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

Zofia Pizoń, Shinji Kimijima and Grzegorz Brus ()
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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

Energies, 2024, vol. 17, issue 10, 1-15

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).

Keywords: methane steam reforming; hydrogen; deep learning; reaction kinetics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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