Artificial Neural Network-Based Mathematical Model of Methanol Steam Reforming on the Anode of Molten Carbonate Fuel Cell Based on Experimental Research

Olaf Dybiński (), Tomasz Kurkus, Lukasz Szablowski, Arkadiusz Szczęśniak, Jaroslaw Milewski, Aliaksandr Martsinchyk and Pavel Shuhayeu
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Olaf Dybiński: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warsaw, Poland
Tomasz Kurkus: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warsaw, Poland
Lukasz Szablowski: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warsaw, Poland
Arkadiusz Szczęśniak: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warsaw, Poland
Jaroslaw Milewski: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warsaw, Poland
Aliaksandr Martsinchyk: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warsaw, Poland
Pavel Shuhayeu: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 21/25 Nowowiejska Street, 00-665 Warsaw, Poland

Energies, 2025, vol. 18, issue 11, 1-17

Abstract: The article describes a mathematical model of methanol steam reforming taking place at the anode of a molten carbonate fuel cell (MCFC). An artificial neural network with an appropriate structure was subjected to a learning process on the data obtained during an experiment on the laboratory stand for testing high-temperature fuel cells located at the Institute of Heat Engineering of the Warsaw University of Technology. The backpropagation of error method was used to train the neural network. The training data included the results of methanol steam reforming in the fuel cell for steam-to-carbon ratios of 2:1, 3:1, and 4:1. The artificial neural network was then asked to generate results for other steam-to-carbon ratios. As a result, the artificial neural network predicted that the highest power density for a molten carbonate fuel cell working on methanol would be obtained with a steam-to-carbon ratio of 2.8:1. The article’s key achievement is the application of artificial intelligence to calculate an unusual steam-to-carbon ratio for the methanol steam reforming process occurring directly at the anode of an MCFC fuel cell. The solution proposed in the article contributed to reducing the number of experimental studies.

Keywords: artificial neural networks; steam reforming; internal reforming; methanol; molten carbonate fuel cell (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: 2025
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