Machine Learning Applications in Fuel Reforming for Hydrogen Production in Marine Propulsion Systems
Year: 2025
Type: article
Abstract: In the context of the shipping industry’s transition towards low-carbon solutions, hydrogen energy exhibits substantial application potential in marine propulsion systems. Fuel reforming for hydrogen production represents one of the key technologies for efficient hydrogen production in maritime applications. Nevertheless, this process involves complex multi-scale reaction mechanisms, challenges in catalyst design, and difficulties in system optimization. This paper conducts a comprehensive review of the recent progress in the application of machine learning in fuel reforming hydrogen production technology. In the realm of catalysts, machine learning has expedited the design of efficient catalysts via high-throughput screening, performance prediction, and active site regulation. In reaction modeling, machine learning has facilitated the development of multi-scale kinetic models, enhancing the interpretability and predictive accuracy of reaction pathways. Regarding equipment and system optimization, machine learning has enabled innovations in reactor design, collaborative optimization of process parameters, and intelligent system control. This review aims to provide theoretical foundations and practical guidance for the technological development of ship propulsion systems. Moreover, it explores the future directions for the deep integration of machine learning and hydrogen energy technologies, thereby promoting the low-carbon and intelligent transformation of the shipping industry.
DOI: 10.3390/jmse14010085
Authors: Yexin Chen, Xinyu Liu, Xu Liu, Hao Lu, Ziqin Wang
Institution: Hubei University of Technology
Language: English
Subfield: Catalysis
Field: Chemical Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G7253146960, G8582892455
OpenAlex ID: W7117775651