This paper presents an in-depth investigation into Molten Carbonate Electrolysis (MCE), combining experimental research with advanced machine learning-based modeling. MCE is explored for its potential in producing hydrogen and syngas, which are critical components for sustainable energy systems. This study examines the behavior of a single molten carbonate cell under various operating conditions and employs Artificial Neural Networks (ANN) to model and optimize the electrolysis process. The findings underscore MCE's viability for fuel generation and demonstrate the effectiveness of ANN in predictive modeling and operational optimization, offering significant insights for future energy systems.
Milewski, J., & Martsinchyk, A. (2025). Optimizing Molten Carbonate Electrolysis for sustainable fuel production: Experimental insights and machine learning enhancements. Journal of Engineering Advances and Technologies for Sustainable Applications, 1(1), 31-36. doi: 10.21608/jeatsa.2025.427798
MLA
Jaroslaw Milewski; A. Martsinchyk. "Optimizing Molten Carbonate Electrolysis for sustainable fuel production: Experimental insights and machine learning enhancements", Journal of Engineering Advances and Technologies for Sustainable Applications, 1, 1, 2025, 31-36. doi: 10.21608/jeatsa.2025.427798
HARVARD
Milewski, J., Martsinchyk, A. (2025). 'Optimizing Molten Carbonate Electrolysis for sustainable fuel production: Experimental insights and machine learning enhancements', Journal of Engineering Advances and Technologies for Sustainable Applications, 1(1), pp. 31-36. doi: 10.21608/jeatsa.2025.427798
VANCOUVER
Milewski, J., Martsinchyk, A. Optimizing Molten Carbonate Electrolysis for sustainable fuel production: Experimental insights and machine learning enhancements. Journal of Engineering Advances and Technologies for Sustainable Applications, 2025; 1(1): 31-36. doi: 10.21608/jeatsa.2025.427798
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