Optimizing Molten Carbonate Electrolysis for sustainable fuel production: Experimental insights and machine learning enhancements | ||||
| Journal of Engineering Advances and Technologies for Sustainable Applications | ||||
| Volume 1, Issue 1, January 2025, Page 31-36 PDF (377.04 K) | ||||
| Document Type: Original research paper | ||||
| DOI: 10.21608/jeatsa.2025.427798 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Jaroslaw Milewski  ; A. Martsinchyk
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| Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Warsaw, Poland | ||||
| Abstract | ||||
| 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. | ||||
| Keywords | ||||
| Molten Carbonate Electrolysis; MCE; syngas generation; experimental investigation; Machine Learning; Artificial Neural Networks; ANN | ||||
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