Modeling and Optimization of Alkaline Water Electrolyzer for Green Hydrogen Production in UAE

Water electrolysis is an important pathway for producing hydrogen from renewable energy sources, with alkaline water electrolysis widely applied due to its technological maturity and suitability for large-scale systems. The performance of alkaline electrolyzers is highly dependent on operating conditions, particularly current density and temperature. In this study, an alkaline electrolyzer model is developed to analyze cell voltage, stack power, hydrogen production, and efficiency under different operating conditions. The model is used to generate a dataset covering current densities from 0.05 to 1 Acm−2 and operating temperatures between 60 °C and 110 °C. An adaptive neuro-fuzzy inference system (ANFIS) is then employed to model efficiency and identify favorable operating regions. The results show that the proposed framework effectively captures efficiency trends and supports the identification of operating conditions associated with improved performance, demonstrating the benefits of ANFIS-based modeling for electrolyzer optimization.