Artificial Intelligence (AI)-Enhanced Sustainable Hydrogen Production Employing Microbial Electrolysis Cells

The urgent global need for sustainable and low-carbon energy solutions has highlighted hydrogen production from wastewater and waste activated sludge as a promising pathway to achieve both energy recovery and environmental remediation. This study investigates microbial electrolysis cells (MECs) as a bioelectrochemical technology for converting organic matter containing in wastewater and sludge into bio-hydrogen, while simultaneously reducing chemical oxygen demand (COD) and mitigating related pollution risks. The research aims to evaluate performance efficiency, hydrogen yield, and system scalability, and also to provide a framework for integrating MECS with hybrid renewable energy generation systems. A combination of experimental analyses and artificial intelligence-based optimization was employed to enhance the performance. MEC experiments focused on key operational parameters, including applied voltage, hydraulic retention time, substrate composition, and pH value, to maximize hydrogen production while ensuring effective treatment of organic contaminants. Integration scenarios were modeled to assess coupling with intermittent renewable energy sources for reliable energy supply in off-grid or decentralized industrial applications. Results demonstrate that optimized MECS can achieve high hydrogen yields from wastewater and sludge, providing dual environmental and energy benefits. AI-driven predictive modeling enables real-time process control, performance optimization, and scalable deployment, improving efficiency and reducing costs. Collectively, these findings highlight the potential of MECs combined with intelligent control strategies to transform wastewater and sludge into a renewable energy source, offering a sustainable solution for decentralized energy production, pollution reduction, and support of low-carbon energy transition goals.

کلیدواژه ها:

Microbial electrolysis cell ، hydrogen production ، artificial intelligence ، Sustainability ، Decarbonization