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Bayesian Network Analysis of Explosion Events at Petrol Stations

  • Guowei MaEmail author
  • Yimiao Huang
  • Jingde Li
Chapter
  • 253 Downloads

Abstract

This chapter illustrates a Bayesian-network-based quantitative risk analysis method for VCE accidents at small oil and gas facilities, such as petrol stations. Meanwhile, to reduce uncertainties by data shortage, three types of data, i.e. practical information, computational simulations and subjective judgements are introduced to quantify the proposed BN. A case study using the proposed method to model the complete explosion process is presented.

References

  1. AcuTech Consulting Group. (2014). A risk analysis/hazard assessment of high ethanol content fuels at service station, CRC Project No. CM-138-12-1, Alpharetta, GA, USA.Google Scholar
  2. Technologies, A. R. I. A. (2009). Petrol station accidents France, 1958–2007. France: Boulogne-Billancourt.Google Scholar
  3. Asumadu-Sarkodie, S., Owusu, P. A., & Rufangura, P. (2015). Impact analysis of flood in Accra, Ghana. Advances in Applied Science Research, 6(9), 53–78.Google Scholar
  4. Department of Minerals and Energy. (1996; 1998–2000). Explosives and Dangerous Goods Act 1961 summary of accident reports 1996; 1998–2000. Government of Western Australia, Australia. Google Scholar
  5. Department of Consumer and Employment Protection. (2000; 2002; 2004; 2006). Dangerous good incidents logs 2000; 2002; 2004; 2006. Australia: Government of Western Australia.Google Scholar
  6. Department of Mines and Petroleum. (2008). Overview of dangerous goods incident reports 2008. Australia: Government of Western Australia.Google Scholar
  7. Department of Mines and Petroleum. (2009a). Overview of dangerous goods incident reports 2009. Australia: Government of Western Australia.Google Scholar
  8. Department of Mines and Petroleum. (2009b). Fuel tanker fire at Maddington 15 May 2009. Australia: Government of Western Australia.Google Scholar
  9. Department of Mines and Petroleum. (2010). Overview of dangerous goods incident reports 2010. Australia: Government of Western Australia.Google Scholar
  10. Department of Mines and Petroleum. (2011–2015). Overview of dangerous goods reportable situations and incidents 2011. Government of Western Australia, Australia.Google Scholar
  11. Dnv, G. L. (2016). PHAST tutorial manual. London, UK: DNV GL software.Google Scholar
  12. Evarts, B. (2011). Fires at U.S. service stations, National Fire Protection Association, Quincy, MA, USA.Google Scholar
  13. Federal Emergency Management Agency. (1990). Handbook of chemical hazard analysis procedures. USA: Department of Transportaion.Google Scholar
  14. Haugom, G. P., & Friis-Hansen, P. (2011). Risk modelling of a hydrogen refuelling station using Bayesian network. International Journal of Hydrogen Energy, 36(3), 2389–2397.CrossRefGoogle Scholar
  15. HSE. (2015). Offshore Statistics & Regulatory Activity Report 2015. United Kingdom: Health and Safety Executive.Google Scholar
  16. Huang, Y., Ma, G., Li, J., & Hao, H. (2015). Confidence-based quantitative risk analysis for offshore accidental hydrocarbon release events. Journal of Loss Prevention in the Process Industries, 35, 117–124.CrossRefGoogle Scholar
  17. Khakzad, N., Khan, F., & Amyotte, P. (2011). Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliability Engineering & System Safety, 96(8), 925–932.CrossRefGoogle Scholar
  18. Lobato, J., Rodríguez, J., Jiménez, C., Llanos, J., Nieto-Márquez, A., & Inarejos, A. (2009). Consequence analysis of an explosion by simple models: Texas refinery gasoline explosion case. Afinidad, 66(543), 372–279.Google Scholar
  19. Nielsen, T. D., & Jensen, F. V. (2009). Bayesian networks and decision graphs. Springer Science & Business Media.Google Scholar
  20. Norazahar, N., Khan, F., Veitch, B., & MacKinnon, S. (2017). Prioritizing safety critical human and organizational factors of EER systems of offshore installations in a harsh environment. Safety Science, 95, 171–181.CrossRefGoogle Scholar
  21. Pasman, H., & Rogers, W. (2013). Bayesian networks make LOPA more effective, QRA more transparent and flexible, and thus safety more definable! Journal of Loss Prevention in the Process Industries, 26(3), 434–442.CrossRefGoogle Scholar
  22. Uijt, P., & Ale, B. (2005). Guidelines for quantitative risk assessment. VROM: Ministerie van Verkeer en Waterstaat.Google Scholar
  23. Van Wingerden, K., Van Den Berg, B., Van Leeuwen, D., Mercx, P., & Van Wees, R. (1994). Guidelines for Evaluating the Characteristics of Vapor Cloud Explosions, Flash Fires, and BLEVES. Center for Chemical Process Safety, American Institute of Chemical Engineers, ISBN 0-8169-0474-x, New York, NY.Google Scholar
  24. Wu, J., Zhou, R., Xu, S., & Wu, Z. (2017). Probabilistic analysis of natural gas pipeline network accident based on Bayesian network. Journal of Loss Prevention in the Process Industries, 46, 126–136.CrossRefGoogle Scholar
  25. Xin, P., Khan, F., & Ahmed, S. (2017). Dynamic hazard identification and scenario mapping using Bayesian network. Process Safety and Environmental Protection, 105, 143–155.CrossRefGoogle Scholar
  26. Zarei, E., Azadeh, A., Khakzad, N., Aliabadi, M. M., & Mohammadfam, I. (2017). Dynamic safety assessment of natural gas stations using Bayesian network. Journal of Hazardous Materials, 321, 830–840.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Civil and Transportation EngineeringHebei University of TechnologyTianjinChina
  2. 2.Department of Civil, Environmental and Mining Engineering, School of EngineeringUniversity of Western AustraliaPerthAustralia
  3. 3.Centre for Infrastructural Monitoring and ProtectionCurtin UniversityPerthAustralia

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