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Journal of Failure Analysis and Prevention

, Volume 18, Issue 2, pp 392–404 | Cite as

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

  • Mohammad Yazdi
  • Esmaeil Zarei
Technical Article---Peer-Reviewed

Abstract

Chemical process plants, especially the oil and gas plants operating under severe processing conditions and dealing with hazardous materials, are susceptible to catastrophic accidents. Thus safety risk assessment is vital in designing effective strategies for preventing and mitigating potential accidents. Fault tree analysis (FTA) is a well-known technique to analyze the risks related to a specific system. In the 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). This study presents an integrated approach to fuzzy set theory and FTA for handling uncertainty in the risk analysis of chemical process plants. In this context, the worst case scenario based on a qualitative risk analysis is selected first and then the fuzzy FTA is established. Finally, different fuzzy aggregation and defuzzification approaches are employed to obtain the probability of each BE and TE, the output of each approach is compared to the occurrence probability of TE, and the critical BEs are ranked. The proposed methodology is applied to the fuzzy probabilistic analysis of hydrocarbon release in the BP tragic accident of March 2005. The results indicate that the proposed approach is very effective in risk analysis considering uncertainty reduction or handling.

Keywords

Uncertainty Process plant Fuzzy FTA Risk analysis BP accident 

References

  1. 1.
    S. Kabir, An overview of fault tree analysis and its application in model based dependability analysis. Expert Syst. Appl. 77, 114–135 (2017).  https://doi.org/10.1016/j.eswa.2017.01.058 CrossRefGoogle Scholar
  2. 2.
    M. Yazdi, S. Kabir, A fuzzy Bayesian network approach for risk analysis in process industries. Process Saf. Environ. Prot. 111, 507–519 (2017).  https://doi.org/10.1016/j.psep.2017.08.015 CrossRefGoogle Scholar
  3. 3.
    E. Ruijters, M. Stoelinga, Fault tree analysis: a survey of the state-of-the-art in modeling, analysis and tools. Comput. Sci. Rev. 15, 29–62 (2015).  https://doi.org/10.1016/j.cosrev.2015.03.001 CrossRefGoogle Scholar
  4. 4.
    L. Zadeh, Fuzzy sets. Inf. Control. 8, 338–353 (1965).  https://doi.org/10.1109/2.53 CrossRefGoogle Scholar
  5. 5.
    K.W. Lee, F.A. Tillman, J.J. Higgins, A literature survey of the human reliability component in a man–machine system. IEEE Trans. Reliab. 37, 24–34 (1988).  https://doi.org/10.1109/24.3708 CrossRefGoogle Scholar
  6. 6.
    R. Ferdous, F. Khan, R. Sadiq, P. Amyotte, B. Veitch, Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Anal. 31, 86–107 (2011).  https://doi.org/10.1111/j.1539-6924.2010.01475.x CrossRefGoogle Scholar
  7. 7.
    R. Ferdous, F. Khan, R. Sadiq, P. Amyotte, B. Veitch, Handling data uncertainties in event tree analysis. Process Saf. Environ. Prot. 87, 283–292 (2009).  https://doi.org/10.1016/j.psep.2009.07.003 CrossRefGoogle Scholar
  8. 8.
    P.V. Suresh, A.K. Babar, V.V. Raj, Uncertainty in fault tree analysis: a fuzzy approach. Fuzzy Sets Syst. 83, 135–141 (1996).  https://doi.org/10.1016/0165-0114(95)00386-X CrossRefGoogle Scholar
  9. 9.
    J. Wang, J.B. Yang, P. Sen, Safety analysis and synthesis using fuzzy sets and evidential reasoning. Reliab. Eng. Syst. Saf. 47, 103–118 (1995).  https://doi.org/10.1016/0951-8320(94)00053-Q CrossRefGoogle Scholar
  10. 10.
    K.-Y. Cai, C. Kai-Yuan, System failure engineering and fuzzy methodology: an introductory overview. Fuzzy Sets Syst. 83, 113–133 (1996).  https://doi.org/10.1016/0165-0114(95)00385-1 CrossRefGoogle Scholar
  11. 11.
    G.-S. Liang, M.-J.J. Wang, Fuzzy fault-tree analysis using failure possibility. Microelectron. Reliab. 33, 583–597 (1993).  https://doi.org/10.1016/0026-2714(93)90326-T CrossRefGoogle Scholar
  12. 12.
    I.D. Walker, J.R. Cavallaro, Failure mode analysis for a hazardous waste clean-up manipulator. Reliab. Eng. Syst. Saf. 53, 277–290 (1996).  https://doi.org/10.1016/S0951-8320(96)00055-5 CrossRefGoogle Scholar
  13. 13.
    C. Preyssl, Safety risk assessment and management-the ESA approach. Reliab. Eng. Syst. Saf. 49, 303–309 (1995).  https://doi.org/10.1016/0951-8320(95)00047-6 CrossRefGoogle Scholar
  14. 14.
    I.L. Johansen, M. Rausand, Ambiguity in risk assessment. Saf. Sci. 80, 243–251 (2015).  https://doi.org/10.1016/j.ssci.2015.07.028 CrossRefGoogle Scholar
  15. 15.
    A. Mentes, I.H. Helvacioglu, An application of fuzzy fault tree analysis for spread mooring systems. Ocean Eng. 38, 285–294 (2011).  https://doi.org/10.1016/j.oceaneng.2010.11.003 CrossRefGoogle Scholar
  16. 16.
    M. Celik, S.M. Lavasani, J. Wang, A risk-based modelling approach to enhance shipping accident investigation. Saf. Sci. 48, 18–27 (2010).  https://doi.org/10.1016/j.ssci.2009.04.007 CrossRefGoogle Scholar
  17. 17.
    S.M. Lavasani, N. Ramzali, F. Sabzalipour, E. Akyuz, Utilisation of fuzzy fault tree analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural-gas wells. Ocean Eng. 108, 729–737 (2015).  https://doi.org/10.1016/j.oceaneng.2015.09.008 CrossRefGoogle Scholar
  18. 18.
    S.M. Lavasani, A. Zendegani, M. Celik, An extension to fuzzy fault tree analysis (FFTA) application in petrochemical process industry. Process Saf. Environ. Prot. 93, 75–88 (2015).  https://doi.org/10.1016/j.psep.2014.05.001 CrossRefGoogle Scholar
  19. 19.
    M.R. Miri Lavasani, J. Wang, Z. Yang, J. Finlay, Application of fuzzy fault tree analysis on oil and gas offshore pipelines. Int. J. Mar. Sci. Eng. 1, 29–42 (2011). http://www.sid.ir/en/VEWSSID/J_pdf/1035520110104.pdf
  20. 20.
    M. Yazdi, F. Nikfar, M. Nasrabadi, Failure probability analysis by employing fuzzy fault tree analysis. Int. J. Syst. Assur. Eng. Manag. (2017).  https://doi.org/10.1007/s13198-017-0583-y Google Scholar
  21. 21.
    R. Ferdous, F. Khan, B. Veitch, P.R. Amyotte, Methodology for computer aided fuzzy fault tree analysis. Process Saf. Environ. Prot. 87, 217–226 (2009).  https://doi.org/10.1016/j.psep.2009.04.004 CrossRefGoogle Scholar
  22. 22.
    L. Shi, J. Shuai, K. Xu, Fuzzy fault tree assessment based on improved AHP for fire and explosion accidents for steel oil storage tanks. J. Hazard. Mater. 278, 529–538 (2014).  https://doi.org/10.1016/j.jhazmat.2014.06.034 CrossRefGoogle Scholar
  23. 23.
    H.K. Chan, X. Wang, Fuzzy extent analysis for food risk assessment, in Fuzzy Hierarchical Model Risk Assess (Springer London, London, 2013), pp. 89–114.  https://doi.org/10.1007/978-1-4471-5043-5_6
  24. 24.
    Y. Liu, Z.P. Fan, Y. Yuan, H. Li, A FTA-based method for risk decision-making in emergency response. Comput. Oper. Res. 42, 49–57 (2014).  https://doi.org/10.1016/j.cor.2012.08.015 CrossRefGoogle Scholar
  25. 25.
    S. Rajakarunakaran, A. Maniram Kumar, V. Arumuga Prabhu, Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station. J. Loss Prev. Process Ind. 33, 109–123 (2015).  https://doi.org/10.1016/j.jlp.2014.11.016 CrossRefGoogle Scholar
  26. 26.
    M. Rausand, Risk Assessment: Theory, Methods, and Applications (Wiley, Hoboken, 2011)CrossRefGoogle Scholar
  27. 27.
    M. Modarres, Risk Analysis in Engineering: Techniques, Tools, and Trends (Taylor & Francis, Boca Raton, 2006)Google Scholar
  28. 28.
    B.M. Ayyub, Risk Analysis in Engineering and Economics, 2nd edn. (2014), https://books.google.com.my/books?id=71XOBQAAQBAJ
  29. 29.
    M. Haddara, F.I. Khan, L. Krishnasamy, A new methodology for risk-based availability analysis. IEEE Trans. Reliab. 57, 103–112 (2008).  https://doi.org/10.1109/TR.2007.911248 CrossRefGoogle Scholar
  30. 30.
    N. Khakzad, F. Khan, P. Amyotte, Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab. Eng. Syst. Saf. 96, 925–932 (2011).  https://doi.org/10.1016/j.ress.2011.03.012 CrossRefGoogle Scholar
  31. 31.
    C.-T. Lin, M.-J.J. Wang, Hybrid fault tree analysis using fuzzy sets fFL (X). Reliab. Eng. Syst. Saf. 58, 205–213 (1997).  https://doi.org/10.1016/S0951-8320(97)00072-0 CrossRefGoogle Scholar
  32. 32.
    H. Pan, W. Yun, Fault tree analysis with fuzzy gates. Comput. Ind. Eng. 8, 3–4 (1997).  https://doi.org/10.1016/S0360-8352(97)00195-2 Google Scholar
  33. 33.
    M. Yazdi, An extension of fuzzy improved risk graph (FIRG) and fuzzy analytical hierarchy process (FAHP) for determination of chemical complex safety integrity levels (SILs). Int. J. Occup. Saf. Ergon. (2017).  https://doi.org/10.1080/10803548.2017.1419654 Google Scholar
  34. 34.
    E. Zarei, A. Azadeh, N. Khakzad, M.M. Aliabadi, I. Mohammadfam, Dynamic safety assessment of natural gas stations using Bayesian network. J. Hazard. Mater. 321, 830–840 (2017).  https://doi.org/10.1016/j.jhazmat.2016.09.074 CrossRefGoogle Scholar
  35. 35.
    Y. Dutuit, A. Rauzy, Efficient algorithms to assess component and gate importance in fault tree analysis. Reliab. Eng. Syst. Saf. 72, 213–222 (2001).  https://doi.org/10.1016/S0951-8320(01)00004-7 CrossRefGoogle Scholar
  36. 36.
    M. Rausand, A. Hoyland, System Reliability Theory: Models, Statistical Methods, and Applications (2004), p. 664.  https://doi.org/10.1109/wescon.1996.554026.
  37. 37.
    T.J. Ross, Fuzzy Logic with Engineering Applications (2009).  https://doi.org/10.1002/9781119994374 Google Scholar
  38. 38.
    N. Ramzali, M.R.M. Lavasani, J. Ghodousi, Safety barriers analysis of offshore drilling system by employing fuzzy event tree analysis. Saf. Sci. 78, 49–59 (2015).  https://doi.org/10.1016/j.ssci.2015.04.004 CrossRefGoogle Scholar
  39. 39.
    F. Yan, K. Xu, X. Yao, Y. Li, Fuzzy Bayesian network-bow-tie analysis of gas leakage during biomass gasification. PLoS ONE 11, e0160045 (2016).  https://doi.org/10.1371/journal.pone.0160045 CrossRefGoogle Scholar
  40. 40.
    M. Gul, A.F. Guneri, A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. J. Loss Prev. Process Ind. 40, 89–100 (2016).  https://doi.org/10.1016/j.jlp.2015.11.023 CrossRefGoogle Scholar
  41. 41.
    J.J. Buckley, Fuzzy hierarchical analysis. Fuzzy Sets Syst. 17, 233–247 (1985).  https://doi.org/10.1016/0165-0114(85)90090-9 CrossRefGoogle Scholar
  42. 42.
    D.-Y. Chang, Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95, 649–655 (1996).  https://doi.org/10.1016/0377-2217(95)00300-2 CrossRefGoogle Scholar
  43. 43.
    A. Altunkaynak, M. Özger, M. Çakmakcı, Fuzzy logic modeling of the dissolved oxygen fluctuations in Golden Horn. Ecol. Model. 189, 436–446 (2005).  https://doi.org/10.1016/j.ecolmodel.2005.03.007 CrossRefGoogle Scholar
  44. 44.
    M. Mohsendokht, Risk assessment of uranium hexafluoride release from a uranium conversion facility by using a fuzzy approach. J. Loss Prev. Process Ind. 45, 217–228 (2017).  https://doi.org/10.1016/j.jlp.2017.01.004 CrossRefGoogle Scholar
  45. 45.
    Y. Duan, J. Zhao, J. Chen, G. Bai, A risk matrix analysis method based on potential risk influence: a case study on cryogenic liquid hydrogen filling system. Process Saf. Environ. Prot. 102, 277–287 (2016).  https://doi.org/10.1016/j.psep.2016.03.022 CrossRefGoogle Scholar
  46. 46.
    B.M. Ayyub, G.J. Klir, Uncertainty Modeling and Analysis in Engineering and the Sciences (2006).  https://doi.org/10.1201/9781420011456
  47. 47.
    T.L. Saaty, M.S. Ozdemir, Why the magic number seven plus or minus two. Math. Comput. Model. 38, 233–244 (2003).  https://doi.org/10.1016/S0895-7177(03)90083-5 CrossRefGoogle Scholar
  48. 48.
    J.H. Purba, J. Lu, G. Zhang, W. Pedrycz, A fuzzy reliability assessment of basic events of fault trees through qualitative data processing. Fuzzy Sets Syst. 243, 50–69 (2014).  https://doi.org/10.1016/j.fss.2013.06.009 CrossRefGoogle Scholar
  49. 49.
    K.T. Atanassov, On the Concept of Intuitionistic Fuzzy Sets (2012), pp. 1–16.  https://doi.org/10.1007/978-3-642-29127-2_1
  50. 50.
    A.S. Markowski, M.S. Mannan, Fuzzy risk matrix. J. Hazard. Mater. 159, 152–157 (2008).  https://doi.org/10.1016/j.jhazmat.2008.03.055 CrossRefGoogle Scholar
  51. 51.
    M. Yazdi, The application of bow-tie method in hydrogen sulfide risk management using layer of protection analysis (LOPA). J. Fail. Anal. Prev. 17, 291–303 (2017).  https://doi.org/10.1007/s11668-017-0247-x CrossRefGoogle Scholar
  52. 52.
    A. Mardani, A. Jusoh, E.K. Zavadskas, Fuzzy multiple criteria decision-making techniques and applications—two decades review from 1994 to 2014. Expert Syst. Appl. 42(2015), 4126–4148 (2015).  https://doi.org/10.1016/j.eswa.2015.01.003 CrossRefGoogle Scholar
  53. 53.
    R. Ferdous, F. Khan, R. Sadiq, P. Amyotte, B. Veitch, Analyzing system safety and risks under uncertainty using a bow-tie diagram: an innovative approach. Process Saf. Environ. Prot. 91, 1–18 (2013).  https://doi.org/10.1016/j.psep.2011.08.010 CrossRefGoogle Scholar
  54. 54.
    W. Pedrycz, Why triangular membership functions? Fuzzy Sets Syst. 64, 21–30 (1994).  https://doi.org/10.1016/0165-0114(94)90003-5 CrossRefGoogle Scholar
  55. 55.
    F. Aqlan, E. Mustafa, Ali, Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry. J. Loss Prev. Process Ind. 29, 39–48 (2014).  https://doi.org/10.1016/j.jlp.2014.01.006 CrossRefGoogle Scholar
  56. 56.
    A. Ishikawa, M. Amagasa, T. Shiga, G. Tomizawa, R. Tatsuta, H. Mieno, The max–min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets Syst. 55, 241–253 (1993).  https://doi.org/10.1016/0165-0114(93)90251-C CrossRefGoogle Scholar
  57. 57.
    Hsi-Mei Hsu, Chen-Tung Chen, Aggregation of fuzzy opinions under group decision making. Fuzzy Sets Syst. 79, 279–285 (1996).  https://doi.org/10.1016/0165-0114(95)00185-9 CrossRefGoogle Scholar
  58. 58.
    M. Yazdi, S. Daneshvar, H. Setareh, An extension to fuzzy developed failure mode and effects analysis (FDFMEA) application for aircraft landing system. Saf. Sci. 98, 113–123 (2017).  https://doi.org/10.1016/j.ssci.2017.06.009 CrossRefGoogle Scholar
  59. 59.
    A. Kaufmann, M.M. Gupta, Introduction to Fuzzy Arithmetic: Theory and Applications (Van Nostrand Reinhold Co, New York, 1985)Google Scholar
  60. 60.
    T. Onisawa, An application of fuzzy concepts to modelling of reliability analysis. Fuzzy Sets Syst. 37, 267–286 (1990).  https://doi.org/10.1016/0165-0114(90)90026-3 CrossRefGoogle Scholar
  61. 61.
    CSB, Anatomy of a disaster, in Safety Videos 2005–2008 (2008)Google Scholar
  62. 62.
    CSB, Investigation report: refinery explosion and fire, BP Texas city incident final investigation report (2007)Google Scholar
  63. 63.
  64. 64.
    F.I. Khan, P.R. Amyotte, Modeling of BP Texas City refinery incident. J. Loss Prev. Process Ind. 20, 387–395 (2007).  https://doi.org/10.1016/j.jlp.2007.04.037 CrossRefGoogle Scholar
  65. 65.
    M. Kalantarnia, F. Khan, K. Hawboldt, Modelling of BP Texas City refinery accident using dynamic risk assessment approach. Process Saf. Environ. Prot. 88, 191–199 (2010).  https://doi.org/10.1016/j.psep.2010.01.004 CrossRefGoogle Scholar
  66. 66.
    X. Yang, W.J. Rogers, M.S. Mannan, Uncertainty reduction for improved mishap probability prediction: application to level control of distillation unit. J. Loss Prev. Process Ind. 23, 149–156 (2010).  https://doi.org/10.1016/j.jlp.2009.07.006 CrossRefGoogle Scholar
  67. 67.
    M. Yazdi, Hybrid probabilistic risk assessment using fuzzy FTA and fuzzy AHP in a process industry. J. Fail. Anal. Prev. 17, 756–764 (2017).  https://doi.org/10.1007/s11668-017-0305-4 CrossRefGoogle Scholar
  68. 68.
    I. Mohammadfam, E. Zarei, Safety risk modeling and major accidents analysis of hydrogen and natural gas releases: a comprehensive risk analysis framework. Int. J. Hydrogen Energy 40, 13653–13663 (2015).  https://doi.org/10.1016/j.ijhydene.2015.07.117 CrossRefGoogle Scholar
  69. 69.
    E. Zarei, A. Azadeh, M.M. Aliabadi, I. Mohammadfam, Dynamic safety risk modeling of process systems using bayesian network. Process Saf. Prog. (2017).  https://doi.org/10.1002/prs.11889 Google Scholar

Copyright information

© ASM International 2018

Authors and Affiliations

  1. 1.Centre for Marine Technology and Ocean Engineering (CENTEC)Instituto Superior Técnico, Universidade de LisboaLisbonPortugal
  2. 2.Department of Occupational Health and Safety Engineering, Social Determinants of Health Research CenterMashhad University of Medical SciencesMashhadIran

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