基于FFTA-LOPA的化工装置安全仪表系统SIL确定方法

为了优化确定化工装置安全仪表系统（SIS）安全完整性等级（SIL）,分析了现有确定SIL的不足,针对化工装置的失效数据缺失和不确定性特点,提出模糊事故树-保护层（FFTA-LOPA）模型计算安全仪表系统SIL。以某低密度聚乙烯反应釜为例,建立了该反应釜爆炸事故树,运用模糊理论定量分析顶上事件发生的概率,最终确定其安全仪表系统安全完整性等级为SIL 1。结果表明:该方法结合两种风险分析理论,分析结果与实际和理论统计结果符合性较好,具有一定地准确性和实用性,可以为定量确定系统SIL提供理论指导。     

饮食业油烟道火灾事故概率的模糊事故树分析

根据常规事故树定量计算中基本事件概率的不确定性问题,通过引入模糊集的概念,将常规事故树中基本事件的发生概率模糊化,用三角形模糊数代替确定性发生概率,应用模糊数截集方法,推导了模糊事故树的相关算法.采用模糊事故树方法得到饮食业油烟道火灾事故发生概率,并进行了敏感性分析,找出对顶事件发生概率影响较大的基本事件,确认减小油烟道火灾事故发生的相关措施.研究表明,饮食业油烟道火灾事故概率的模糊事故树分析是切实可行的,有助于对饮食业油烟道火灾的认识、预防和扑救工作.     

An extension of the fuzzy improved risk graph and fuzzy analytical hierarchy process for determination of chemical complex safety integrity levels

The risk graph (RG) is widely used to evaluate the safety integrity level (SIL) of safety instrument systems (SIS). However, subjective opinion-based conventional RGs cannot provide successful results for the problems of risk parameters, such as shortages or lack of data; hence, the output of a conventional approach lacks sufficient reliability. We introduced the fuzzy improved risk graph (FIRG), an extension of fuzzy set theory, to deal with possible ambiguities during SIL study and increase the reliability of conventional RGs. In the present study, the levels of consequences defined as linguistic terms were converted into qualitative intervals; therefore, by correlating the proposed approach with experts’ opinions and attributing weight factors, a desired SIL value was obtained. The output of this new approach can be compared directly with quantitative risk assessment techniques to improve the safety performance of industrial systems.     

Reliability assessment of safety instrumented systems subject to different demand modes

Safety instrumented systems (SISs) are commonly used in the process industry, to respond to hazardous events. In line with the important standard IEC 61508, SISs are generally classified into two types: low-demand systems and high-demand systems. This article explores this classification by studying the SIS reliability for varying demand rates, demand durations, and test intervals. The approach is based on Markov models and is exemplified by two simple system configurations. The SIS reliability is quantified by the probability of failure on demand (PFD) and the frequency of entering a hazardous state that will lead to an accident if the situation is not controlled by additional barriers. The article concludes that very low-demand systems are similar and may be treated as a group. The same applies to very high-demand system. Between these group, there is a rather long interval where the demand rate is neither high-demand nor low-demand. These medium-demand systems need a specific treatment. The article shows that the frequency of entering into a hazardous state increases with the demand rate for low-demand systems, while it is nearly independent of both the demand rate and the demand duration for high-demand systems. The PFD is an adequate measure for the SIS reliability for low-demand systems, but may be confusing and difficult to interpret for high-demand systems.     

Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion

Floods have become increasingly alarming worldwide. Flood risk management in terms of assessing disaster risk properly is a great challenge that society faces today. Natural disaster risk analysis is typically beset with issues such as imprecision, uncertainty, and partial truth. There are two basic forms of uncertainty related to natural disaster risk assessment, namely, randomness caused by inherent stochastic variability and fuzziness due to macroscopic grad and incomplete knowledge sample. However, the traditional probability statistical method ignores the fuzziness of risk assessment with incomplete data sets and requires a large sample size of data. The fuzzy set methodology is introduced in the area of disaster risk assessment to improve probability estimation. The purpose of the current study is to establish a fuzzy model to evaluate flood risk with incomplete data sets. The present paper puts forward a composite method based on variable fuzzy sets and information diffusion method for disaster risk assessment. The results indicate that the methodology is effective and practical; thus, it has the potential to forecast the flood risk in flood risk management. We hope that by conducting such risk analysis, the impact of flood disasters can be mitigated in the future.     

The role of component arrangement in complex safety instrumented systems—A case study

The arrangement of components plays a key role in the performance of complex Safety Instrumented Systems (SIS), in which a SIS logic solver is interlocked with other logic solvers, to share a final element, for instance. The position of the components and the way they are utilized affects the reliability characteristics, such as the Probability of Failure on Demand (PFD), Spurious Trip Rate (STR), architectural sensitivity and model uncertainty. This case study uses quantitative and qualitative approaches to elaborate on various aspects of component arrangement in complex SIS. Numerous simplified models are analyzed; new classification is introduced for SIS components based on their response to demand; a set of guidelines are developed for SIS architecture design, with a focus on component arrangement; and the use of these guidelines is demonstrated in a real-life example, where an existing turbine SIS is modified to incorporate a new over-speed protection system. The simplified models and the turbine upgrade project are also used to explain the issue of unknowns and uncertainties in reliability analysis and how these issues can be addressed in SIS architecture by optimizing component arrangement.     

Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments

Hot and humid environments are prevalent in many industries. People working in hot and humid environments are at great risk of specific heat-related disorders, the productivity decrease and safety problems. In order to guarantee workers’ health and safety, safety evaluation and early warning rating of the hot and humid environments are studied in this paper. The fuzzy analytic hierarchy process (AHP) method is proposed to evaluate the work safety in hot and humid environments. Trapezoidal fuzzy numbers are adopted to handle inherent uncertainty and imprecision of the data involved in decision process. Within the proposed methodology, a decision group is firstly established. A safety evaluation framework containing three factors (work, environment, and workers) and ten sub-factors are established. The fuzzy weights of the factors and sub-factors are calculated based on the pair-wise comparisons. Then the fuzzy evaluating vectors of the sub-factors and factors can be calculated according to the initial evaluation data. Therefore, the comprehensive safety index, safety grade and early warning grade can be determined. An example is given to demonstrate the proposed method. The results demonstrate the engineering practicability and effectiveness of this method in extreme environment evaluation.     

Comprehensive evaluation on self-ignition risks of coal stockpiles using fuzzy AHP approaches

Self-ignition in coal stockpiles is a serious economic, environmental and safety problem. Evaluation is an effective way to identify the self-ignition hazards in implicit environment of coal piles and provides a guide for countermeasures against spontaneous combustion in coal piles. In this paper, a comprehensive evaluation system is proposed. The trapezoidal and the triangular extent fuzzy AHP methods are employed to handle the imprecision and uncertainty of the effects of factors. A coal stockpile stored at the Bulk Cargo Logistics of Tianjin Port of China and three coal piles in the Teruel basins of Spain are studied to demonstrate the validity of the index system and the effectiveness of fuzzy AHP approaches. The evaluated results indicate that the proposed evaluation system and approaches are valid and objective for evaluating self-ignition risks of coal stockpiles; compared with the trapezoidal fuzzy AHP method, the triangular extent fuzzy AHP approach is more effective to evaluate self-ignition risks of coal piles for the priorities of factors impacting self-ignition risks are highlighted by their weight comparisons' calculation using the triangular extent analysis. The comprehensive evaluation system is beneficial to manage self-ignition risks of coal piles from a holistic point of view and to establish an early warming system of self-ignition risks of coal piles.     

区域自然灾害综合风险模糊随机评价方法

从自然灾害危险性、社会经济易损性和区域设防等多种角度建立了区域自然灾害综合风险评价指标体系。针对典型自然灾害综合风险评价影响因素具有随机性、模糊性和数据资料不精确等多种不确定性,利用蒙特卡洛方法模拟三角模糊数,把三角模糊数及其函数之间的运算简化为普通的实数之间的运算,从而建立区域自然灾害综合风险模糊随机评价模型。研究表明,在评价结果一致的前提下以置信区间形式表示的综合风险评价结果比其他常规方法,提供的信息更加丰富,充分反映了受多种不确定性因素综合影响的区域自然灾害综合风险评价的客观实际情况,研究结果可为区域防御自然灾害与提升综合抗灾能力提供决策依据。     

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。     

基于Monte Carlo与Markov法参数不确定性条件下SIL评估

为了避免由参数不确定性因素导致较大的Markov法SIL评估偏差,且减少计算工作量和复杂性,采用Monte Carlo（MC）仿真方法处理含不确定性参数的Markov模型,并借助Matlab GUI编程开发MC仿真处理参数不确定性条件下Markov法SIL评估可视化仿真计算软件。在理论研究的基础上,为说明该研究方法与计算软件的可行性,以石油天然气工业高完整性压力保护系统（HIPPS）为算例进行SIL评估。结果表明:MC仿真方法可以有效处理Markov法SIL评估中参数不确定性问题;基于Matlab GUI编程设计出的仿真计算软件在一定程度上可以提高计算效率。     

Fuzzy logic-based approach for identifying the risk importance of human error

In the system reliability and safety assessment, the focuses are not only the risks caused by hardware or software, but also the risks caused by “human error”. There are uncertainties in the traditional human error risk assessment (e.g. HECA) due to the uncertainties and imprecisions in Human Error Probability (HEP), Error-Effect Probability (EEP) and Error Consequence Severity (ECS). While fuzzy logic can deal with uncertainty and imprecision. It is an efficient tool for solving problems where knowledge uncertainty may occur. The purpose of this paper is to develop a new Fuzzy Human Error Risk Assessment Methodology (FHERAM) for determining Human Error Risk Importance (HERI) as a function of HEP, EEP and ECS. The modeling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs (i.e. HEP, EEP, and ECS) and outputs (i.e. HERI) of a risk assessment system in the form of IF–THEN rules. It is implemented on fuzzy logic toolbox of MATLAB using Mamdani techniques. A case example is presented to demonstrate the proposed approach. Results show that the method is more realistic than the traditional ones, and it is practicable and valuable.     

Systematic inherent safety and its implementation in chlorine liquefaction process

As a proactive safeguard, inherent safety has been regarded as the top hierarchy for loss prevention and risk management due to its salient features in eliminating or significantly reducing risks at source rather than mitigating them by add-on protections. Simultaneously, various assessment tools have been developed for ranking and selecting inherently safer designs or modifications. However, there still lacks a metric that can systematically incorporate various hazardous factors, which may hinder most industries from utilizing it to a full extent. To address this limitation, this work developed a Systematic Inherent Safety Metric (SISM) for measuring the inherently safer modifications. Firstly, the conceptual framework of SIS was proposed based on 5M1E (man, machine, material, method, measurement, and environment). Subsequently, analytic hierarchy process and fuzzy comprehensive evaluation were adapted to conduct risk identification and assessment. Finally, taking chlorine liquefaction process as a case study, the applicability and efficacy of SIS were validated based on PDCA (plan-do-check-action) cycle. The results show that the SISM value has improved from the relatively dangerous (RD) to the relatively safe (RS) after implementing SIS, thus demonstrating that the revised design is inherently safer than the base design.     

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.     

油气储运设施事故风险指数模糊逻辑评估方法

油气储运设施风险是其事故发生概率和事故后果的综合度量,而事故概率和后果的定量评估结果往往是具有不确定性的数据,以确定性风险评估准则为基础的传统风险矩阵法和风险值法显然难以评估油气储运设施风险。为此提出开展油气储运设施事故风险的模糊逻辑推理法,首先,对风险矩阵的概率语言等级和损失语言等级的边界进行定量划分;然后,建立油气储运设施风险矩阵模糊集和模糊逻辑推理规则;最后,通过风险模糊推理运算和模糊风险解模糊化以确定油气储运设施的风险水平。实例应用与分析表明,利用推荐方法可得到较为详尽的风险数据信息,不但风险指数更加清晰,而且其所属风险等级类别也更加明确,评估结果能更好地指导油气储运设施的风险管理。     

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.     

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.     

安全联锁设备SIL验证中的参数估计偏差分析*

为提高安全联锁系统（SIS）不可用度计算的准确性,减少SIL验证的不确定性偏差,研究参数估计置信度分别为70%和90%时功能安全标准IEC61508的恒定失效率算法与实际非恒定失效率（威布尔分布）算法结果的偏差。结果表明:在70%置信度时,采用指数分布算法计算PFDavg,比威布尔分布算法的计算结果偏大,2者的相对偏差平均值为68.01%;90%置信度时,2者的相对偏差平均值为42.63%,揭示功能安全标准IEC61508恒定失效率计算将显著高估SIS的不可用性,可能造成企业不必要的整改和相应的维护成本。     

基于模糊数学的石油化工装置HAZOP风险分析技术研究

对传统的HAZOP分析中偏差原因发生可能性进行量化。对于有统计数据的,根据行业数据、公司经验及企业事故建立HAZOP风险分析统计数据库;对于没有统计数据的HAZOP分析偏差原因发生概率,通过专家主观评判,用模糊数理论将专家自然语言转换为模糊数,采用左右模糊排序法将模糊数转换为模糊失效概率值。研究了偏差后果严重程度的划分标准,并根据偏差原因概率和偏差后果严重程度确定风险等级,利用风险矩阵得出偏差风险的大小。从而把HAZOP分析方法从定性改进为半定量的分析方法。据此对石油化工装置进行了HAZOP风险分析。     

基于模糊熵和非对称贴近度的安全预警评估方法

针对系统安全风险评估和预警过程中,系统安全特性和各预警因素所存在的模糊不确定性问题,将熵理论和模糊数学相结合,提出一种基于模糊熵和非对称贴近度的系统安全预警评估方法。在建立系统安全预警评估指标体系的基础上,首先,分析基于模糊熵理论确定预警指标权重的可行性,给出系统预警指标赋权算法;其次,建立基于模糊非对称贴近度确定系统安全等级的过程,第三,给出系统安全预警评估的具体计算步骤;最后,以人为因素系统的安全预警评估为例进行方法的实例验证。实例分析结果表明采用该方法所确定的系统预警指标权重更加符合客观实际,能有效解决系统安全预警评估中存在的模糊不确定性问题,系统预警决策结果合理有效。