保护层分析中独立保护层的识别研究

为阐述保护层分析(LOPA)中独立保护层(IPL)的识别规则,以及这些规则在实际应用中要注意的问题,以生产聚氯乙烯(PVC)的间歇聚合反应为例,对8个不同的LOPA场景进行分析,给出不同场景的IPL和要求时的失效概率(PFD),以及建议增加的IPL。分析结果表明,在进行IPL的识别时,应重点确认IPL的有效性和独立性。在评估IPL有效性时,应关注具有共同元件的IPL,IPL的行动能力、人员行动有效性及IPL的PFD等。在评估独立性时,应确保IPL独立于初始事件和同一场景中的其他IPL的任何构成元件。通过分析,发现PVC工艺中安全阀(PSV)设计、安全仪表功能(SIF)设计和人员行动等IPL中存在的问题,并提出相应的建议。     

A formulation to optimize the risk reduction process based on LOPA

This paper presents a mixed integer nonlinear programming (MINLP) model to improve the computational use of the layer of protection analysis (LOPA). For a given set of independent protection layers to be implemented in a process, the proposed optimization model is solved to: a) Include costs associated with the different prevention, protection and mitigation devices, and b) Satisfy the risk level typically specified in the LOPA analysis through the occurrence probability. The underline purpose focuses on improving the analysis process and decision making to obtain the optimal solution in the safeguards selection that satisfies the requirements to be considered as IPL’s. The optimization is based on economic and risk tolerance criteria. As a first stage of this proposal, the safety instrumented system (SIS) design is optimized so that the selection of SIS components minimizes the risk and satisfies the safety integrity level (SIL) requirements. A case study is presented to validate the whole proposed approach.     

ExSys-LOPA for the chemical process industry

The chemical process industries are characterized by the use, processing, and storage of large amounts of dangerous chemical substances and/or energy. Among different missions of chemical plants there are two very important ones, which: 1. provide a safe work environment, 2. fully protect the environment. These important missions can be achieved only by design of adequate safeguards for identified process hazards. Layer of Protection Analysis (LOPA) can successfully answer this question. This technique is a simplified process of quantitative risk assessment, using the order of magnitude categories for initiating cause frequency, consequence severity, and the likelihood of failure of independent protection layers to analyze and assess the risk of particular accident scenarios. LOPA requires application of qualitative hazard evaluation methods to identify accident scenarios, including initiating causes and appropriate safeguards. This can be well fulfilled, e.g., by HAZOP Studies or What-If Analysis. However, those techniques require extensive experience, efforts by teams of experts as well as significant time commitments, especially for complex chemical process units. In order to simplify that process, this paper presents another strategy that is a combination of an expert system for accident scenario identification with subsequent application of LOPA. The concept is called ExSys-LOPA, which employs, prepared in advance, values from engineering databases for identification of loss events specific to the selected target process and subsequently a accident scenario barrier model developed as an input for LOPA. Such consistent rules for the identification of accident scenarios to be analyzed can facilitate and expedite the analysis and thereby incorporate many more scenarios and analyze those for adequacy of the safeguards. An associated computer program is under development. The proposed technique supports and extends the Layer of Protection Analysis application, especially for safety assurance assessment of risk-based determination for the process industries. A case study concerning HF alkylation plant illustrates the proposed method.     

Layer of Protection Analysis – Quantifying human performance in initiating events and independent protection layers

Layer of Protection Analysis (LOPA) is widely used within the process industries as a simplified method to address risks and determine the sufficiency of protection layers. LOPA brings a consistent approach with added objectivity and a greater degree of understanding of the scenarios and risks as compared to purely qualitative studies such as Process Hazard Analyses. LOPA can be used to address a wide range of risk issues and serves as a highly effective aid to decision making.Incorporation of human performance within LOPA is recognized as an important, though often challenging, aspect of the analysis. The human role in potential initiating events or within human independent protection layers is important throughout the process industries, and becomes even more critical for batch processing facilities and in non-routine operations. The human role is key to process safety and the control of risks, necessitating the inclusion and quantification of human actions in independent protection layers for most companies. Human activities as potential initiating events and human performance within independent protection layers are reviewed and methods for quantification outlined. An extension into Human Reliability Analysis (HRA) is provided, including methods to develop Human Error Probabilities specific to the process safety culture and operations at a given plant site.     

SIL determination: Recognising and handling high demand mode scenarios

The International Standards for Functional Safety (IEC 61508 and IEC 61511) are well recognised and have been adopted globally in many of the industrialised countries during the past 10 years or so. Conformance with these standards involves determination of the requirements for instrumented risk reduction measures, described in terms of a safety integrity level (SIL). During this period within the process sector, layer of protection analysis (LOPA) has become the most widely used approach for SIL determination. Experience has identified that there is a type of hazardous event scenario that occurs within the process sector that is not well recognised by practitioners, and is therefore not adequately handled by the standard LOPA approach. This is when the particular scenario places a high demand rate on the required safety instrumented function. This paper will describe how to recognise a high demand rate scenario. It will discuss what the standards have to say about high demand rates. It will then demonstrate how to assess this type of situation and provide a case study example to illustrate how to determine the necessary integrity level. It will conclude by explaining why it is important to treat high demand rate situations in this way and the resulting benefit of a lower but sufficient required integrity level.     

Bayesian networks make LOPA more effective,QRA more transparent and flexible,and thus safety more definable!

Quantitative risk analysis is in principle an ideal method to map one’s risks, but it has limitations due to the complexity of models, scarcity of data, remaining uncertainties, and above all because effort, cost, and time requirements are heavy. Also, software is not cheap, the calculations are not quite transparent, and the flexibility to look at various scenarios and at preventive and protective options is limited. So, the method is considered as a last resort for determination of risks. Simpler methods such as LOPA that focus on a particular scenario and assessment of protection for a defined initiating event are more popular. LOPA may however not cover the whole range of credible scenarios, and calamitous surprises may emerge.In the past few decades, Artificial Intelligence university groups, such as the Decision Systems Laboratory of the University of Pittsburgh, have developed Bayesian approaches to support decision making in situations where one has to weigh gains and costs versus risks. This paper will describe details of such an approach and will provide some examples of both discrete random variables, such as the probability values in a LOPA, and continuous distributions, which can better reflect the uncertainty in data.     

Beyond-compliance uses of HAZOP/LOPA studies

Recent years have seen a convergence of scenario-based Hazard and Operability (HAZOP) studies, Layer of Protection Analyses (LOPAs), and safety integrity level (SIL) determinations. These can all be performed using order-of-magnitude estimates for the initiating cause frequency, the effectiveness of protection layers, the severity of loss event consequences, and the inclusion of other risk-reduction factors. Conducting a HAZOP study or a HAZOP/LOPA study in this manner makes it possible to extend the study results to not only determine required SILs, but also to sum scenario risks by process unit and show the quantitative benefit of implementing risk-reduction measures. The aggregated risk can be compared to process-wide tolerable risk criteria, in addition to comparing each scenario to a risk matrix or risk magnitude. This presentation demonstrates how a true risk-based HAZOP study can be performed with little additional effort over that required for commonly performed cause-by-cause HAZOP studies, and how facility managers and engineers can then use the results when deciding on and implementing risk-reduction measures.     

A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis

Layer of protection analysis (LOPA) is a widely used semi-quantitative risk assessment method. It provides a simplified and less precise method to assess the effectiveness of protection layers and the residual risk of an incident scenario. The outcome failure frequency and consequence of that residual risk are intended to be conservative by prudently selecting input data, given that design specification and component manufacturer's data are often overly optimistic. There are many influencing factors, including design deficiencies, lack of layer independence, availability, human factors, wear by testing and maintenance shortcomings, which are not quantified and are dependent on type of process and location. This makes the risk in LOPA usually overestimated. Therefore, to make decisions for a cost-effective system, different sources and types of uncertainty in the LOPA model need to be identified and quantified. In this study, a fuzzy logic and probabilistic hybrid approach was developed to determine the mean and to quantify the uncertainty of frequency of an initiating event and the probabilities of failure on demand (PFD) of independent protection layers (IPLs). It is based on the available data and expert judgment. The method was applied to a distillation system with a capacity to distill 40 tons of flammable n-hexane. The outcome risk of the new method has been proven to be more precise compared to results from the conventional LOPA approach.     

The role of people and human factors in performing process hazard analysis and layers of protection analysis

Process hazard analysis (PHA) and Layers of Protection Analysis (LOPA) studies address human failures in operating and maintaining processes and the human factors that influence them, amongst other types of failures. People perform PHA and LOPA studies and, therefore, such studies themselves are subject to various possible human failures. Much less attention has been paid to the human factors that influence the performance of PHA and LOPA studies than human factors that influence hazard scenarios. Human failures in the performance of PHA and LOPA studies should be of significant concern to practitioners as such studies are difficult and time-consuming activities that place significant demands on participants, which increases the chance that errors will be made. Human factors such as willingness to rely on the unsubstantiated opinions of others, groupthink, underestimation of the frequencies of low-probability, high-consequence events, and allowing a false sense of accomplishment to distract from implementing study results must be recognized and addressed.This paper identifies and discusses various human factor issues that can influence the quality of PHA and LOPA studies covering preparing for, conducting, recording, documenting, and following-up on studies. Guidelines are provided to help minimize the extent to which these human factor issues may impair study quality.     

Scenario identification and evaluation for layers of protection analysis

The identification and screening of scenarios has been identified as a source of variation in Layers of Protection Analysis (LOPA). Often the experience of the analyst is a significant factor in determining what scenarios are evaluated and the worst credible consequences. This paper presents a simplified chemical process risk analysis that is effective in providing a semi-quantitative measure of consequence that may include human harm and is independent of the analyst. This process may be used in evaluation of Management of Change, inherently safer design decisions for capital projects and LOPA re-validation. Conditional and relational logic may be captured with the use of simple spreadsheets to further improve overall efficiency. For example, this method minimizes the overall time required for scenario development and re-validation relative to Hazard and Operability studies (HAZOP).The technique simplifies established models used by engineers engaged in the operation or design of a chemical manufacturing facility without special software or training. The results of this technique are realistic and may be directly compared with corporate or regulatory guidelines for risk of fatality or injury. At each step in the risk analysis process, more detailed or sophisticated methods may be used to refine the technique. Furthermore, results from any step may indicate that the hazard from a specific scenario case is not sufficient to continue with subsequent analysis steps.     

Improving PHA/LOPA by consistent consequence severity estimation

Most risk analysis methods rely on a qualitative judgment of consequence severity, regardless of the analysis rigor applied to the estimation of hazardous event frequency. Since the risk analysis is dependent on the estimated frequency and consequence severity of the hazardous event, the error associated with the consequence severity estimate directly impacts the estimated risk and ultimately the risk reduction requirements. Overstatement of the consequence severity creates excessive risk reduction requirements. Understatement results in inadequate risk reduction.Consistency in the consequence severity estimate can be substantially improved by implementing consequence estimation tools that assist PHA/LOPA team members in understanding the flammability, explosivity, or toxicity of process chemical releases. This paper provides justification for developing semi-quantitative look-up tables to support the team assessment of consequence severity. Just as the frequency and risk reduction tables have greatly improved consistency in the estimate of the hazardous event frequency, consequence severity tables can significantly increase confidence in the severity estimate.     

过程工业计算机辅助安全防护层分析技术进展

介绍当前过程工业安全防护层分析(LOPA)的基本内容,研讨LOPA方法与深层次的危险和可操作性分析方法(HAZOP)之间的关系以及计算机辅助HAZOP的研究进展。针对人工LOPA方法的缺点,开发了SDG-HAZOP软件平台,为计算机辅助LOPA平台研发创造了先决条件。应用计算机辅助LOPA方法,使防护层的设置具有更好的针对性、合理性和有效性,发挥对事故的预防和预警作用,并具有良好的发展前景。     

An assessment of health and safety management within working groups in the UK manufacturing sector

Problem: In response to the demands of competitiveness, there has been a proliferation of management delayering in order to move responsibility to those people carrying out the operations and to focus on working as a team. Teams can be managed in different ways: using supervisors, team leaders, or self-managed. The management of health and safety and other business risks is dependent on the way in which the team is managed. Method: This study has assessed, through the use of semistructured interviews, how UK manufacturing companies have addressed a range of health and safety management issues (i.e., the setting, communication, and measurement of company objectives, employee participation and empowerment, and risk assessment) in relation to different styles of group working (i.e., supervised groups, groups with team leaders, and self-managed groups). Discussion: Although the differences are not always significant, it is noticeable that within organizations using supervised work environments, there is a lack of communication of specific health and safety information, little involvement and participation in safety activities, and a greater reliance on the safety function. However, in organizations using team leaders and self-managed groups, there is evidence of greater management involvement, more open communication, and greater employee involvement in health and safety, although empowerment in its true sense was still limited in these organizations. Impact on industry: The results obtained illustrate the impact of different working groups on the management of health and safety in the UK manufacturing sector.     

HAZOP分析中LOPA的应用研究

通过分析危险与可操作性研究(HAZOP)方法的不足和保护层分析(LOPA)方法的功能,提出将LOPA融入HAZOP分析中,能进一步提高HAZOP的事故预防能力和丰富HAZOP的分析结果。介绍LOPA基本方法,阐述LOPA融入HAZOP的机理、衔接关系及分析步骤,并通过一个化工工艺流程危险性分析实例说明LOPA的作用及如何将LOPA融入HAZOP分析中。结果表明:在HAZOP分析中融入LOPA方法,能实现对现有保护措施的可靠性进行量化评估,确定其消除或降低风险的能力,从而寻求是否需要附加减少风险的安全保护措施。     

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.     

Job engagement in organizations: Fad,fashion, or folderol?

Engagement, broadly defined as involvement, satisfaction, and enthusiasm, is widely used by organizations and consultants for improving retention. However, engagement fails to meet many of the common criteria for positive organizational practice, i.e. theoretical, valid, unique, state‐like, and positive. With attention to these criteria, engagement may useful to management. Copyright © 2008 John Wiley & Sons, Ltd.     

HAZOP、LOPA和SIL方法的应用分析

通过概括介绍危险与可操作性分析(HAZOP)、保护层分析(LOPA)和安全完整性等级分析(SIL)三种方法的特点,总结三种分析方法之间的关系.LOPA分析是HAZOP分析的继续,可以解决HAZOP分析中残余风险不能定量化的不足,是对HAZOP分析结果的丰富和补充;SIL分析则在LOPA分析的基础上,进一步对需要增加的安全仪表系统(SIS)进行设计,并对LOPA分析结果进行验证,即HAZOP、LOPA分析是SIL分析的前期准备工作.因此,在详细介绍SIS的组成、安全生命周期阶段、SIL的选择确定方法以及SIL分析流程之前,也简要介绍了HAZOP、LOPA分析方法,梳理了两种方法的分析流程.最后通过引入示例来展示三种分析方法之间的关系.     

HAZOP和LOPA方法在食品企业氨制冷系统中的应用

为评估食品企业氨制冷系统所处的风险状态,文章在HAZOP方法和LOPA方法理论研究的基础上,将其组合应用于食品企业氨制冷系统的风险评估中,以提前识别可能导致事故的原因和后果,确定现有防控措施是否足够、有效,并将风险控制在可接受水平。通过对氨制冷系统中低压循环桶液位过高场景的实例分析,得出该场景下的剩余风险基本在企业可接受范围内,不需要增加独立安全仪表系统的结论,为食品企业安全管理决策提供了科学依据。     

环境风险源及其分类方法研究

环境风险源的分类是进行环境风险源识别和监管的基础.已有的以人的安全为主要关注对象的重大危险源管理,对事故的潜在环境危害考虑欠缺,无法体现事故可能产生的环境风险影响.阐明环境风险源的基本概念,比较环境风险源与危险源的区别,针对环境风险源的特点,从环境受体、危害物质状态和风险传播方式3个方面提出了环境风险源的分类方法.实际应用中,环境风险源的分类需综合考虑当地环境管理需求、环境受体状况、主要危害物质类别等,选择合适的分类方法.     

基于模糊保护层的池火灾事故风险分析

为计算引发池火灾事故的风险值,提高事故风险的量化水平,判断现有风险控制措施是否满足风险容忍度的要求,为制定减缓风险措施提供依据,给出了新的池火灾风险评估模型。基于传统的保护层分析模型(LOPA),结合模糊集合理论,引入模糊风险矩阵进行风险评估,构建适用于引发池火灾事故的模糊保护层(fL OPA)风险分析模型。该模型的特点是将模糊逻辑和保护层分析结合,减少了传统保护层分析方法计算过程中的不确定性因素,引入严重度减少指数(SRI)概念,使严重度计算、风险评估更加准确。运用该模型对原油储罐泄漏池火灾事故风险进行分析,给出风险决策方案,判断现有保护措施是否能控制风险在可容忍范围内,实例验证了模型的可行性。