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Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Author

Listed:
  • Zofia Pizoń

    (Department of Fundamental Research in Energy Engineering, Faculty of Energy and Fuel, AGH University of Krakow, 30059 Mickiewicza Ave., 30-059 Krakow, Poland)

  • Shinji Kimijima

    (Department of Machinery and Control Systems, Faculty of Mechanical Engineering, Shibaura Institute of Technology, Tokyo 135-8548, Japan)

  • Grzegorz Brus

    (Department of Fundamental Research in Energy Engineering, Faculty of Energy and Fuel, AGH University of Krakow, 30059 Mickiewicza Ave., 30-059 Krakow, Poland)

Abstract

Methane steam reforming is the foremost method for hydrogen production, and it has been studied through experiments and diverse computational models to enhance its energy efficiency. This study focuses on employing an artificial neural network as a model of the methane steam reforming process. The proposed data-driven model predicts the output mixture’s composition based on reactor operating conditions, such as the temperature, steam-to-methane ratio, nitrogen-to-methane ratio, methane flow, and nickel catalyst mass. The network, a feedforward type, underwent training with a comprehensive dataset augmentation strategy that augments the primary experimental dataset through interpolation and theoretical simulations of the process, ensuring a robust model training phase. Additionally, it introduces weights to evaluate the relative significance of different data categories (experimental, interpolated, and theoretical) within the dataset. The optimal artificial neural network architecture was determined by evaluating various configurations, with the aim of minimizing the mean squared error (0.00022) and maximizing the Pearson correlation coefficient (0.97) and Spearman correlation coefficient (1.00).

Suggested Citation

  • Zofia Pizoń & Shinji Kimijima & Grzegorz Brus, 2024. "Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques," Energies, MDPI, vol. 17(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2413-:d:1396655
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    References listed on IDEAS

    as
    1. Marcin Pajak & Grzegorz Brus & Shinji Kimijima & Janusz S. Szmyd, 2023. "Enhancing Hydrogen Production from Biogas through Catalyst Rearrangements," Energies, MDPI, vol. 16(10), pages 1-21, May.
    2. Wang, Yang & Wu, Chengru & Zhao, Siyuan & Wang, Jian & Zu, Bingfeng & Han, Minfang & Du, Qing & Ni, Meng & Jiao, Kui, 2022. "Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell," Applied Energy, Elsevier, vol. 315(C).
    3. Vo, Nguyen Dat & Oh, Dong Hoon & Hong, Suk-Hoon & Oh, Min & Lee, Chang-Ha, 2019. "Combined approach using mathematical modelling and artificial neural network for chemical industries: Steam methane reformer," Applied Energy, Elsevier, vol. 255(C).
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