Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach

A bow-tie diagram combines a fault tree and an event tree to represent the risk control parameters on a common platform for mitigating an accident. Quantitative analysis of a bow-tie is still a major challenge since it follows the traditional assumptions of fault and event tree analyses. The assumptions consider the crisp probabilities and “independent” relationships for the input events. The crisp probabilities for the input events are often missing or hard to come by, which introduces data uncertainty. The assumption of “independence” introduces model uncertainty. Elicitation of expert's knowledge for the missing data may provide an alternative; however, such knowledge incorporates uncertainties and may undermine the credibility of risk analysis.This paper attempts to accommodate the expert's knowledge to overcome missing data and incorporate fuzzy set and evidence theory to assess the uncertainties. Further, dependency coefficient-based fuzzy and evidence theory approaches have been developed to address the model uncertainty for bow-tie analysis. In addition, a method of sensitivity analysis is proposed to predict the most contributing input events in the bow-tie analysis. To demonstrate the utility of the approaches in industrial application, a bow-tie diagram of the BP Texas City accident is developed and analyzed.     

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.     

Qualitative risk assessment of a Dual Fuel (LNG-Diesel) system for heavy-duty trucks

A Dual Fuel (LNG-Diesel) system can be applied to heavy-duty diesel trucks for achieving environmental benefits in comparison to existing diesel vehicles. Because of lack of reports about risk assessment of this technology, we performed a qualitative assessment based on a framework of some literature techniques for risk identification, analysis and evaluation. After constructing a Reliability Block Diagram (RBD) to establish the context, we conducted bow-tie analysis, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), likelihood and consequence analysis, and used a risk matrix. We applied these methods and techniques qualitatively to identify causes (e.g. collisions), critical events (e.g. releases of natural gas), related consequences (e.g. fires and explosions), and different possible pathways from a specific cause to its consequence, and to assess some negative accident scenarios related to use and parking of the vehicle. The bow-tie analysis also allowed to make explicit barriers and controls that prevent critical events and/or mitigate consequences. Therefore, we identified a set of safety measures, including design, technical, management, and emergency actions, which shall be implemented in each step of the system's life cycle.Our risk assessment showed that the risk level of the Dual Fuel (LNG-Diesel) system is similar to the risk level of a traditional diesel system. Future research will overcome current lack of data and, therefore, permit a quantitative rating of the risk of the Dual Fuel (LNG-Diesel) system.     

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.     

Uncertainty aspects in process safety analysis

Uncertainties of input data as well as of simulation models used in process safety analysis (PSA) are key issues in the application of risk analysis results. Mostly, it is connected with an incomplete and uncertain identification of representative accident scenario (RAS) and other vague and ambiguous information required for the assessment of particular elements of risk, especially for determination of frequency as well as severity of the consequences of RAS. The authors discuss and present the sources and types of uncertainties encountered in PSA and also methods to deal with them. There are different approaches to improve such analysis including sensitivity analysis, expert method, statistics and fuzzy logic. Statistical approach uses probability distribution of the input data and fuzzy logic approach uses fuzzy sets. This paper undertakes the fuzzy approach and presents a proposal for fuzzy risk assessment. It consists of a combination of traditional part, where methods within the process hazard analysis (PHA) are used, and “fuzzy part”, applied quantitatively, where fuzzy logic system (FLS) is involved. It concerns frequency, severity of the consequences of RAS and risk evaluation. In addition, a new element called risk correction index (RCI) is introduced to take into account uncertainty concerned with the identification of RAS. The preliminary tests confirmed that the final results on risk index are more precisely and realistically determined.     

Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis

Vast amounts of oil & gas (O&G) are consumed around the world everyday that are mainly transported and distributed through pipelines. Only in Canada, the total length of O&G pipelines is approximately 100,000 km, which is the third largest in the world. Integrity of these pipelines is of primary interest to O&G companies, consultants, governmental agencies, consumers and other stakeholder due to adverse consequences and heavy financial losses in case of system failure. Fault tree analysis (FTA) and event tree analysis (ETA) are two graphical techniques used to perform risk analysis, where FTA represents causes (likelihood) and ETA represents consequences of a failure event. ‘Bow-tie’ is an approach that integrates a fault tree (on the left side) and an event tree (on the right side) to represent causes, threat (hazards) and consequences in a common platform. Traditional ‘bow-tie’ approach is not able to characterize model uncertainty that arises due to assumption of independence among different risk events. In this paper, in order to deal with vagueness of the data, the fuzzy logic is employed to derive fuzzy probabilities (likelihood) of basic events in fault tree and to estimate fuzzy probabilities (likelihood) of output event consequences. The study also explores how interdependencies among various factors might influence analysis results and introduces fuzzy utility value (FUV) to perform risk assessment for natural gas pipelines using triple bottom line (TBL) sustainability criteria, namely, social, environmental and economical consequences. The present study aims to help owners of transmission and distribution pipeline companies in risk management and decision-making to consider multi-dimensional consequences that may arise from pipeline failures. 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. A simple example is used to demonstrate the proposed approach.     

Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry

In this research, a framework combining lean manufacturing principles and fuzzy bow-tie analyses is used to assess process risks in chemical industry. Lean manufacturing tools and techniques are widely used for eliminating wastes in manufacturing environments. The five principles of lean (identify value, map the value stream, create flow, establish pull, and seek perfection) are utilized in the risk assessment process. Lean tools such as Fishbone Diagram, and Failure Mode and Effect Analysis (FMEA) are used for risk analysis and mitigation. Lean principles and tools are combined with bow-tie analysis for effective risk assessment process. The uncertainty inherent with the risks is handled using fuzzy logic principles. A case study from a chemical process industry is provided. Main risks and risk factors are identified and analyzed by the risk management team. Fuzzy estimates are obtained for the risk factors and bow-tie analysis is used to calculate the aggregated risk probability and impact. The risks are prioritized using risk priority matrix and mitigation strategies are selected based on FMEA. Results showed that the proposed framework can effectively improve the risk management process in the chemical industry.     

Failure rate evaluation with influencing factors

A new methodology for failure rate evaluation with influencing factors is proposed. A quantitative part allows integrating potential available data from feedback. Then, a qualitative analysis deals with influencing factors such as design, environment, and use to provide more coherent and argued results. The main idea is to use some criteria to fix the failure rate within a prior interval, according to the influencing factor states. To this end, the system is broken up into main component groups. When a component group is susceptible to an influencing factor, its baseline failure rate is multiplied by the relevant influencing coefficient. A seven-step methodology is presented to define the model, identify and rate the influencing factors, set indicator functions taking the uncertainties into account, and calculate the influencing coefficients. The proposed methodology is global enough to be usable for a large number of safety instrumented systems and influencing factors. For example, an application regarding safety pressure relief valves is given. The prospect is for more efficient risk management by acting both on systems and influencing factors.     

Uncertainty representation and propagation in quantified risk assessment using fuzzy sets

It is generally acknowledged that there are substantial uncertainties present in any analysis of risk. This paper provides a brief overview of the current techniques used for uncertainty analyses, and highlights their inappropriateness for practical use in the complete risk assessment process. The concept of fuzzy sets as a means for quantifying uncertainty is introduced and a case study demonstrates the application of this method to a simple consequence analysis where parameter uncertainty is considered. The results of this fuzzy analysis are compared with those of a more traditional probabilistic approach using a Monte Carlo simulation. This comparison demonstrates that the novel approach of fuzzy sets is a more appropriate technique due to its non-statistical nature and that the amount of computation required is substantially reduced compared to the traditional probabilistic approach. The versatility of fuzzy set theory suggests that this approach could also be used to quantify other types of uncertainty present in the risk assessment process, including model uncertainty and expert opinion.     

From a survey on accidents in the downstream oil industry to the development of a detailed near-miss reporting system

A historical analysis with statistical investigation on accidental events in the oil industry from the beginning of the XX century till now, was performed in order to identify historical trend and go deeper into accident causes. The classification methodology was developed referring to three headings, namely plant/process, environment and organization and trying to go deeper into the analysis of the causes of the accidents reported and understand more of what is probably behind the accidents. The accident types and severity were studied, plotting the accumulated frequency–fatality curve for each item. In the subsequent applicative phase, we applied a similar classification approach to near-misses directly collected over nine years observation in a large downstream oil firm. The historical analysis was extended on each section of the refinery, paying a careful attention to all causes and consequences of the event. Data were structured for analyzing trends and identifying possible precursors of unwanted events. According to the step-by-step approach we try to evidence how immediate causes of a near-miss could be linked in some kind of causal chain to underlying causes that should be controlled by middle or higher management, or are part of the corporate safety culture.     

Environmental risk source management system for the petrochemical industry

We identify environmental risk sources within the petrochemical industry with a bow-tie analysis, evaluate environmental risk sources with an integrated environmental risk assessment index, and classify environmental risk sources considering both environmental consequences and management costs. Furthermore, we develop a routine management system for environmental risk sources based on browser/server model and web-GIS technology. The system has four main functions: petrochemical enterprise registration and declaration, environmental risk source information correction and confirmation, environmental risk source evaluation and classification, and environmental risk source management. The system runs with the following sequential steps. (1) Petrochemical enterprises register and declare their environmental risk source information. (2) The registered environmental risk source information is checked, corrected and confirmed by local environmental officials. (3) The probability and intensity of environmental risk are calculated for all registered petrochemical factories. (4) All environmental risk sources are classified into high, medium and low risk sections based on their potential regional environmental and ecological impacts. (5) The system provides recommendations on the routine risk management based on empirical expert opinions. The software provides an effective tool for safety production of petrochemical enterprises and can be applied by local governments for environmental risk source management.     

A risk concept applicable for both probabilistic and non-probabilistic perspectives

In engineering risk assessments, probability is the common tool used to describe the epistemic uncertainties about unknown quantities. Probability is considered a main component of risk. However, a number of alternative approaches exist for representing and describing uncertainties in risk assessments, including possibility theory and evidence theory. For these approaches and theories a probability-based risk definition cannot in general serve as a conceptual framework for risk assessments. A broader risk perspective is required. The purpose of the present paper is to present such a perspective and show how both the probabilistic and the alternative approaches and theories can be supported by this perspective. The key feature of this perspective is that uncertainty replaces probability in the definition of risk.     

A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment

In highly complex industries, capturing and employing expert systems is significantly important to an organization's success considering the advantages of knowledge-based systems. The two most important issues within the expert system applications in risk and reliability analysis are the acquisition of domain experts' professional knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition processes. The members of an expert panel regularly represent different experiences and knowledge. Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in current expert system applications in probabilistic risk assessment could not accurately represent the increasingly complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Z-numbers to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate the application and feasibility of the proposed methodology.     

基于贝叶斯网络的复杂工程安全管理决策支持方法研究

为改进当前复杂工程安全风险管理中普遍存在偏重事前管理、过分依赖专家经验等不足,探讨复杂工程贝叶斯网络(BN)的构建流程,提出基于BN的安全管理辅助决策分析(DSA)方法,并以武汉越江地铁盾构隧道渗漏水事故为例进行实证研究。结果表明:利用BN能够融合多领域专家关于具体工程的实践经验,以有向图形式直观表达安全事故致因变量间的不确定性关系,在事前阶段运用正向推理技术预测事故发生概率以界定警情等级及早防范,事中运用重要度分析辨识关键致险因子以明确过程控制要点,事后运用反向推理技术快速诊断查明最可能致因组合,从而为复杂工程安全事故全过程管理提供实时辅助决策支持。     

Dynamic Procedure for Atypical Scenarios Identification (DyPASI): A new systematic HAZID tool

The availability of a hazard identification methodology based on early warnings is a crucial factor in the identification of emerging risks. In the present study, a specific method named Dynamic Procedure for Atypical Scenarios Identification (DyPASI) was conceived as a development of bow-tie identification techniques. The main aim of the methodology is to provide a comprehensive hazard identification of the industrial process analysed, joined to a process of continuous improvement of the results of the assessment. DyPASI is a method for the continuous systematization of information from early signals of risk related to past events. The technique provides a support to the identification and assessment of atypical potential accident scenarios related to the substances, the equipment and the site considered, capturing available early warnings or risk notions. DyPASI features as a tool to support emerging risk management process, having the potentiality to contribute to an integrated approach aimed at breaking “vicious circles”, helping to trigger a gradual process of identification and assimilation of previously unrecognised atypical scenarios.     

Influence of parameter uncertainty on modeling of industrial ammonia reactor for safety and operability analysis

Chemical reactors represent probably the most hazardous units of chemical industry. Safety analysis of a chemical reactor requires basic knowledge of all particular processes which can be described by mathematical models. Most of the model parameters involved in the prediction of reactor behavior are uncertain. These uncertainties can cause discrepancies mainly in the prediction by models with nonlinear behavior and they can be the source of confusion in the design of chemical reactors and consequently also in the safety and operability analysis.The main aim of this work was to analyze the influence of uncertainties in the model parameters on the prediction of operating quantities by mathematical models with nonlinear behavior. Such analysis can be used for safety and operability analysis of an industrial catalytic ammonia reactor. The industrial fixed-bed reactor was used by a mathematical model with nine parameters. Analyses of the influence of uncertainty in a single model parameter and their combination were carried out by the Monte Carlo approach. It is shown that even a small uncertainty in one of the key parameters or in a combination of these key parameters can result in several steady states results of the operating quantities and can be the source of confusion in the design and consequently also in the safety and operability analysis.     

基于模糊Bow-tie模型的油轮靠港装卸作业溢油风险分析

为了全面分析油轮靠港装卸作业溢油事故风险,在风险定量分析中引入了模糊Bow-tie模型,基于事故树方法分析油轮靠港装卸作业发生溢油事故的原因,采用事件树方法分析溢油事故可能导致的后果,利用模糊集理论与专家评价相结合的方法分析油轮靠港装卸作业溢油的模糊可能值,采用层次分析法确定作业溢油后果因素的权重值,采用矩阵乘法计算溢油后果风险值。分析结果表明:油轮靠港装卸作业过程中一旦发生溢油,发生火灾+污染、爆炸+污染的概率较高。基于以上风险分析提出了油轮靠港装卸作业风险的防控措施,可为油轮靠港装卸作业安全风险管理提供参考。     

Quantitative assessment of domino scenarios by a GIS-based software tool

A software procedure was developed for the quantitative assessment of domino effect. The procedure was based on a systematic methodology for the identification of domino scenarios and for the assessment of consequences and expected frequencies of the escalation events. A geographical information system (GIS) platform was interfaced to the domino assessment software. The implementation of plant lay-out data to the GIS allowed the automatic identification of the possible targets of escalation effects by the software procedure, and a straightforward calculation of the contribution to individual and societal risk indexes caused by the possible domino scenarios. The procedure was applied to the analysis of several case-studies based on actual plant lay-outs. The results evidenced that the approach allows the quantitative assessment of risk caused by escalation events with a limited additional effort with respect to that required by a conventional QRA. The use of a GIS-based software was a key element in the limitation of the effort required for the quantitative assessment of domino scenarios. Moreover, the results of the case-studies pointed out that the estimation of risk increase due to domino events is an important tool for an effective assessment and control of industrial risk in chemical and process plants.     

Major accident management in the process industry: An expert tool called CESMA for intelligent allocation of prevention investments

A tool (called CESMA) was developed to carry out cost–benefit analyses and cost-effectiveness analyses of prevention investments for avoiding major accidents. A wide variety of parameters necessary to calculate both the costs of the considered preventive measures and the benefits related with the avoidance of accidents were identified in the research. The benefits are determined by estimating the difference in (hypothetical) major accident costs without and with the implementation of a preventive measure. As many relevant costs and benefits as possible were included into the tool, based on literature and expert opinion, in order to be able to deliver an all-embracing cost–benefit analysis and cost-effectiveness analysis to assist in the investment decision process. Because major accidents are related to extremely low frequencies, the tool takes the uncertainty of the unwanted occurrence of a major accident into account through the usage of a so-called ‘disproportion factor’. Compared with existing software, the CESMA tool is innovative by striving for an as-accurate-as-possible picture of costs and benefits of major accident prevention, and taking the uncertainties accompanying disastrous events into consideration. Furthermore, an illustrative example of CESMA is presented in the paper.     

TOPHAZOP: a knowledge-based software tool for conducting HAZOP in a rapid, efficient yet inexpensive manner

Hazard and operability (HAZOP) studies constitute an essential step in the risk analysis of any chemical process industry and involve systematic identification of every conceivable abnormal process deviation, its causes and abnormal consequences. These authors have recently proposed optHAZOP as an alternative procedure for conducting HAZOP studies in a shorter span of time than taken by conventional HAZOP procedure, with greater accuracy and effectiveness [Khan, F. I. and Abassi, S. A., optHAZOP. An effective and efficient technique for hazard identification and assessment Journal of Loss Prevention in the Process Industries, 1997, 10, 191–204]. optHAZOP consists of several steps, the most crucial one requires use of a knowledge-based software tool which would significantly reduce the requirement of expert man-hours and speed up the work of the study team. TOPHAZOP (Tool for OPTmizing HAZOP) has been developed to fulfil this need.

The TOPHAZOP knowledge-base consists of two main branches: process-specific and general. The TOPHAZOP framework allows these two branches to interact during the analysis to address the process-specific aspects of HAZOP analysis while maintaining the generality of the system. The system is open-ended and modular in structure to make easy implementation and/or expansion of knowledge. The important features of TOPHAZOP and its performance on an industrial case study are described.