Fuzzy risk modeling of process operations in the oil and gas refineries

Operating several assets has resulted in more complexity and so occurrence of some major accidents in the refining industries. The process operations risk factors including failure frequency and the consequence components like employees' safety and environment impacts, operation downtime, direct and indirect cost of operations and maintenance, and mean time to repair should be considered in the analysis of these major accidents in any refinery. Considering all of these factors, the risk based maintenance (RBM) as a proper risk assessment methodology minimizes the risk resulting from asset failures. But, one of the main engineering problems in risk modeling of the complex industries like refineries is uncertainty due to the lack of information. This paper proposes a model for the risk of the process operations in the oil and gas refineries. The fuzzy logic system (FLS) was proposed for risk modeling. The merit of using fuzzy model is to overcome the uncertainty of the RBM components. This approach also can be accounted as a benchmark for future failures. A unified risk number would be obtained to show how the criticality of units is. The case study of a gas plant in an oil refinery is performed to illustrate the application of the proposed model and a comparison between the results of both traditional RBM and fuzzy method is made.For the case study, 26 asset failures were identified. The fuzzy risk results show that 3 failures have semi-critical level and other 23 failures are non-critical. In both traditional and fuzzy RBM methods, some condenser failures had the highest risk number and some pumps were prioritized to have the lowest risk level. The unit with unified risk number less than 40 is in the non-critical conditions. Proposed methodology is also applicable to other industries dealing with process operations risks.     

基于模糊证据理论的铁路信号系统风险评价

为综合考虑风险中的不确定性对评价结果的影响,针对目前铁路信号系统风险评价中无法充分利用专家定性评价信息的问题,提出一种基于模糊证据理论的铁路信号系统风险评价方法。首先在对系统进行风险识别的基础上,采用模糊集理论对风险因素进行统一的模糊描述;然后将模糊集中的描述映射在证据理论的识别框架下,通过证据理论进行信息融合;再结合风险因素权重进行综合评价,确定铁路信号系统的风险等级。以无线闭塞中心临时限速处理功能为例进行风险评价,分析确定该功能的风险等级,评估结果与实际情况相符,验证了该方法的有效性、合理性和可行性。     

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.     

Developing a new fuzzy inference system for pipeline risk assessment

The problem of less and/or even lack of information and uncertainty in modeling and decision making plays a key role in many engineering problems; so that, it results in designers and engineers could not reach to sure solutions for the problems under consideration. In this paper, an application of the fuzzy logic for modeling the uncertainty involved in the problem of pipeline risk assessment is developed. For achieving the aim, relative risk score (RRS) methodology, one of the most popular techniques in pipeline risk assessment, is integrated with fuzzy logic. The proposed model is performed on fuzzy logic toolbox of MATLAB^® using Mamdani algorithm based on experts' knowledge. A typical case study is implemented and a comparison between the classical risk assessment approach and the proposed model is made. The results demonstrate that the proposed model provides more accurate, precise, sure results; so that, it can be taken into account as an intelligent risk assessment tool in different engineering problems.     

A fuzzy multi-attribute model for risk evaluation in workplaces

Risk assessment constitutes a critical phase of the safety management process. It basically consists of evaluating the risks involved in the execution of working activities, so as to provide the managers with information suitable to address intervention measures. The most important activity in risk assessment is the risk evaluation task but, despite its importance, national and international regulations have never formalized a standard methodology for addressing it, leaving companies complete freedom to adopt the approach they consider appropriate. As a consequence, companies generally settle on the use of simple methodologies which frequently are not able to emphasize all the main aspects affecting risk in the workplace and to produce a precise ranking of hazardous activities. In this paper, a new methodology for risk evaluation is then proposed with the aim to overcome limitations of the classical approaches. In particular, new factors are introduced to take into account effects of human behavior and environment on risk level, other than the classical injury magnitude and occurrence probability of an accident. The proposed methodology also integrates an estimative approach based on the fuzzy logic theory, which permits more coherence in the evaluation process, producing a very suitable final rank of hazardous activities. Finally, an actual case study is reported to show the capabilities of the new risk evaluation model.     

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.     

基于人机工程的物流运输三参数风险评估模型

为了有效分析物流运输的风险状况,针对传统风险评估技术的不足,构建了一种以风险发生概率、风险后果和风险重要度为技术参数的物流运输风险三参数评估模型。首先,依据安全人机工程原理系统分析物流运输过程风险的影响因素,并建立物流运输风险评价指标体系。然后考虑风险因素的随机性和模糊性,提出了一种基于三角模糊数和模糊层次分析耦合的参数确定方法。最后,通过一个算例分析说明构建模型的有效性。结果表明,所提出方法用于物流运输风险定量分析优于传统风险评估技术。     

Integrating major accidents hazard into occupational risk assessment: An index approach

Major Accident Hazard (MAH) and Occupational Safety and Health (OSH) are two separated topics in both industrial practice and legislation; recently, interest is increasing toward an integrated risk assessment mainly forced by the tendency to a more efficient safety management system. The present study proposes a semi-quantitative approach to integrate MAH in OSH risk assessment. The two risk types are characterized by opposite features: the OSH analysis is usually task-based and focused on job profiles, while the MAH analysis is space-based and focused on plant characteristics. The basic idea of the proposed approach is to merge spatial information and job profile features in order to improve OSH assessment; thus, a risk index derived by the recent standard ISO 12100 (2010) has been adapted. In detail, the proposed index combines exposure times of each worker at each plant unit – derived from the OSH analysis – with damage areas derived from MAH analysis allowing a quantitative assessment of the MAH risk level for each individual job profile. The model has been tested in a large petrochemical plant; several hypotheses have been developed in order to validate the model. Results have showed the potentiality of the proposed approach in providing a common and coherent representation of both MAH and OSH risks, according to job profiles and plant units.     

A neuro-fuzzy algorithm for assessment of health,safety, environment and ergonomics in a large petrochemical plant

Process plants such as petrochemical units have been continuously trying to improve Health, Safety, Environment and Ergonomics (HSEE) programs. This study proposes an adaptive network-based fuzzy inference system (ANFIS) for assessment of HSEE programs in a petrochemical plant. The proposed neuro-fuzzy approach is applied to a set of operators in the petrochemical unit to show its applicability and superiority. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and accomplishment of HSEE programs is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSEE. Finally, the algorithm identifies efficient operators with respect to HSEE. This is the first study that introduces an intelligence algorithm for assessment and improvement of HSEE program in a petrochemical plant.     

Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry

Introduction: Underground mining is considered one of the most hazardous industries and is often associated with serious work-related fatalities; this paper addresses job-related hazards and associated risks. Method: A risk assessment approach is proposed (Pythagorean fuzzy environment) and a case study is carried out in an underground copper and zinc mine. Results: Results of the study demonstrate that hazards can be categorized into different risk levels via compromised solutions of the fuzzy approach. Conclusion: The study provides a theoretical contribution by suggesting a Pythagorean fuzzy numbers-based VlseKriterijumska Optimizacija I Kompromisno Resenje (PFVIKOR) approach. Moreover, it contributes to improving overall safety levels of underground mining by considering and advising on the potential hazards of risk management. Practical applications: The proposed approach will improve the existing safety risk assessment mechanism in underground copper and zinc mining.     

A Noisy-OR gate based fuzzy fault tree approach for micro-leakage evaluation of bolt-gasket-flange connection (BGFC)

In the natural gas gathering and transportation station, bolt-gasket-flange connection (BGFC) is the main connection mode, but due to its large number, the micro-leakage may result in serious consequences. For this reason, early BGFC micro-leakage evaluation can effectively assist decision-making and prevent the occurrence of accidents. To solve the problem of insufficient field data collection, we proposed a Noisy-OR gate based fuzzy fault tree approach, and calculated the corresponding minimum cut sets and the highest risk factors. Moreover, sensitivity analysis (SA) was used to determine that material problems (wear and corrosion of the sealing surface), man-made operation problems (bolts and flanges) and working conditions (pipe vibration and temperature fluctuations) are the key factors that contributing to the BGFC micro-leakage. In brief, the combination of the fuzzy fault tree analysis and Noisy-OR gate is an effective approach for BGFC micro-leakage evaluation, which is of great significance to the micro-leakage analysis, safe operation, and risk assessment of the gas station.     

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.     

基于三角模糊理论的尾矿库风险评价研究

根据尾矿库的特点和我国现行的尾矿库安全生产监督管理法律法规及标准规范的要求,构建了尾矿库风险评价指标体系。综合考虑评价指标对风险发生可能性及后果严重程度的不同影响来确定指标权重,同时考虑到权重确定和指标赋值中的模糊性和不确定性等问题,选用三角模糊理论建立了尾矿库风险评价模型,并以本溪某尾矿库为例进行说明。理论分析和实例计算表明,该方法对尾矿库风险评价有很好的适用性。     

Failure Mode and Effect Analysis using an integrated approach of clustering and MCDM under pythagorean fuzzy environment

Failure Mode and Effect Analysis (FMEA) is an effective risk analysis and failure avoidance approach in the design, process, services, and system. With all its benefits, FMEA has three limitations: failure mode risk assessment and prioritization, complex FMEA worksheets, and difficult application of FMEA tables. This paper seeks to overcome the shortcomings of FMEA using an integrated approach based on a developed Pythagorean fuzzy (PF) k-means clustering algorithm and a popular MCDM method called PF-VIKOR. In the first step, Pythagorean fuzzy numbers (PFNs) were used to collect Severity (S), Occurrence (O), and Detection (D) factors for failure modes to incorporate uncertainty and fuzziness into subjective judgments. Afterward, failure modes were clustered by developing a novel k-means clustering algorithm that accepts PFNs as input. Finally, the PF-VIKOR approach was used to analyze the ordering of cluster risks. The proposed approach was implemented in the dehydration unit of an Iranian gas refinery and the results were compared with the traditional FMEA. The findings showed the flexibility and applicability of the proposed approach in addressing real-world problems. This research provides two key contributions: (1) designing a PFN-based k-means clustering algorithm that tackles FMEA limitations and (2) using the PF-VIKOR method for prioritizing and evaluating failure mode clusters.     

Application of fuzzy logic to explosion risk assessment

Safety and health of workers potentially being at risk from explosive atmospheres are regulated by separate regulations (ANSI/AIHA in USA and ATEX in the European Union). The ANSI/AIHA does not require risk assessment whereas it is compulsory for ATEX. There is no standard method to do that assessment. For that purpose we have applied the explosion Layer of Protection Analysis (ExLOPA), which enables semi-quantitative risk assessment for process plants where explosive atmospheres occur. The ExLOPA is based on the original work of CCPS for LOPA taking into account an explosion accident scenario at workplace. That includes typical variables appropriate for workplace explosion like occurrence of the explosive atmosphere, the presence of effective ignition sources, activity of the explosion prevention and mitigation independent protection layers as well as the severity of consequences. All those variables are expressed in the form of qualitative linguistic categories and relations between them are presented using expert based engineering knowledge, expressed in the form of appropriate set of rules. In this way the category of explosion risk may be estimated by the semi-quantitative analysis. However, this simplified method is connected with essential uncertainties providing over or under estimation of the explosion risk and may not provide real output data.In order to overcome this problem and receive more detailed quantitative results, the fuzzy logic system was applied. In the first stage called fuzzification, all linguistic categories of the variables are mapped by fuzzy sets. In the second stage, the number of relation between all variables of analysis are determined by the enumerative combinatorics and the set of the 810 fuzzy rules “IF-THEN” is received. Each rule enables determination of the fuzzy risk level for a particular accident scenario. In the last stage, called defuzzification, the crisp value of final risk is obtained using a centroid method. The final result of the risk presents a contribution of each risk category represented by the fuzzy sets (A, TA, TNA and NA) and is therefore more precise and readable than the traditional approach producing one category of risk only. Fuzzy logic gives a possibility of better insights into hazards and safety phenomena for each explosion risk scenario. It is not possible to receive such conclusions from the traditional ExLOPA calculation results. However it requires the application of computer-aided analyses which may be partially in conflict with a simplicity of ExLOPA.The practical example provides a comparison between the traditional results obtained by ExLOPA and by fuzzy ExLOPA methods.     

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.     

Fuzzy logic for piping risk assessment (pfLOPA)

This paper explores the application of the fuzzy logic for risk assessment of major hazards connected with transportation of flammable substances in long pipelines. As a basis for risk assessment, the framework of the fuzzy Layer of Protection Analysis (fLOPA) was used. fLOPA presents a new approach to risk assessment based on two assumptions: 1. different effects of the layer of protection functions on particular elements of the risks (frequency and severity of consequence), and 2. the application of fuzzy logic system (FLS) composed of three elements: fuzzification, inference process and defuzzification. A further calculation follows LOPA methodology with the use of fuzzy logic system where fuzzy risk matrix is used for risk assessment. A typical case study comprising section of a long pipeline failure is performed and a comparison between the classical LOPA approach and fuzzy approach is made.     

A fuzzy risk assessment approach for occupational hazards in the construction industry

The techniques in the construction industry have been improved due to the rapid development of science and technology. However, the constructional hazards are not decreased as expected. To reduce or prevent occupational hazards in the construction industry, a fuzzy risk assessment method was proposed to provide a prevention and improvement technique against occupational hazards. This method used two-stage quality function deployment (QFD) tables to represent the relationships among construction items, hazard types and hazard causes. A fuzzy analytic network process (ANP) method was developed to identify important hazard types and hazard causes. Failure modes and effect analysis (FMEA) was performed to assess the risk value of hazard causes based on the fuzzy inference approach. The proposed method was applied to a telecom engineering company in southern Taiwan. The performance evaluation result indicated that this method can provide satisfactory risk assessment values of hazard causes and relevant improvement strategies.     

航空公司安全系统风险的模糊综合评价研究

财航空公司安令系统风险进行科学的评价，足确保其安全运行的有效措施。针对影响航空公司安全系统风险诸多因素具有的模糊性，引入模糊综合评价理论对航空公司安全系统风险进行风险评价，构建了航空公司安全系统风险评价的指标体系，建立了基于模糊综合评价的航空公司安全系统风险评价模型。并结合实例进行了计算，结果表明该方法能有效地利用评价指标的信息，科学合理地综合评价航空公司的安全系统风险状况，具有可操作性和实用性。     

SPA–fuzzy method based real-time risk assessment for major hazard installations storing flammable gas

For monitoring and control of major hazard installations storing flammable gas, the risk based warning/early-warning is very important. A set-pair analysis (SPA) based fuzzy assessment method (SPA–fuzzy) is proposed for the real-time risk assessment in this paper. Based on principle of SPA and fuzzy logic theory, the likelihood of accident occurrence and the consequence of the accident can be assessed, and the risk value or risk degree can be evaluated. The method takes advantage of the data acquired from the real-time safety monitoring system, so that the varying of the risk can be revealed during an accident developing. The risk assessment simulation of VCE accident caused by gas leaked from LPG tank is performed. It is shown that SPA–fuzzy method has the same risk value as that assessed by normal fuzzy method.