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Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model

Author

Listed:
  • Yue Su

    (Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China)

  • Jingfa Li

    (School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing 102617, China)

  • Wangyi Guo

    (School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing 102617, China)

  • Yanlin Zhao

    (Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China)

  • Jianli Li

    (School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing 102617, China)

  • Jie Zhao

    (School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing 102617, China)

  • Yusheng Wang

    (PetroChina Planning and Engineering Institute, Beijing 100083, China)

Abstract

It is economical and efficient to use existing natural gas pipelines to transport hydrogen. The fast and accurate prediction of mixing uniformity of hydrogen injection in natural gas pipelines is important for the safety of pipeline transportation and downstream end users. In this study, the computational fluid dynamics (CFD) method was used to investigate the hydrogen injection process in a T-junction natural gas pipeline. The coefficient of variation (COV) of a hydrogen concentration on a pipeline cross section was used to quantitatively characterize the mixing uniformity of hydrogen and natural gas. To quickly and accurately predict the COV, a deep neural network (DNN) model was constructed based on CFD simulation data, and the main influencing factors of the COV including flow velocity, hydrogen blending ratio, gas temperature, flow distance, and pipeline diameter ratio were taken as input nodes of the DNN model. In the model training process, the effects of various parameters on the prediction accuracy of the DNN model were studied, and an accurate DNN architecture was constructed with an average error of 4.53% for predicting the COV. The computational efficiency of the established DNN model was also at least two orders of magnitude faster than that of the CFD simulations for predicting the COV.

Suggested Citation

  • Yue Su & Jingfa Li & Wangyi Guo & Yanlin Zhao & Jianli Li & Jie Zhao & Yusheng Wang, 2022. "Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model," Energies, MDPI, vol. 15(22), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8694-:d:977698
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    References listed on IDEAS

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    1. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin & Yao, Jun, 2021. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).
    4. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin, 2022. "Numerical investigation on the leakage and diffusion characteristics of hydrogen-blended natural gas in a domestic kitchen," Renewable Energy, Elsevier, vol. 189(C), pages 899-916.
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    Cited by:

    1. Emmanuel Ogbe & Ali Almansoori & Michael Fowler & Ali Elkamel, 2023. "Optimizing Renewable Injection in Integrated Natural Gas Pipeline Networks Using a Multi-Period Programming Approach," Energies, MDPI, vol. 16(6), pages 1-24, March.

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