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Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach

Author

Listed:
  • Ali Taghi-Molla

    (University of Tehran)

  • Masoud Rabbani

    (University of Tehran)

  • Mohammad Hosein Karimi Gavareshki

    (Malek Ashtar University of Technology)

  • Ehsan Dehghani

    (Iran University of Science and Technology
    National Elites Foundation of Iran)

Abstract

The risk of accidents at workplaces, particularly in the sensitive locations with unsafe behaviors, have increased substantially, needing to be managed accurately. To ameliorate the safety in such systems, enhancing the integrated resilience engineering and macro-ergonomics concepts is of pivotal importance. In this sense, this paper unveils a novel method based on Bayesian network and artificial neural network models to enhance safety of such systems considering both mentioned concepts. Exploiting the Bayesian network, the effects of the indicators on the system safety efficiency is evaluated according to the expert’s opinions. The Artificial neural network examines these effects based on the operator’s opinions. Thereinafter, to decrease the uncertainty and bias of results and also augment the robustness and accuracy of them, the combination of the results of these models is considered as the final criterion. For analyzing the efficacy of the proposed method, a case study in a gas refinery in Ilam, Iran is conducted. The results corroborate the validity and efficacy of the proposed method and draw outstanding managerial insights.

Suggested Citation

  • Ali Taghi-Molla & Masoud Rabbani & Mohammad Hosein Karimi Gavareshki & Ehsan Dehghani, 2020. "Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 641-654, June.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:3:d:10.1007_s13198-020-00968-x
    DOI: 10.1007/s13198-020-00968-x
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    Cited by:

    1. Rachid Ouache & Gyan Chhipi-Shrestha & Kasun Hewage & Rehan Sadiq, 2021. "An integrated risk assessment and prediction framework for fire ignition sources in smart-green multi-unit residential buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1262-1295, December.
    2. Mottahedi, Adel & Sereshki, Farhang & Ataei, Mohammad & Qarahasanlou, Ali Nouri & Barabadi, Abbas, 2021. "Resilience estimation of critical infrastructure systems: Application of expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Samieinasab, Mina & Hamid, Mahdi & Rabbani, Masoud, 2022. "An integrated resilience engineering-lean management approach to performance assessment and improvement of clinical departments," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).

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