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Towards accident prevention on liquid hydrogen: A data-driven approach for releases prediction

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  • Alfarizi, Muhammad Gibran
  • Ustolin, Federico
  • Vatn, Jørn
  • Yin, Shen
  • Paltrinieri, Nicola

Abstract

Hydrogen is a clean substitute for hydrocarbon fuels in the marine sector. Liquid hydrogen (LH2) can be used to move and store large amounts of hydrogen. This novel application needs further study to assess the potential risk and safety operation. A recent study of LH2 large-scale release tests was conducted to replicate spills of LH2 inside the ship’s tank connection space and during bunkering operations. The tests were performed in a closed and outdoor facility. The LH2 spills can lead to detonation, representing a safety concern. This study analyzed the aforementioned LH2 experiments and proposed a novel application of the random forests algorithm to predict the oxygen phase change and to estimate whether the hydrogen concentration is above the lower flammability limit (LFL). The models show accurate predictions in different experimental conditions. The findings can be used to select reliable safety barriers and effective risk reduction measures in LH2 spills.

Suggested Citation

  • Alfarizi, Muhammad Gibran & Ustolin, Federico & Vatn, Jørn & Yin, Shen & Paltrinieri, Nicola, 2023. "Towards accident prevention on liquid hydrogen: A data-driven approach for releases prediction," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:reensy:v:236:y:2023:i:c:s0951832023001916
    DOI: 10.1016/j.ress.2023.109276
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    1. Noh, Yeelyong & Chang, Kwangpil & Seo, Yutaek & Chang, Daejun, 2014. "Risk-based determination of design pressure of LNG fuel storage tanks based on dynamic process simulation combined with Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 76-82.
    2. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Deng, Wanyi, 2023. "Determining the critical risk factors for predicting the severity of ship collision accidents using a data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Kim, Kyungmee O. & Roh, Taeseong & Lee, Jae-Woo & Zuo, Ming J., 2016. "Derating design for optimizing reliability and cost with an application to liquid rocket engines," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 13-20.
    6. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Luo, Hao & Yin, Shen, 2023. "An integrated multi-head dual sparse self-attention network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    7. Marcoulaki, E.C. & Venetsanos, A. & Papazoglou, I.A., 2017. "Quantitative safety analysis of cryogenic liquid releases in a deep underground large scale installation," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 51-63.
    8. Dinesh, A. & Benson, C.M. & Holborn, P.G. & Sampath, S. & Xiong, Y., 2020. "Performance evaluation of nitrogen for fire safety application in aircraft," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    9. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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