MACHINE LEARNING FOR GREEN HYDROGEN PRODUCTION

Abstract

Green hydrogen production, achieved through the electrolysis of water using renewable energy sources, represents a promising pathway towards sustainable energy systems. However, optimizing the electrolysis process to enhance efficiency and reduce costs remains a significant challenge. This study explores the application of machine learning (ML) techniques to develop AI-driven models that optimize the electrolysis process, thereby improving the efficiency and cost-effectiveness of green hydrogen production. Machine learning models can analyze complex datasets generated during the electrolysis process, including variables such as electricity input, water quality, temperature, pressure, and electrochemical properties. By identifying patterns and relationships within these datasets, ML algorithms can predict optimal operational conditions and provide real-time adjustments to maximize hydrogen output while minimizing energy consumption. The research focuses on the development and validation of various ML models, including regression analysis, neural networks, and reinforcement learning, to enhance the performance of the electrolysis process. These models are trained on historical data from industrial-scale electrolysis operations and laboratory experiments, ensuring robustness and reliability. Feature selection and engineering techniques are employed to isolate the most significant factors influencing efficiency and cost. Key findings demonstrate that AI-driven optimization can significantly improve the energy efficiency of hydrogen production, with potential energy savings of up to 20%. Additionally, predictive maintenance algorithms developed through machine learning can anticipate equipment failures and schedule timely maintenance, further reducing operational costs and downtime. The study also explores the integration of machine learning models with renewable energy management systems, enabling dynamic adjustments based on the availability of renewable power sources such as solar and wind. This integration ensures that the electrolysis process operates during periods of peak renewable energy generation, thereby maximizing the use of green electricity and reducing reliance on fossil fuels. Application of machine learning to green hydrogen production offers a transformative approach to optimizing the electrolysis process. AI-driven models enhance efficiency, reduce costs, and facilitate the integration of renewable energy sources, supporting the broader transition to a sustainable energy future. This research advocates for continued exploration and implementation of advanced machine-learning techniques to drive innovation in green hydrogen production. Keywords: ML, Green Hydrogen Production, AI-driven Models, Electrolysis Process, Renewable Energy Sources.