Optimization of Green Hydrogen Production in Africa Using Adaptive Artificial Evolution (AAE): Enhancing Efficiency and Sustainability in Renewable Energy Systems

There is consented efforts globally to transition to green energy as a way to combat global warming. This study investigates the application of Adaptive Artificial Evolution (AAE) techniques to enhance the production of green hydrogen focusing on optimizing electrolyzer efficiency, renewable energy inputs and minimizing energy losses. Green hydrogen produced through the electrolysis of water using renewable energy sources presents a sustainable alternative to fossil fuels, with significant potential for reducing carbon emissions and supporting energy transition goals. The integration of AAE can facilitate the optimization of electrolyzer, which are critical for efficient hydrogen production, by adapting their operational parameters in real-time based on fluctuating renewable energy inputs such as solar and wind. Moreover, the scalability and cost-effectiveness of green hydrogen production are paramount for its widespread adoption. There is need to significantly reduce the production cost and improve the efficiency of electrolyser through innovative techniques. One of such innovative technique is AAE that can help optimize performance efficiency. This review focus on optimization of green hydrogen production in Africa through the use of Adaptive Artificial Evolution. This will assist in accelerating the deployment of gren hydrogen in the continent of Africa.