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Uncertainty Handling in the Safety Risk Analysis: An Integrated Approach Based on Fuzzy Fault Tree Analysis

Uncertainty Handling in the Safety Risk Analysis: An Integrated Approach Based on Fuzzy Fault Tree Analysis
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           1 3  Journal of Failure Analysis andPrevention  ISSN 1547-7029Volume 18Number 2 J Fail. Anal. and Preven. (2018)18:392-404DOI 10.1007/s11668-018-0421-9 Uncertainty Handling in the Safety Risknalysis: An Integrated Approach Based onFuzzy Fault Tree Analysis Mohammad Yazdi & Esmaeil Zarei           1 3 Your article is protected by copyright andall rights are held exclusively by ASMInternational. This e-offprint is for personaluse only and shall not be self-archived inelectronic repositories. If you wish to self-archive your article, please use the acceptedmanuscript version for posting on your ownwebsite. You may further deposit the acceptedmanuscript version in any repository,provided it is only made publicly available 12months after official publication or later andprovided acknowledgement is given to thesrcinal source of publication and a link isinserted to the published article on Springer'swebsite. The link must be accompanied bythe following text: "The final publication isavailable at”.  TECHNICAL ARTICLE—PEER-REVIEWED Uncertainty Handling in the Safety Risk Analysis: An IntegratedApproach Based on Fuzzy Fault Tree Analysis Mohammad Yazdi  . Esmaeil Zarei Submitted: 30 October 2017/Published online: 14 February 2018   ASM International 2018 Abstract  Chemical process plants, especially the oil andgas plants operating under severe processing conditions anddealing with hazardous materials, are susceptible to catas-trophic accidents. Thus safety risk assessment is vital indesigning effective strategies for preventing and mitigatingpotentialaccidents.Faulttreeanalysis(FTA)isawell-knowntechnique to analyze the risks related to a specific system. Inthe conventional FTA, the ambiguities and uncertainties of basic events (BEs) cannot be handled effectively. Therefore,employing fuzzy set theory helps probabilistic estimation of BEs and subsequently the top event (TE). Thisstudy presentsan integrated approach to fuzzy set theory and FTA forhandling uncertainty in the risk analysis of chemical processplants. In this context, the worst case scenario based on aqualitative risk analysis is selected first and then the fuzzyFTA is established. Finally, different fuzzy aggregation anddefuzzification approaches are employed to obtain the prob-ability of each BE and TE, the output of each approach iscomparedtotheoccurrenceprobabilityofTE,andthecriticalBEs are ranked. The proposed methodology is applied to thefuzzy probabilistic analysis of hydrocarbon release in the BPtragic accident of March 2005. The results indicate that theproposed approach is very effective in risk analysis consid-ering uncertainty reduction or handling. Keywords  Uncertainty    Process plant    Fuzzy FTA   Risk analysis    BP accident Introduction Fault tree analysis (FTA) is a powerful and widely usedtechnique for evaluating safety, performance, reliabilityand risk analysis for a system. From a risk assessmentapproach, FTA can be presented as a logical block dia-gram starting from the top event (TE) and then developedfor a specific system to explore the logical relationshipbetween the causes and occurrence of basic events (BEs).FTA uses the probability of BEs and employs Booleanalgebra to determine the probability of TE [1, 2]. While dynamic fault trees (DFT) are known as the most com-mon extension of the conventional FTA, in recent years,fuzzy FTA (FFTA) has emerged as another popularextension [3]. Zadeh [4] introduced a fuzzy set theory to cope up with the data shortage in the conventionalprobability theory, which is insufficient in signifying allkinds of uncertainties based on the unpredictability of human performance as well as ambiguities of the systemphenomena [5]. Therefore, the fuzzy set theory can dealwith the uncertainties and can be considered as anextension of the conventional set theory [6, 7]. In FFTA, the assessors use linguistic terms to evaluatethe BEs in a regular way and subsequently the failurepossibility can be estimated with respect to the membershipfunction. Numerous applications of FFTA have shown thatit is capable of handling ambiguities and data shortagewhich may exist in the real safety system engineering[8–13]. In addition, ambiguity is a huge challenge to a variety of strategic decisions including FTA which mayincrease the occurrence of major accidents [14]. M. Yazdi ( & )Centre for Marine Technology and Ocean Engineering(CENTEC), Instituto Superior Te´cnico, Universidade de Lisboa,1049-001 Lisbon, Portugale-mail: Mohammad_yazdi@live.comE. ZareiDepartment of Occupational Health and Safety Engineering,Social Determinants of Health Research Center, MashhadUniversity of Medical Sciences, Mashhad, Irane-mail:  1 3 J Fail. Anal. and Preven. (2018) 18:392–404  To deal with ambiguities and the shortage of data inconventional FTA, extensive studies have been performedemploying the fuzzy set theory in different areas such asmanagement application, medical treatment, and engi-neering among others [15]. In recent years, Celik et al.[16] and Lavasani et al. [17–19] have used triangular and trapezoidal fuzzy numbers to compute the failure proba-bility (FP) of TE with respect to expert judgment indifferent chemical industries. In addition, Yazdi et al.[20, 51, 58, 67] utilized the similarity aggregation method (SAM) in terms of fuzzy set theory to compute the FP of granule storage tank. Ferdous et al. [21] used computer-aided fuzzy fault tree analysis. Shi et al. [22] studiedfuzzy FT assessment to analyze fire and explosion acci-dents for steel oil storage tanks. Furthermore, Chan andWang [23] employed FFTA for fire and explosion of crude oil tanks and Liu et al. [24] utilized fuzzy FTA inorder to prepare an emergency response planning. Apeculiar study has been performed recently, which esti-mates the risk at LPG refueling stations using anintegration of Fuzzy FTA method and expert opinionsdealing with data shortages [25].All earlier studies have used different fuzzy tools forfuzzification, aggregations and defuzzification proce-dures as per the fuzzy capabilities for their purposes.To the best of our knowledge, no study has so farcompared different fuzzy tools in the FFTA. Therefore,the objective of this study was to propose a novelapproach to perform risk analysis in a more consistentway under uncertainty conditions and employ differenttypes of fuzzy tools for the comparison of results. Inthe proposed approach, FT is used for qualitativeanalysis to identify the root cause of the hazardousevent and the fuzzy set theory along with an expert judgment is used for obtaining the failure data of BEsof the FT. The proposed methodology was applied forrisk analysis of the BP refinery accident that took placein 2005. The results thus obtained are also comparedwith the results of the BP investigation report.The main contribution of this study is provided asfollows. In next section, a new framework based onfuzzy set theory is introduced to compute the FP of TE.A numerical example is presented in subsequently toindicate the feasibility and effectiveness of the pro-posed model. Finally, the discussion and conclusion aredescribed in last section. Material and Methodology This section gives a brief overview of the methodology (infour steps) which is illustrated in Fig. 1.Hazard AnalysisThe hazard and operability study (HAZOP) is an importantstructured and systematic technique widely used to identifyand evaluate hazards (that may affect the safety perfor-mance of a system in progressive phases) in chemicalprocess plants. In other words, HAZOP is a systems ana-lytical tool for understanding how deviations from correctoperation may occur and identifying possible measures todeal with the causes [26–28]. Therefore, in the present work, HAZOP was used to identify significant potentialprocess hazards of a chemical process plant. The likelihoodand severity of the determined potential hazards wereallocated, and their related risks were specified usingAmerican Military Standard (MTL-STD–882). A qualita-tive classification of the identified risks is provided at theend.Fuzzy Probabilistic Risk AnalysisThe construction of an FT always starts with a specified TEplaced atop the tree with the rest of the tree constructeddownwards. The TE generally indicates an accident that cancause asset loss or safety hazards [29]. In order to completea tree, BEs that are denoted as the lowest level of the tree(leaves) should be known. In an FT, BEs are widely con-sidered statistically independent and exist possibly in twobinary states (failed and non-failed) and the relationshipsbetween them are commonly represented by the means of AND-gates and OR-gates [30]. Once an FT is established, itcan be analyzed quantitatively as well as qualitatively. In thequantitative evaluation, the probability of TE is demon-strated based on the occurrence probability of BEs, while inthe qualitative assessment, Boolean algebra is used to derivethe probability of TE in terms of combinations of BEs[3, 21]. To compute the probability of TE in a quantitative analysis, following conventional assumptions and mathe-matical operations can be performed: P OR  ¼  1  Y ni ¼ 1 1  P i ð Þ ð Eq 1 Þ P AND  ¼ Y ni ¼ 1 P i  ð Eq 2 Þ Three methods including extrapolation, statistical, andexpert judgment can be employed to estimate the proba-bility of BEs with unknown/limited failure data [13]. Anextrapolation technique, based on employing estimationapproach, is applied to the standard reliability data source,and a statistical technique is followed by the examinationof data in a direct way to compute the probability of anevent. The expert judgment method can be engaged toestimate the probabilities with respect to the experts’ J Fail. Anal. and Preven. (2018) 18:392–404 393  1 3  opinions. In this study, the expert judgment method, as ascientific consensus approach, was utilized to compute theprobability of BEs. Therefore, the integration of fuzzy settheory and subjective opinions to cope with any possibleambiguities can help the assessors [31]. The quantitativeanalysis of the probability of each BE and subsequently TEis an important challenge and depends highly on the qualityof expert knowledge on the specific system and the relatedaccurate data including the probability of ambiguous BEsor the interdependency between them [32].In FTA, there are many important measures available todetermine the relative importance of each BE in terms of their impact on the occurrence of TE [27, 33]. Thus, looking at the probability value of TE alone is not sufficientenough for further actions. In order to improve the safetyperformance of a system in a chemical process plant, thecritical BEs should be recognized and corresponding cor-rective measures should be implemented within a limitedspan of time and budget [34]. In this study, a structuralimportance based on ranking failure probabilities was used.The risk reduction worth (RRW), as the highest increase inthe system reliability, is obtained as a ratio of the actual TEprobability to the TE probability, when BE i is replaced withBE that can never occur [35, 36]. This analysis examines how the results of a computation or model vary as indi-vidual assumptions are changed. Furthermore, it can helpthe assessors to understand the dynamics of the system.Fuzzy Integrated ApproachesThe fuzzy set theory is used throughout, in the literaturecited, in order to aggregate the experts’ opinions to copewith the possible uncertainty in the failure data. Animportant point in the fuzzy set theory is considering howto quantify the qualitative index [37]. The subjectivityshould be avoided in order to get more reliable results.A simple averaging method having criteria such as age,personal experience, job tenure, and education level isconsidered in the literature for expert weighting, whichdoes not show a high objectivity [17–20, 38, 39]. Hazop Study MIL-STD-88213Worst risk based case accident scenario Step 1:  Hazard Analysis Establishing FTObtaining expert opinionsComputing failures  probability of TEE Step 2:  Fuzzy  probabilistic risk analysisRanking critical BEsSum-production/Max-min approachSAM/CoA approach- Step 3:  Fuzzy integrated approaches (Aggregation and defuzzification) Step 4:  Sensitivity analysisSum-production/CoA approachSAM/Max-min approach Fig. 1  Proposed framework for probabilistic risk analysis394 J Fail. Anal. and Preven. (2018) 18:392–404  1 3
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