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Journal of Failure Analysis and Prevention

, Volume 17, Issue 1, pp 159–165 | Cite as

Bayesian Belief Network Used in the Chemical and Process Industry: A Review and Application

  • Hamza Zerrouki
  • Hacene Smadi
Technical Article---Peer-Reviewed

Abstract

With the increasing growth of the chemical and process industries, it is necessary to ensure the safe operation of their complex and often hazardous installations, given their proximity to residential areas. Several techniques, such as fault tree analysis (FTA), bow-tie analysis (BTA), and Bayesian belief networks (BBNs), have been developed for adequate probabilistic risk assessment and management. The current work is aimed at performing a brief statistical review of the use of Bayesian networks in the chemical and process industry within the last decade. The review reveals that Bayesian networks have been used extensively in various forms of safety and risk assessment. This trend is attributable to the complexity of the installations found in this industry and the ability of BBN to intuitively represent these complexities, handle uncertainties, and update event probabilities. The paper is concluded with an illustrative example of the use of BBN to investigate the effectiveness of the safety barriers of a gas facility.

Keywords

Bayesian belief network Safety and risk analysis Chemical industries Risk acceptance criteria 

Notes

Acknowledgments

The authors would like to thank anonymous reviewers for their comments that have allowed the improvement in this paper.

References

  1. 1.
    G.L. Reniers, Multi-Plant Safety and Security Management in the Chemical and Process Industries (WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2010)CrossRefGoogle Scholar
  2. 2.
    F.I. Khan, S. Abbasi, Techniques and methodologies for risk analysis in chemical process industries. J. Loss Prev. Process Ind. 11(4), 261–277 (1998)CrossRefGoogle Scholar
  3. 3.
    A. Bobbio, L. Portinale, M. Minichino, E. Ciancamerla, Improving the analysis of dependable systems by mapping Fault Trees into Bayesian Networks. Reliab. Eng. Syst. Saf. 71(3), 249–260 (2001)CrossRefGoogle Scholar
  4. 4.
    H. Langseth, L. Portinale, Bayesian networks in reliability. Reliab. Eng. Syst. Saf. 92(1), 92–108 (2007)CrossRefGoogle Scholar
  5. 5.
    J. G. Torres-Toledano and L. Sucar, Bayesian Networks for Reliability Analysis of Complex Systems. Prog. Artif. Intell. - IBERAMIA 98, vol. 1484, p. 465, 1998Google Scholar
  6. 6.
    N. Khakzad, G. Reniers, Application of Bayesian network and multi-criteria decision analysis to risk-based design of chemical plants. Chem. Eng. Trans. 48(January), 223–225 (2016)Google Scholar
  7. 7.
    N. Khakzad, G. Reniers, Risk-based design of process plants with regard to domino effects and land use planning. J. Hazard. Mater. 299, 289–297 (2015)CrossRefGoogle Scholar
  8. 8.
    N. Khakzad, Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliab. Eng. Syst. Saf. 138, 263–272 (2015)CrossRefGoogle Scholar
  9. 9.
    P.R. Kannan, Bayesian networks: application in safety instrumentation and risk reduction. ISA Trans. 46(2), 255–259 (2007)CrossRefGoogle Scholar
  10. 10.
    H. Zerrouki, A. Tamrabet, Safety and risk analysis of an operational heater using Bayesian network. J. Fail. Anal. Prev. 15(5), 657–661 (2015)CrossRefGoogle Scholar
  11. 11.
    Z. Chiremsel, R. Nait Said, R. Chiremsel, Probabilistic fault diagnosis of safety instrumented systems based on fault tree analysis and Bayesian network. J. Fail. Anal. Prev. 16(5), 747–760 (2016)CrossRefGoogle Scholar
  12. 12.
    S. Rathnayaka, F. Khan, P. Amyotte, Accident modeling approach for safety assessment in an LNG processing facility. J. Loss Prev. Process Ind. 25(2), 414–423 (2012)CrossRefGoogle Scholar
  13. 13.
    S. Rathnayaka, F. Khan, P. Amayotte, Accident modeling and risk assessment framework for safety critical decision-making: application to deepwater drilling operation. J. Risk Reliab. 227(1), 86–105 (2013)Google Scholar
  14. 14.
    A. Al-Shanini, A. Ahmad, F. Khan, M. Hassim, A. Al-Shatri, Modeling the impact of natural and security hazards in an LNG processing facilitity. J. Teknol. 75(6), 17–25 (2015)Google Scholar
  15. 15.
    S. Rathnayaka, F. Khan, P. Amyotte, SHIPP methodology: predictive accident modeling approach. Part I: methodology and model description. Process Saf. Environ. Prot. 89(3), 151–164 (2011)CrossRefGoogle Scholar
  16. 16.
    S. Rathnayaka, F. Khan, P. Amyotte, SHIPP methodology: predictive accident modeling approach. Part II. Validation with case study. Process Saf. Environ. Prot. 89(2), 75–88 (2010)CrossRefGoogle Scholar
  17. 17.
    N. Khakzad, S. Khakzad, F. Khan, Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico. Nat. Hazards 74, 1759–1771 (2014)CrossRefGoogle Scholar
  18. 18.
    G. Yun, W.J. Rogers, M.S. Mannan, Risk assessment of LNG importation terminals using the Bayesian-LOPA methodology. J. Loss Prev. Process Ind. 22(1), 91–96 (2009)CrossRefGoogle Scholar
  19. 19.
    P.N. Thodi, F.I. Khan, M.R. Haddara, The development of posterior probability models in risk-based integrity modeling. Risk Anal. 30(3), 400–420 (2010)CrossRefGoogle Scholar
  20. 20.
    H. Wang, F. Khan, S. Ahmed, and S. Imtiaz, Dynamic quantitative operational risk assessment of chemical processes. Chem. Eng. Sci., 142, 62–78 (2016)Google Scholar
  21. 21.
    M. Kalantarnia, F. Khan, K. Hawboldt, Dynamic risk assessment using failure assessment and Bayesian theory. J. Loss Prev. Process Ind. 22(5), 600–606 (2009)CrossRefGoogle Scholar
  22. 22.
    N. Khakzad, F. Khan, P. Amyotte, Dynamic risk analysis using bow-tie approach. Reliab. Eng. Syst. Saf. 104, 36–44 (2012)CrossRefGoogle Scholar
  23. 23.
    J. Pearl, Probabilistic reasoning in intelligent systems: Networks of plausible inference (Morgan Kaufmann Publishers, INC., San Francisco, CA, 1988)Google Scholar
  24. 24.
    P. Weber, L. Jouffe, Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliab. Eng. Syst. Saf. 91(2), 149–162 (2006)CrossRefGoogle Scholar
  25. 25.
    J. Bhandari, R. Abbassi, V. Garaniya, F. Khan, Risk analysis of deepwater drilling operations using Bayesian network. J. Loss Prev. Process Ind. 38, 11–23 (2015)CrossRefGoogle Scholar
  26. 26.
    L. Mkrtchyan, L. Podofillini, V.N. Dang, Bayesian belief networks for human reliability analysis: a review of applications and gaps. Reliab. Eng. Syst. Saf. 139, 1–5 (2015)CrossRefGoogle Scholar
  27. 27.
    P. Weber, G. Medina-Oliva, C. Simon, B. Iung, Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. 25(4), 671–682 (2012)CrossRefGoogle Scholar
  28. 28.
    N. Khakzad G. Reniers, Protecting chemical plants against terrorist attacks: a review. J. Soc. 5, 142 (2015). doi: 10.4172/2167-0358.1000142
  29. 29.
    N. Khakzad, H. Yu, N. Paltrinieri, and F. Khan, in Dynamic Risk Analysis in the Chemical and Petroleum Industry. 1st ed. Reactive Approaches of Probability Update Based on Bayesian Methods. (Elsevier, Amsterdam, 2016), pp. 51–61Google Scholar
  30. 30.
    E. De Rademaeker, G. Suter, H.J. Pasman, B. Fabiano, A review of the past, present and future of the European loss prevention and safety promotion in the process industries. Process Saf. Environ. Prot. 92(4), 280–291 (2014)CrossRefGoogle Scholar
  31. 31.
    F. Khan, S. Rathnayaka, S. Ahmed, Methods and models in process safety and risk management: past, present and future. Process Saf. Environ. Prot. 98, 116–147 (2015)CrossRefGoogle Scholar
  32. 32.
    J. Li, G. Reniers, V. Cozzani, F. Khan, A bibliometric analysis of peer-reviewed publications on domino effects in the process industry. J. Loss Prev. Process Ind. (2016). doi: 10.1016/j.jlp.2016.06.003
  33. 33.
    V. Villa, N. Paltrinieri, F. Khan, V. Cozzani, Towards dynamic risk analysis: a review of the risk assessment approach and its limitations in the chemical process industry. Saf. Sci. 89, 77–93 (2016)CrossRefGoogle Scholar
  34. 34.
    N. Khakzad, F. Khan, P. Amyotte, Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab. Eng. Syst. Saf. 96(8), 925–932 (2011)CrossRefGoogle Scholar
  35. 35.
    F.I. Khan, S. Abbasi, Reply to comments on ‘Major accidents in process industries and an analysis of causes and consequences’. J. Loss Prev. Process Ind. 14(1), 85 (1999)CrossRefGoogle Scholar
  36. 36.
    HUGIN, HUGIN Expert software version 8.1. [Online]. Available: http://www.hugin.com (2015)
  37. 37.
    F. Lees, Loss Prevention in the Process Industries, 3rd edn. (Butterworths, London, 2005)Google Scholar

Copyright information

© ASM International 2016

Authors and Affiliations

  1. 1.IHSI-LRPIUniversity of Batna 2BatnaAlgeria

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