents are of low frequency in most chemical plants, but once an accident happens, it often causes serious consequences. Near-misses are the precursor of accidents. As the process progresses, near misses caused by abnormal fluctuation of process variables may eventually lead to accidents. However, variables that may lead to serious consequences in the production process cannot update the risk in the life cycle of the process by traditional risk assessment methods, which do not pay enough attention to the near misses. Therefore, this paper proposed a new method based on Bayesian theory to dynamically update the probability of key variables associated with process failure risk and obtain the risk change of the near-misses. This article outlines the proposed approach and uses a chemical process of styrene production to demonstrate the application. In this chemical process, the key variables include flow rate, liquid level, pressure and temperature. In order to study the dynamic risk of the chemical process with consideration of near misses, according to the accumulated data of process variables, firstly the abnormal probability of the variables and the failure rate of safety systems associated with the variables were updated with time based on Bayesian theory. On the basis of the dynamic probability of key process variables, an event tree of possible consequences caused by variable anomalies was established. From the logical relationship of the event tree, the probability of different consequences can be obtained. The results show that the proposed risk assessment method based on Bayesian theory can overcome the shortcomings of traditional analysis methods. It shows the dynamic characteristics of the probability of different near misses, and achieves the dynamic risk analysis of chemical process accidents.     

A reliability evaluation method based on the weakest failure modes for side-by-side offloading mooring system of FPSO

Offshore structures are complex systems, and numerous failure modes must be taken into consideration when reliability analysis in different loads and environment conditions are conducted on them. It is difficult to obtain structural system reliability with respect to complicated systems with numerous failure modes and dependency consideration among them. This paper applies the combination of the weakest failure modes theory with structural reliability theory to conduct reliability analysis on of side-by-side offloading mooring system of FPSO. Firstly, the numerical simulation of the system in different conditions is addressed to acquire the statistical data of time-history stress of components including hawsers, fenders and yoke, based on which, the reliability indexes of all the failure modes and correlation coefficient matrix are derived. Then the weakest failure modes, i.e. the representative failure modes that have significant impact on the system, are located through Probability Network Evaluation Technique. The probability of structural system is estimated through the weakest failure modes by considering the system as series. The analysis results indicate that two environment conditions (0°&0°&0° and 0°&30°&45° in combinations of wave, wind and current) are relative dangerous, which is in good correspondence with the practical expertise. The method is verified to be an effective and convenient evaluation approach for structural reliability analysis in terms of complex systems. It is beneficial for the identification of structure indicators from the weakest failure mode group and conduct optimum of structural system configurations in the design stage.     

Planning protection measures against runaway reactions using criticality classes

A systematic approach to the assessment of thermal risks linked with the performance of exothermal reactions at industrial scale was proposed a long time ago. The approach consisted of a runaway scenario starting from a cooling failure and a classification of these scenarios into criticality classes. In the mean time these tools became quite popular and many chemical companies use them. Recently, the international standard IEC 61511 required the use of protection systems with reliability depending on the risk level. Since the criticality classes were developed as a tool for the choice of risk reducing measures as a function of the criticality, it seems obvious that the criticality classes may be used in the context of the standard IEC 61511, which provides a relation between the risk level and the reliability of protection systems.Firstly, the runaway scenario and the criticality classes will be shortly described. Secondly, the assessment criteria for severity and probability of occurrence of a runaway scenario will be described together with the required data and their interpretation in terms of risk. Thirdly, the assessment procedure is exemplified for the different criticality classes. Finally, the design of protection measures against runaway and the required IPL and SIL are based on the risk assessment obtained from the criticality classes. This approach allows minimising the required data set for the safety assessment and for the definition of the protection system designed in order to avoid the development of the runaway.     

机械系统的风险概率与经济性安全评价方法研究与应用

针对机械事故频发,为预防和控制事故,依据安全评价发展方向,结合安全系统工程、模糊可靠性理论、人因可靠性技术和安全经济学,从人-机-环3方面分析风险评价因素,建立符合客观实际的风险评价模型,提出包括人为因素、机械设备和环境在内的基本事件失效概率估算方法;以安全经济学的角度将事故严重度量化为风险经济损失,根据每个风险因素的风险概率和风险经济损失来选择风险评价项目,通过检查评价项目状况,最终得出系统风险度,从而形成关于机械系统的定量风险概率与经济性安全评价方法。并给出起重机大车运行机构的工程示例,验证该方法的正确性和可行性。     

A new approach to quantitative assessment of reliability of passive systems

The objective of this paper is to show how probabilistic reliability can be assessed for complex systems in the absence of statistical data on their operating experience, based on performance evaluation of the dominant underlying physical processes. The approach is to distinguish between functional and performance probabilities when dealing with the quantification of the overall probability of a system to perform a given function in a given period of time (reliability). In the case of systems where sufficient statistical operating experience data are available, one can focus the quantitative evaluation entirely on the assessment of the functional probability for a given active item (e.g. a pump) by assuming that the specification, layout, construction and installation is such that the item is providing the assigned performance, e.g. in the form of generating the required flow rate. This is how traditional probabilistic safety assessments (PSAs) focus the reliability analysis for the various safety features on the calculation of values for the availability per demand. In contrast, for various systems relevant in advanced technical applications, such as passive safety features in innovative reactor designs, it is essential to evaluate both functional and performance probabilities explicitly and combine the two probabilities later on. This is of course due to the strong reliance of passive safety systems on inherent physical principles. In practice, this means that, for example, in case of a passive cooling system based on natural circulation of a given medium, one has to evaluate and to assess the probability to have a medium condition and a flow rate such that a cladding temperature, represented by a probability distribution, can be hold at a required level. A practical example of this method is given for the case of the reliability assessment of a residual passive heat removal system. General conclusions are drawn regarding reliability estimation of complex, interconnected systems in the absence of statistical performance data, such as for infrastructures.     

基于RBF神经网络的火灾报警系统失效概率预测

报警系统失效主要包括漏报、误报,对系统进行失效概率预测,可以帮助判断设备质量优劣,评估系统效能。利用Matlab软件编程,通过神经网络预测失效概率。根据不同场所正在使用的火灾报警器的失效数据作为原始数据,归纳总结失效原因,建立事故树,结合专家打分法与模糊理论得到网络的输入值与输出值。通过网络训练,得到可以对系统失效概率进行预测的RBF神经网络,测算效率大幅提高。以70组不同品牌、用途的火灾报警系统作为算例,通过训练数据,最终达到输入底事件发生概率可直接输出顶事件发生概率的目的。结果表明,RBF神经网络相较于BP网络与事故树算得的失效概率具有更高的拟合度,RBF神经网络模型在进行系统失效概率预测时具有可靠性。     

极端海况下海洋石油结构的风险评估

根据中国海上石油工业缺乏独立的风险评价体系的现状 ,笔者对极端海况进行了环境荷载的联合概率分析 ,利用随机模拟技术 ,求解结构的失效概率 ,提出结构失效分析的新方法 ,并利用DNV提供的历史数据 ,对结构失效的后果做了适当分析。以埕北 12 C井组平台作为实例 ,对平台甲板高程 (AirGap)失效分析进行了实例计算。结果表明 ,使用多维联合极值分布理论及相应的求解方法 ,是离岸工程结构物风险评估的重要手段。     

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 Bayesian networks for reliability evaluation of subsea wellhead connector during service life based on Monte Carlo method

The subsea wellhead connector is a critical connection component between subsea Christmas tree and subsea wellhead for preventing the leakage of oil and gas in the subsea production system. Excited by cyclical loadings due to environmental forces and the other support forces, the subsea wellhead connector is prone to the failure, which could lead to the loss of subsea tree or wellhead integrity and even catastrophic accidents. With the Monte Carlo simulation method, this paper presents a reliability analysis approach based on dynamic Bayesian Networks, aiming to assess the failure probability of the subsea wellhead connector during service life. Take the driving ring component of the subsea wellhead connector as an example to demonstrate the reasonability of the proposed model. The generation data is processed by the transform between the numerical value and the state variable. Based on the stress-strength interference theory, the structure reliability of the driving ring with 96.26% is achieved by the proposed model with the consideration the aging of the material strength and the most influential factors are figured out. Meanwhile, the corresponding control measures are proposed effectively reduce the failure risk of the subsea wellhead connector during service life.     

Decision analysis on fire safety design based on evaluating building fire risk to life

This study presents a framework of decision analysis on fire safety design alternatives based on evaluating building fire risk to life. A probabilistic risk assessment method for occupant life is presented with consideration of some uncertainties of evacuation process and fire development at first. For occupant evacuation time assessment, occupant pre-movement time is characterized by normal distribution. For onset time to untenable conditions assessment, its uncertainty is considered as probability distribution according to the range of design fires. Based on event tree technique, probable fire scenarios are analyzed with consideration of the effect of fire protection systems, employees extinguishing, etc. It is difficult to make a precise assessment on probability and consequence of every fire scenario, but their lower bound and upper bound can be achieved based on statistical data. Therefore, Supersoft Decision Theory [Malmnäs, P.E., 1995. Methods of Evaluation in Supersoft Decision Theory. Department of Philosophy, Stockholm University, 365 Stockholm; Johansson, H., Malmnäs, P.E., 2000. Application of supersoft decision theory in fire risk assessment. Journal of Fire Protection Engineering 14, 55–84] that allows decision maker to utilize vague statement is utilized to integrate with risk assessment to make decision on different fire safety design alternatives. To express how to make decision on different fire safety design alternatives based on evaluating building fire risk to life, two hypothetical fire safety design alternatives for a commercial building are presented.     

Development of a risk-based maintenance (RBM) strategy for a power-generating plant

The unexpected failures, the down time associated with such failures, the loss of production and, the higher maintenance costs are major problems in any process plant. Risk-based maintenance (RBM) approach helps in designing an alternative strategy to minimize the risk resulting from breakdowns or failures. Adapting a risk-based maintenance strategy is essential in developing cost-effective maintenance policies.The RBM methodology is comprised of four modules: identification of the scope, risk assessment, risk evaluation, and maintenance planning. Using this methodology, one is able to estimate risk caused by the unexpected failure as a function of the probability and the consequence of failure. Critical equipment can be identified based on the level of risk and a pre-selected acceptable level of risk. Maintenance of equipment is prioritized based on the risk, which helps in reducing the overall risk of the plant.The case study of a power-generating unit in the Holyrood thermal power generation plant is used to illustrate the methodology. Results indicate that the methodology is successful in identifying the critical equipment and in reducing the risk of resulting from the failure of the equipment. Risk reduction is achieved through the adoption of a maintenance plan which not only increases the reliability of the equipment but also reduces the cost of maintenance including the cost of failure.     

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.     

System failure probability evaluation using fault tree analysis and expert opinions in intuitionistic fuzzy environment

In quantitative fault tree analysis of a system, exact failure probability values of components are utilized to calculate the failure probability of the system. However, in many real world problems, it is problematic to get precise and sufficient failure data of system components due to insufficient or imprecise information about components, changing environment or new components. A methodology has already been developed by employing fuzzy set theory for the system reliability evaluation by utilizing qualitative failure data of system components when quantitative failure data of components are inaccessible or insufficient. This paper extends the concept of fuzzy set to intuitionistic fuzzy set and proposes a novel approach to evaluate system failure probability using intuitionistic fuzzy fault tree analysis with qualitative failure data of system components. The qualitative failure data such as expert opinions are collected as linguistic terms. These linguistic terms are then quantified by triangular intuitionistic fuzzy numbers in form of membership function and non-membership function. Additionally, a method is developed for combining the different opinions of experts. To illustrate the applicability of proposed approach, a case study of the crude oil tank fire and explosion accident is performed. The obtained results are very close to the results from pre-existing approaches which confirm that the proposed approach is a more realistic alternative for the study of system reliability in intuitionistic fuzzy environment when quantitative failure data of system components are not known. To help decision makers for improving the security execution of the crude oil tank system, importance measures including Fussell-Vesely importance and cut sets importance are also executed.     

Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station

A method is presented for analysis of reliability of complex engineering systems using information from fault tree analysis and uncertainty/imprecision of data. Fuzzy logic is a mathematical tool to model inaccuracy and uncertainty of the real world and human thinking. The method can address subjective, qualitative, and quantitative uncertainties involving risk analysis. Risk analysis with all the inherent uncertainties is a prime candidate for Fuzzy Logic application. Fuzzy logic combined with expert elicitation is employed in order to deal with vagueness of the data, to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing.The proposed model is able to quantify the fault tree of LPG refuelling facility in the absence or existence of data. This paper also illustrates the use of importance measures in sensitivity analysis. The result demonstrates that the approach is an apposite for the probabilistic reliability approach when quantitative historical failure data are unavailable. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process.     

Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context

Failure mode and effect analysis (FMEA), which aims to identify and assess potential failure modes in a system, has been widely utilized in diverse areas for improving and enhancing the performance of systems due to it is a powerful and useful risk and reliability assessment instrument. However, the conventional FMEA approach has been suffered several criticisms for it has some shortcomings, such as unable to handle ambiguous and uncertain information, neglect the relative weights of risk criteria, and without considering the psychological behaviors of decision-makers. To ameliorate these limitations, this paper aims at establishing a hybrid risk ranking model of FMEA via combing linguistic neutrosophic numbers, regret theory, and PROMETHEE (Preference ranking organization method for enrichment evaluation) approach. In the presented model, linguistic neutrosophic numbers are adopted to capture decision-makers’ evaluation regarding the failure modes on each risk criterion. A modified PROMETHEE approach based on regret theory is presented to obtain the risk priority of failure modes considering the psychological behaviors of decision-makers. Moreover, a maximizing deviation model and TOPSIS (Technique for order preference similar to ideal solution) are separately applied to derive the weights of risk criteria and decision-makers. Finally, a numerical example relating to the supercritical water gasification system is employed to implement the presented method, and the effectiveness and feasibility of the proposed model are validated by the results derived from a sensitivity and comparison analysis.     

基于子集模拟的埋地管道失效概率评估

对失效概率的评估是管道系统风险评价的核心内容,针对目前对埋地管道失效概率问题研究的不足,采用子集模拟方法（SS）建立埋地管道失效概率的定量评估模型以及参数敏感度模型,并对随机变量进行敏感分析。研究结果表明:子集模拟能利用少量样本定量计算出管道失效概率,有效弥补了管道结构可靠性模型中的不足;在管道运行中期,工作压力、腐蚀速率、管道壁厚和屈服强度是对管道失效概率影响较大的4个因素,而腐蚀因素在管道运行后期成为对系统失效概率影响最大的因素。     

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。     

基于可靠度理论的RBI修正模型研究

基于风险的检测技术包括定性RBI判断和定量RBI计算,定性判断得到风险相对的高低,风险相对较低者则不必进行定量RBI计算,风险高者再进行下一步定量RBI计算。在API581中,国外风险分析统计表明,石化企业的风险分布范围有一个所谓的"二八"现象,即"80%的风险是由20%的设备引起",中高风险以上的设备范围一般在20%左右。但我国十套装置风险分析结果发现,这个结论在我国并不适用,我国中高风险设备往往在40%左右。论文分析了API581在中国应用的局限性,引入可靠度理论计算设备失效概率,预估未来的失效概率或可靠度,可以完整地以RBI方法在得知失效后果和失效概率下,评估出此设备在整个系统中所处的地位,从而适当地分配检测、维修资源,在RBI完成后再进行风险评价进而完成风险评估过程。     

油气输送用焊接钢管可靠性评估方法

笔者研究并建立了焊接钢管焊缝漏检缺陷的概率分布模型及材料性能、输送压力的概率分布模型 ;采用失效评估图 (FAD)技术 ,引入可靠性理论 ,建立了油气输送管线用焊接钢管的可靠性评估模型和方法 ,即基于FAD技术的Monte Carlo方法 ;计算得出西气东输用焊接钢管的失效概率低于 10 -5,可靠度超过 0 .99999,达到DNVRPF10 1标准对管道可靠度的要求。     

高墩大跨连续刚构桥施工期安全评价

针对高墩大跨连续刚构桥施工期结构概率失效的特性,提出了基于失效概率的安全评价方法.以徐水沟特大桥为工程背景,通过风险评定识别出施工最不利阶段--最大双悬臂时的主要风险因素即:节段特性模量的不确定性、梁体自重的不确定性以及预应力损失的不确定性.采用大型通用有限元程序ANSYS及蒙特卡洛法,计算徐水沟特大桥在最大悬臂时的失效概率,并确定施工期的目标可靠度.依据高墩大跨连续刚构桥施工期结构概率失效的评价结果,提出相应的风险控制措施.