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Process accident prediction using Bayesian network based on IT2Fs and Z-number: A case study of spherical tanks

Author

Listed:
  • Mostafa Mirzaei Aliabadi
  • Rouzbeh Abbassi
  • Omid Kalatpour
  • Omran Ahmadi
  • Vahid Ahmadi Moshiran

Abstract

This study aimed to propose a novel method for dynamic risk assessment using a Bayesian network (BN) based on fuzzy data to decrease uncertainty compared to traditional methods by integrating Interval Type-2 Fuzzy Sets (IT2FS) and Z-numbers. A bow-tie diagram was constructed by employing the System Hazard Identification, Prediction, and Prevention (SHIPP) approach, the Top Event Fault Tree, and the Barriers Failure Fault Tree. The experts then provided their opinions and confidence levels on the prior probabilities of the basic events, which were then quantified utilizing the IT2FS and combined using the Z-number to reduce the uncertainty of the prior probability. The posterior probability of the critical basic events (CBEs) was obtained using the beta distribution based on recorded data on their requirements and failure rates over five years. This information was then fed into the BN. Updating the BN allowed calculating the posterior probability of barrier failure and consequences. Spherical tanks were used as a case study to demonstrate and confirm the significant benefits of the methodology. The results indicated that the overall posterior probability of Consequences after the failure probability of barriers displayed an upward trend over the 5-year period. This rise in IT2FS-Z calculation outcomes exhibited a shallower slope compared to the IT2FS mode, attributed to the impact of experts’ confidence levels in the IT2FS-Z mode. These differences became more evident by considering the 10−4 variance compared to the 10−5. This study offers industry managers a more comprehensive and reliable understanding of achieving the most effective accident prevention performance.

Suggested Citation

  • Mostafa Mirzaei Aliabadi & Rouzbeh Abbassi & Omid Kalatpour & Omran Ahmadi & Vahid Ahmadi Moshiran, 2024. "Process accident prediction using Bayesian network based on IT2Fs and Z-number: A case study of spherical tanks," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-40, August.
    • Handle: RePEc:plo:pone00:0307883
    DOI: 10.1371/journal.pone.0307883
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    References listed on IDEAS

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    1. Md Junayed Hasan & Jong-Myon Kim, 2019. "Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm," Energies, MDPI, vol. 12(6), pages 1-14, March.
    2. Ali Azadeh & Fatemeh Rouhollah & Fatemeh Davoudpour & Iraj Mohammadfam, 2013. "Fuzzy modelling and simulation of an emergency department for improvement of nursing schedules with noisy and uncertain inputs," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 15(1), pages 58-77.
    3. Bingyi Kang & Yong Hu & Yong Deng & Deyun Zhou, 2016. "A New Methodology of Multicriteria Decision-Making in Supplier Selection Based on -Numbers," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-17, January.
    4. Qin, Jindong & Liu, Xinwang & Pedrycz, Witold, 2017. "An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment," European Journal of Operational Research, Elsevier, vol. 258(2), pages 626-638.
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