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Fire as a primary event of accident domino sequences: The case of BLEVE

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  • Hemmatian, Behrouz
  • Planas, Eulà lia
  • Casal, Joaquim

Abstract

The domino sequences found in major accidents have been analyzed for a large set of cases (330); the first event triggering the domino effect was an explosion or a fire, both with approximately the same contribution; the same proportion has been found when all domino effect steps were considered. Although fire effects usually reach a distance much shorter than those of an explosion, as fire is the most frequent major accident it is often found as the first step of domino sequences. This is especially true in the case of BLEVEs. Both in fixed plants and in the transportation of hazardous materials, in the event of a fire, if flames affect a vessel and the fireproofing layer has been damaged, a BLEVE can occur at any moment. 127 BLEVE accidents involving domino effect have been analyzed. It has been found that fire is significantly more frequent than explosion, both in triggering the domino effect sequence and in intermediate steps. The time to failure can range from 1min or even less up to several hours, an aspect that should be very important for the management of the emergency. A set of conclusions are inferred from this survey.

Suggested Citation

  • Hemmatian, Behrouz & Planas, Eulà lia & Casal, Joaquim, 2015. "Fire as a primary event of accident domino sequences: The case of BLEVE," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 141-148.
    • Handle: RePEc:eee:reensy:v:139:y:2015:i:c:p:141-148
    DOI: 10.1016/j.ress.2015.03.021
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    Cited by:

    1. Zhou, Jianfeng & Reniers, Genserik, 2020. "Probabilistic Petri-net addition enabling decision making depending on situational change: The case of emergency response to fuel tank farm fire," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. Li, Qilin & Wang, Yang & Chen, Wensu & Li, Ling & Hao, Hong, 2024. "Machine learning prediction of BLEVE loading with graph neural networks," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Spoelstra, Margreet & Mahesh, Soedesh & Kooi, Eelke & Heezen, Patrick, 2015. "Domino effects at LPG and propane storage sites in the Netherlands," Reliability Engineering and System Safety, Elsevier, vol. 143(C), pages 85-90.
    4. Sun, Qinying & Ma, Haiqun, 2024. "Modelling and performance analysis of the COVID-19 emergency collaborative process based on a stochastic Petri net," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Iranitalab, Amirfarrokh & Khattak, Aemal, 2020. "Probabilistic classification of hazardous materials release events in train incidents and cargo tank truck crashes," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    6. Ding, Long & Khan, Faisal & Abbassi, Rouzbeh & Ji, Jie, 2019. "FSEM: An approach to model contribution of synergistic effect of fires for domino effects," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 271-278.
    7. Martin Folch-Calvo & Francisco Brocal-Fernández & Cristina González-Gaya & Miguel A. Sebastián, 2020. "Analysis and Characterization of Risk Methodologies Applied to Industrial Parks," Sustainability, MDPI, vol. 12(18), pages 1-35, September.

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