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.     

Mitigation of operational failures via an economic framework of reliability,availability, and maintainability (RAM) during conceptual design

Abnormal process situation may lead to tremendous negative impact on sustainability, wellbeing of workers and adjacent communities, company's profit, and stability of supply chains. Failure of equipment and process subsystems are among the primary causes of abnormal situations. The conventional approach in handling failure-based abnormal situations has usually focused on operational strategies. Such an approach overlooks the critical role of process design in mitigating failure, while simultaneously considering the effects of such failure on process economic performance. The aim of this work is to introduce a systematic methodology that accounts for failure early enough during the conceptual design stages. Once a base-case design is developed, the methodology starts by identifying the sources of failure that are caused by reliability issues including equipment, operational procedures, and human errors for a given process system or subsystem. This allows for the identification of critical process subsystem(s) that are more failure-prone or cause greater downtime than other subsystems. Bayesian updating and Monte Carlo techniques are utilized to determine the appropriate distributions for the failure and repair scenario(s), respectively, in question. Markov analysis is used to determine the system availability. Next, the process revenue is described as a function of inherent availability. The effects of failures are incorporated into profitability calculations to establish an economic framework for trading off failure and profitability. In the proposed framework, the economic potential of alternative design scenarios is evaluated and an optimization formulation with the objective of maximizing incremental return on investment (IROI) is utilized to make a design decision. A case study on an ethylene plant is solved to demonstrate the applicability and value of the proposed approach.     

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.     

Dynamic failure assessment of an ammonia storage unit: A case study

Chemical Process Industries usually contain a diverse inventory of hazardous chemicals and complex systems required to perform process operations such as storage, separation, reaction, compression etc. The complex interactions between the equipment make them vulnerable to catastrophic accidents. Risk and failure assessment provide engineers with an intuitive tool for decision making in the operation of such plants. Abnormal events and near-miss situations occur regularly during the operation of a system. Accident Sequence Precursors (ASP) can be used to demonstrate the real-time operating condition of a plant. Dynamic Failure Assessment (DFA) methodology is based on Bayesian statistical methods incorporates ASP data to revise the generic failure probabilities of the systems during its operational lifetime.In this paper, DFA methodology is applied on an ammonia storage unit in a specialized chemical industry. Ammonia is stored in cold storage tanks as liquefied gas at atmospheric pressure. These tanks are susceptible to failures due to various abnormal conditions arising due process failures.Tank failures due to three such abnormal conditions are considered. Variation of the failure probability of the safety systems is demonstrated. The authors use ASP data collected from plant specific sources and safety expert judgement. The failure probabilities of some safety systems concerned show considerable deviation from the generic values. The method helps to locate the components which have undergone more degradation over the period and hence must be paid attention to. In addition, a Bayesian predictive model has been used to predict the number of abnormal events in the next time interval. The user-friendly and intuitive nature of the tool makes it appropriate for application in safety assessment reports in process industries.     

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。     

基于大数据的特种设备宏观安全风险预警方法研究

特种设备监管机构在监督检验过程中积累了大量数据资料,为充分挖掘其中的潜在价值,增强监管针对性,提高检验效率,应用并优化关联规则挖掘、社区发现、可视化等数据挖掘技术,构建1种宏观安全风险预警方法。该方法以特种设备安全监督检验大数据为基础,通过挖掘单台设备微观因素间的关联关系,实现整类特种设备宏观安全风险的识别与预警;以2008—2016年全国长管拖车检验数据为例,进行应用实践分析。研究结果表明:该方法可以对区域范围、缺陷类别等多种宏观指标进行预警,指导针对性监管与检验。     

一种新型危险化工工艺安全评估方法的设计

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

重大危险设备的确定及其失效后果严重度分析

采用火灾、爆炸危险指数(F&EI)方法对石化设备的危险性进行评价,根据计算设备的危险指数值来确定其是否为重大危险设备;应用模糊综合评价方法对石化装置失效后果严重程度进行综合评价研究,建立了石化装置失效后果严重程度的综合评价指标体系;采用定性与定量相结合的层次分析法计算评价因素的权重,运用模糊综合评判理论,提出失效后果严重度的二级模糊综合评价方法,实现对其失效后果严重度的综合评判。实例表明:模糊综合评价方法可操作性强、效果较好,可在石化装置失效后果严重度评价中广泛应用。     

Application of domino effect quantitative risk assessment to an extended industrial area

Escalation of primary accidental scenarios triggering a “domino effect” have caused extremely severe accidental events in the chemical and process industry. The identification of possible escalation events is required in the safety assessment of sites where relevant quantities of hazardous substances are stored or handled. In the European Union, “Seveso-II” Directive requires the assessment of on-site and off-site possible escalation scenarios in sites falling under the obligations of the Directive. In the present study, a methodology developed for the quantitative assessment of risk due to domino effect was applied to the analysis of an extended industrial area. Recently developed equipment damage probability models were applied for the identification of the final scenarios and for escalation probability assessment. The domino package of the Aripar-GIS software was used for risk recomposition. The results evidence that quantitative risk assessment of escalation hazard is of fundamental importance in order to identify critical equipment and to address prevention and protection actions.     

基于RBI技术的海上平台延期服役静设备定量化安全评估技术

针对海上延期服役平台静设备的安全风险评估与控制问题,应用RBI风险评估技术的评估理论和方法对海上延期服役平台静设备的定量化安全管理方法进行了实践和探索,并选择某延期服役平台进行风险分析相关结论的工程验证。其具体过程是运用定量RBI技术,首先根据工艺流程和介质特点对海上平台静设备分别进行了物流回路和损伤回路的划分,并以此为基础对延期服役平台静设备的安全风险进行定量评估,进而得出各静设备单元的失效可能性、失效后果及风险等级,最后通过得出的损伤模式、损伤机理及对应的腐蚀速率,从而制定针对性的检测策略。将利用这些检验策略所检测出的真实情况与分析结论进行对比,最终验证了RBI技术在延期服役平台静设备风险评估方面具有很强的适用性。     

油气集输站场失效可能性定量评价方法研究*

为了保证油气集输站场的安全生产和运行,基于标准《Risk-based Inspection Methodology》（API RP 581 2016）和可靠性分析方法GO法,提出1种可完全定量的站场失效可能性评价方法,为站场的全定量风险评价方法提供依据和参考。首先,分析站场内各设备的失效机理,计算其失效概率;然后,根据站场工艺特点,将其划分为若干个子系统,利用GO操作符的定量计算公式,确定各子系统及站场整体的失效可能性值;最后,以某油田联合处理站为实例,采用该方法对其工艺设备、站场子系统及站场整体的失效概率进行计算和分析,确定该站场的失效可能性等级。研究结果表明:通过失效可能性定量评价可使站场风险评价更加量化直观,为站场维护及安全生产运行工作提供更加客观的依据。     

Selection of failure frequency and its impact on risk assessment – A case study from plot plan optimisation

Facility Siting is an important phase of project development. A critical stage is plot plan optimisation, where significant potential hazards are eliminated due to equipment spacing. In addition to ensuring appropriate compliance with minimum spacing requirements, occupied building studies to achieve compliance with the requirements of API 752 and API 753 could also be undertaken to optimise safety outcomes. The studies are done in three stages, where the first stage is hazard identification, second stage is consequence assessment and the third stage is risk assessment. Third stage assessments are only carried, if the consequence based siting recommendations are not practical to implement.This paper presents the challenges in estimating risk due to process hazards with a focus on selecting right event likelihood data. A comparison is presented on the variation in predicted risk levels based on equipment failure rates and leak frequencies.Case study of a plot plan optimisation study is undertaken with DNVGL Phast Risk and the variation in risk levels up to two orders of magnitude are recorded. Challenges such as adaption of data for local conditions, consistent definitions of failure, sample size of data, applicability of data play a significant role in identifying and correctly quantifying the risk levels. Such challenges and its impact on risk quantification are presented in this paper as well as its impact on facility siting.     

基于RBI的碳五石油树脂装置风险评估分析

从技术特点、实施效用等方面,对QRA与RBI两类风险评估技术进行比较,选择使用RBI技术方法对碳五石油树脂装置内静设备及管道实施风险评估。运用挪威船级社的ORBIT Onshore软件定量计算评估范围内设备、管道风险值,评定风险等级,辨识隐患设备,然后从后果、可能性两方面分析产生风险的主要影响因素,经分析得知碳五石油树脂装置受H2S、HCl腐蚀减薄影响轻微,但NaOH导致的碱应力腐蚀对聚合反应后序工段中部分管道设备失效可能性影响较大。最后根据装置内存在的不同损伤机理提出检验优化策略,为装置定期检验工作提供科学的决策支持。     

Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling

The catastrophic consequences of recent NaTech events triggered by earthquakes highlighted the inadequacy of standard approaches to seismic risk assessment of chemical process plants. To date, the risk assessment of such facilities mainly relies on historical data and focuses on uncoupled process components. As a consequence, the dynamic interaction between process equipment is neglected. In response to this gap, researchers started a progressive integration of the Pacific Earthquake Engineering Research Center (PEER) Performance-Based Earthquake Engineering (PBEE) risk assessment framework. However, a few limitations still prevent a systematic implementation of this framework to chemical process plants. The most significant are: (i) the computational cost of system-level simulations accounting for coupling between process equipment; (ii) the experimental cost for component-level model validation; (iii) a reduced number of hazard-consistent site-specific ground motion records for time history analyses.In response to these challenges, this paper proposes a recently developed uncertainty quantification-based framework to perform seismic fragility assessments of chemical process plants. The framework employs three key elements: (i) a stochastic ground-motion model to supplement scarcity of real records; (ii) surrogate modeling to reduce the computational cost of system-level simulations; (iii) a component-level model validation based on cost-effective hybrid simulation tests. In order to demonstrate the potential of the framework, two fragility functions are computed for a pipe elbow of a coupled tank-piping system.     

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.     

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.     

Compression system check-valve failure hazards

Processes that utilize multistage compression systems (olefins plant compression systems, gas processing, integrated refrigeration systems, etc.) have the potential for overpressure due to single or multiple check-valve failure. Catastrophic equipment failure resulting from overpressure can potentially occur due to compression system discharge, interstage, and/or suction check-valve failure, coincident with compressor shutdown. Depending on system design and application, overpressure values approaching or exceeding 300% of equipment design are possible, while for some equipment, even limited overpressure can result in catastrophic vessel failure due to brittle fracture. Additional hazards associated with compression system fail-to-check scenarios include risks associated with excessive flare loading and compressor rotor reverse rotation. In the case of an ethylene refrigeration compressor at a typical ethylene plant, rotor reverse rotation can potentially exceed overspeed limits.This paper provides risk assessment results based on analyses performed on the three primary compression systems in six ethylene plants. The methodologies used for risk identification screening, detailed risk assessment and evaluation of system dynamics are all presented. Alternative methods for mitigating risks are also discussed, along with check-valve reliability data. An overview of applicable overpressure protection requirements defined in the ASME Boiler and Pressure Vessel Code is provided. This paper will be of interest to anyone who designs or operates multistage compression systems in the chemical, petrochemical or refining industries.     

Inherent risk assessment—A new concept to evaluate risk in preliminary design stage

Quantitative Risk Assessment (QRA) has been a very popular and useful methodology which is widely accepted by the industry over the past few decades. QRA is typically carried out at a stage where complete plant has been designed and sited. At that time, the opportunity to include inherent safety design features is limited and may incur higher cost. This paper proposes a new concept to evaluate risk inherent to a process owing to the chemical it uses and the process conditions. The risk assessment tool is integrated with process design simulator (HYSYS) to provide necessary process data as early as the initial design stages, where modifications based on inherent safety principles can still be incorporated to enhance the process safety of the plant. The risk assessment tool consists of two components which calculate the probability and the consequences relating to possible risk due to major accidents. A case study on the potential explosion due to the release of flammable material demonstrates that the tool is capable to identify potential high risk of process streams. Further improvement of the process design is possible by applying inherent safety principles to make the process under consideration inherently safer. Since this tool is fully integrated with HYSYS, re-evaluation of the inherent risk takes very little time and effort. The new tool addresses the lack of systematic methodology and technology, which is one of the barriers to designing inherently safer plants.     

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.