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Maximizing Green Hydrogen Production from Water Electrocatalysis: Modeling and Optimization


The use of green hydrogen as a fuel source for marine applications has the potential to significantly reduce the carbon footprint of the industry. The development of a sustainable and cost-effective method for producing green hydrogen has gained a lot of attention. Water electrolysis is the best and most environmentally friendly method for producing green hydrogen-based renewable energy. Therefore, identifying the ideal operating parameters of the water electrolysis process is critical to hydrogen production. Three controlling factors must be appropriately identified to boost hydrogen generation, namely electrolysis time (min), electric voltage (V), and catalyst amount (µg). The proposed methodology contains the following two phases: modeling and optimization. Initially, a robust model of the water electrolysis process in terms of controlling factors was established using an adaptive neuro-fuzzy inference system (ANFIS) based on the experimental dataset. After that, a modern pelican optimization algorithm (POA) was employed to identify the ideal parameters of electrolysis duration, electric voltage, and catalyst amount to enhance hydrogen production. Compared to the measured datasets and response surface methodology (RSM), the integration of ANFIS and POA improved the generated hydrogen by around 1.3% and 1.7%, respectively. Overall, this study highlights the potential of ANFIS modeling and optimal parameter identification in optimizing the performance of solar-powered water electrocatalysis systems for green hydrogen production in marine applications. This research could pave the way for the more widespread adoption of this technology in the marine industry, which would help to reduce the industry’s carbon footprint and promote sustainability.

Funding source: The authors would like to express their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, for financing this research study under project number IF2/PSAU/2022/01/20906.
Related subjects: Production & Supply Chain

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