A Fuzzy Bayesian Network–Based Method for Evaluating the Leakage Risk of STS LNG Bunkering
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 1
Abstract
As a promising clean fuel, the liquefied natural gas (LNG) has been considered as the alternative energy for ships and the LNG-fueled vessels has sharply increased in recent years. However, the leakage of LNG has become a serious challenge for the wide application of LNG-fueled vessels, especially during ship-to-ship (STS) bunkering. This study analyzes leakage accidents during STS LNG bunkering, considering its operation process, and develops a fuzzy Bayesian network model to evaluate the probability of LNG leakage during STS LNG bunkering. Sensitivity analysis is carried out to identify the critical risk factors. Moreover, the study simulates the consequence of LNG leakage using the Fire Dynamics Simulator (FDS) software and derives the F-N curve to determine the risk levels of LNG leakage considering the thermal radiation criteria. A case study of LNG-fueled vessel in Ningbo is conducted to verify the proposed method. The results show that the probability of LNG leakage and fire accidents during STS bunkering is 0.93% and respectively, which is located in the as low as reasonably practicable (ALARP) zone. Afterwards, it is found that uneven force on the ropes and fenders are the crucial factor influencing LNG bunkering. The simulation experiments demonstrate that the high-risk areas of STS LNG bunkering are 4 to 16 m away from the LNG-fueled ship.
Practical Applications
This study analyzes leakage accidents during ship-to-ship (STS) liquefied natural gas (LNG) bunkering, and develops a fuzzy Bayesian network model to evaluate the probability of LNG leakage during STS LNG bunkering. A case study of LNG-fueled vessel named XINAO in Ningbo is conducted to verify the proposed method. Moreover, the study simulates the consequence of LNG leakage using the Fire Dynamics Simulator (FDS) software and derives the frequency versus number of fatalities (F-N) curve to determine the risk levels of LNG leakage considering the thermal radiation criteria. The results show that the probability of LNG leakage and fire accidents during STS bunkering is 0.93% and respectively, which is located in the as low as reasonably practical (ALARP) zone. Then, it is found that uneven force on the ropes and fenders are the crucial factor during STS LNG bunkering. The simulation experiments demonstrate that the high-risk areas of STS LNG bunkering are 4–16 m away from the LNG-fueled ship. This paper delivers a remarkable research work providing rule-makers with an insight into the safety management during STS LNG bunkering.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is funded by the National Natural Science Foundation (52301422) and the National Natural Science Foundation (52272422), the Open Fund of National Engineering Research Center for Water Transport Safety (A2022002), and Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province (DSS20190104).
References
Aneziris, O., M. Gerbec, I. Koromila, Z. Nivolianitou, F. Pilo, and E. Salzano. 2021. “Safety guidelines and a training framework for LNG storage and bunkering at ports.” Saf. Sci. 138 (Jun): 105212. https://doi.org/10.1016/j.ssci.2021.105212.
Aneziris, O., I. Koromila, M. Gerbec, Z. Nivolianitou, and E. Salzano. 2022. “Ship-to-ship Lng bunkering: Risk assessment and safety zones.” Chem. Eng. Trans. 91 (Jun): 535–540. https://doi.org/10.3303/CET2291090.
Duan, Y., and Y. Hu. 2013. “LNG ship accident analysis and risk control.” [In Chinese.] Ship Ocean Eng. 42 (3): 191–194.
Fan, H., H. Enshaei, and S. G. Jayasinghe. 2021. “Safety philosophy and risk analysis methodology for LNG bunkering simultaneous operations (SIMOPs): A literature review.” [In Chinese.] Saf. Sci. 136 (Apr): 105150. https://doi.org/10.1016/j.ssci.2020.105150.
Fan, H., H. Enshaei, and S. G. Jayasinghe. 2022. “Dynamic quantitative risk assessment of LNG bunkering SIMOPs based on Bayesian network.” J. Ocean Eng. Sci. 8 (5): 508–526. https://doi.org/10.1016/j.joes.2022.03.004.
Fu, S., X. Yan, D. Zhang, C. Li, and E. Zio. 2016. “Framework for the quantitative assessment of the risk of leakage from LNG-fueled vessels by an event tree-CFD.” J. Loss Prev. Process Ind. 43 (Sep): 42–52. https://doi.org/10.1016/j.jlp.2016.04.008.
Fu, S., Y. Yu, J. Chen, B. Han, and Z. Wu. 2022a. “Towards a probabilistic approach for risk analysis of nuclear-powered icebreakers using FMEA and FRAM.” Ocean Eng. 260 (Mar): 112041. https://doi.org/10.1016/j.oceaneng.2022.112041.
Fu, S., Y. Yu, J. Chen, Y. Xi, and M. Zhang. 2022b. “A framework for quantitative analysis of the causation of grounding accidents in arctic shipping.” Reliab. Eng. Syst. Saf. 226 (Mar): 108706. https://doi.org/10.1016/j.ress.2022.108706.
Gavelli, F., S. G. Davis, and O. R. Hansen. 2011. “Evaluating the potential for overpressures from the ignition of an LNG vapor cloud during offloading.” J. Loss Prev. Process Ind. 24 (6): 908–915. https://doi.org/10.1016/j.jlp.2011.07.002.
Gerbec, M., and O. Aneziris. 2022. “Uncertainties in failure rates in the LNG bunkering risk assessment.” Saf. Sci. 152 (Apr): 105774. https://doi.org/10.1016/j.ssci.2022.105774.
Göksu, B., O. Yüksel, and C. Şakar. 2023. “Risk assessment of the ship steering gear failures using fuzzy-Bayesian networks.” Ocean Eng. 274 (Apr): 114064. https://doi.org/10.1016/j.oceaneng.2023.114064.
Jeong, B., B. S. Lee, P. Zhou, and S. Ha. 2018. “Determination of safety exclusion zone for LNG bunkering at fuel-supplying point.” Ocean Eng. 152 (Mar): 113–129. https://doi.org/10.1016/j.oceaneng.2018.01.066.
Jeong, B., S. Park, S. Ha, and J. Lee. 2020. “Safety evaluation on LNG bunkering: To enhance practical establishment of safety zone.” Ocean Eng. 216 (Feb): 107804. https://doi.org/10.1016/j.oceaneng.2020.107804.
Jiang, M., J. Lu, Z. Yang, and J. Li. 2020. “Risk analysis of maritime accidents along the main route of the Maritime Silk Road: A Bayesian network approach.” Marit. Policy Manage. 47 (6): 815–832. https://doi.org/10.1080/03088839.2020.1730010.
Jianhua, Z., and C. Jiacheng. 2003. “Calculation of characteristic parameters of pool fire and assessment of its thermal radiation hazard.” [In Chinese.] China Saf. Sci. J. 13 (6): 25–28.
Kong, X., W. Jiao, W. Xiang, Q. Wang, J. Cao, and L. Han. 2023. “Quantitative analysis of leakage consequences of LNG ship-to-ship bunkering based on CFD.” Energies 16 (12): 4631. https://doi.org/10.3390/en16124631.
Kuzu, A. C., E. Akyuz, and O. Arslan. 2019. “Application of fuzzy fault tree analysis (FFTA) to maritime industry: A risk analysing of ship mooring operation.” Ocean Eng. 179 (May): 128–134. https://doi.org/10.1016/j.oceaneng.2019.03.029.
Lee, S. 2020. “Quantitative risk assessment of fire and explosion for regasification process of an LNG-FSRU.” Ocean Eng. 197 (Feb): 106825. https://doi.org/10.1016/j.oceaneng.2019.106825.
Nubli, H., A. Fajri, A. R. Prabowo, and J. M. Sohn. 2022. “CFD implementation to mitigate the LNG leakage consequences: A review of explosion accident calculation on LNG-fueled ships.” Procedia Struct. Integrity 41 (Jan): 343–350. https://doi.org/10.1016/j.prostr.2022.05.040.
Pan, M., H. Ji, M. Qiu, Z. Sun, Z. Li, and Y. Zhao. 2020. “Numerical simulation study on fire of lithium-ion battery pack in electric vehicle.” [In Chinese.] J. Saf. Sci. Technol. 16 (6): 104–109.
Park, S., B. Jeong, J. Y. Yoon, and J. K. Paik. 2018. “A study on factors affecting the safety zone in ship-to-ship LNG bunkering.” Supplement, Ships Offshore Struct. 13 (S1): 312–321. https://doi.org/10.1080/17445302.2018.1461055.
Peng, Y., X. Zhao, T. Zuo, W. Wang, and X. Song. 2021. “A systematic literature review on port LNG bunkering station.” Transp. Res. Part D Transp. Environ. 91 (Mar): 102704. https://doi.org/10.1016/j.trd.2021.102704.
Shi, S., D. Zhang, Y. Su, C. Wan, M. Zhang, and C. Liu. 2019. “A fuzzy-based decision-making model for improving the carrying capacity of ship locks: A three gorges dam case.” J. Mar. Sci. Eng. 7 (8): 244. https://doi.org/10.3390/jmse7080244.
Tunçel, A. L., E. B. Beşikçi, E. Akyuz, and O. Arslan. 2023. “Safety analysis of fire and explosion (F&E) accidents risk in bulk carrier ships under fuzzy fault tree approach.” Saf. Sci. 158 (Feb): 105972. https://doi.org/10.1016/j.ssci.2022.105972.
Turna, İ. 2022. “A safety risk assessment for ship boarding parties from fuzzy Bayesian networks perspective.” In Maritime policy & management, 1–14. London: Taylor and Francis. https://doi.org/10.1080/03088839.2022.2112780.
Uflaz, E., S. I. Sezer, E. Akyuz, O. Arslan, and R. E. Kurt. 2022. “A human reliability analysis for ship to ship LNG bunkering process under D-S evidence fusion HEART approach.” J. Loss Prev. Process Ind. 80 (Dec): 104887. https://doi.org/10.1016/j.jlp.2022.104887.
Wan, C., X. Yan, D. Zhang, Z. Qu, and Z. Yang. 2019. “An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks.” Transp. Res. Part E Logist. Transp. Rev. 125 (Dec): 222–240. https://doi.org/10.1016/j.tre.2019.03.011.
Wan, C., Y. Zhao, D. Zhang, and L. Fan. 2023. “A system dynamics-based approach for risk analysis of waterway transportation in a mixed traffic environment.” Marit. Policy Manage. (Jun): 1–23. https://doi.org/10.1080/03088839.2023.2224328.
Wang, D., P. Zhang, Y. Guo, and H. Jiang. 2014. “Fuzzy fault tree quantitative analysis of LNG storage tank leakage with consideration of event interdependencies.” [In Chinese.] China Saf. Sci. J. 1 (Jan): 96–102.
Wu, J., Y. Bai, H. Zhao, X. Hu, and V. Cozzani. 2021a. “A quantitative LNG risk assessment model based on integrated Bayesian-Catastrophe-EPE method.” Saf. Sci. 137 (May): 105184. https://doi.org/10.1016/j.ssci.2021.105184.
Wu, J., J. Cai, S. Yuan, X. Zhang, and G. Reniers. 2021b. “CFD and EnKF coupling estimation of LNG leakage and dispersion.” Saf. Sci. 139 (Jul): 105263. https://doi.org/10.1016/j.ssci.2021.105263.
Xie, C., L. Huang, J. Deng, R. Wang, and G. Hao. 2022. “Hazard assessment and hazard mitigation of fuel leak inside a ship elevator for LNG-fueled vessel.” Ocean Eng. 259 (Sep): 111943. https://doi.org/10.1016/j.oceaneng.2022.111943.
Xu, S., E. Kim, S. Haugen, and M. Zhang. 2022. “A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route.” Reliab. Eng. Syst. Saf. 223 (Jul): 108475. https://doi.org/10.1016/j.ress.2022.108475.
Yang, L. P., S. N. Du, W. Q. Wang, B. Wang, and X. H. Geng. 2020. “Dynamic risk analysis of continuous leakage of LNG storage tank.” [In Chinese.] Chem. Eng. Oil Gas 49 (Mar): 109–116.
Yang, Y., Y. Gong, Y. Wang, X. Wu, Z. Zhou, W. Weng, and Y. Zhang. 2023. “Advances in air pollution control for vessels in China.” J. Environ. Sci. 123 (Jan): 212–221. https://doi.org/10.1016/j.jes.2022.03.026.
Yu, L., Y. Xi, and S. Hu. 2020. “Human error probability prediction of unloading operation on LNG carrier based on specific CREAM.” [In Chinese.] J. Mar. Sci. Eng. 43 (1): 10–17.
Zhang, J., Â. P. Teixeira, C. Guedes Soares, X. Yan, and K. Liu. 2016. “Maritime transportation risk assessment of Tianjin Port with Bayesian belief networks.” Risk Anal. 36 (6): 1171–1187. https://doi.org/10.1111/risa.12519.
Zhang, M., D. Zhang, H. Yao, and K. Zhang. 2020. “A probabilistic model of human error assessment for autonomous cargo ships focusing on human–autonomy collaboration.” Saf. Sci. 130 (Oct): 104838. https://doi.org/10.1016/j.ssci.2020.104838.
Zhang, W., X. Yan, D. Zhang, and J. Zhang. 2019. “Evaluating the probability of power loss in ship electric propulsion systems based on Bayesian belief networks.” Mar. Technol. Soc. J. 53 (3): 63–79. https://doi.org/10.4031/MTSJ.53.3.6.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 1 • March 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 1, 2023
Accepted: Oct 25, 2023
Published online: Dec 30, 2023
Published in print: Mar 1, 2024
Discussion open until: May 30, 2024
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