In this study, a fully coupled three-dimensional multiphysics CFD model of a photoelectrochemical (PEC) water splitting cell incorporating a ZnO/BiVO₄ photoanode was developed using COMSOL Multiphysics^® 6.1. The model integrates semiconductor charge transport, ionic transport (diffusion, migration, and convection), electrochemical kinetics, and fluid dynamics within a widely adopted experimental configuration. This work focuses on coupling the dominant transport phenomena governing macro-scale PEC behavior under realistic operating conditions, allowing the overall PEC behavior to be captured without resolving microscopic interfacial complexities. The simulation results show good agreement with previously reported experimental data in terms of photocurrent density, demonstrating the capability of the model to reproduce photocurrent behavior. The developed framework provides insight into the interplay between photogenerated charge transport and electrochemical reactions and can serve as a predictive tool for analyzing and optimizing PEC system performance prior to experimental implementation.

With the growth of the global population and expansion of the world economy, the global demand for energy continues to rise steadily. Most of the produced energy still relies on fossil fuels, which leads to the depletion of natural resources and environmental degradation. This situation drives active research and development in renewable energy technologies. One of the most promising directions is the electrocatalytic production of hydrogen from water, considered an efficient solution to the intermittency of renewable energy generation. The process of water electrolysis includes two half-reactions: the anodic oxygen evolution reaction (OER, 4OH− → O2 + 2H2O + 4e−) and the cathodic hydrogen evolution reaction (HER, 2H2O + 2e− → H2 + 2OH−). Under standard conditions, the equilibrium potential of water electrolysis is 1.23 V, but in practice it reaches 1.6–2.0 V due to significant charge transfer losses in the OER process. Therefore, the development of efficient and stable electrocatalysts is key for large-scale hydrogen and oxygen production, reducing energy consumption, and advancing industrial water electrolysis technologies. Among various materials, perovskite-type oxides (ABO3) are of particular interest due to their natural abundance, high chemical stability, and tunable composition. These properties make them promising high-activity catalysts for the oxygen evolution reaction. However, their practical application is limited by relatively low specific surface area, which leads to reduced catalytic activity. Keywords: Perovskite, hydrogen, photocatalysts, water splitting, renewable energy, overpotential, activity, electrochemistry.

This study presents a comprehensive computational framework that integrates physics-based modeling and data-driven approaches for analyzing and optimizing photoelectrochemical (PEC) water splitting systems. Utilizing COMSOL Multiphysics 6.1 and MATLAB, we simulate key electrochemical behaviors such as linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS). COMSOL’s multiphysics environment allows for the direct incorporation of electrolyte parameters, semiconductor photophysics, and current distribution, while MATLAB enables custom modeling of impedance behavior and predictive LSV analysis using artificial neural networks (ANNs). By coupling computational fluid dynamics (CFD), machine learning, and experimental validation, the proposed methodology provides an in-depth understanding of light-driven hydrogen generation on semiconductor electrodes such as ZnO/BiVO ₄ . Comparative analysis of simulation results shows that COMSOL and MATLAB produce consistent outputs, yet COMSOL demonstrates superior flexibility, accuracy, and ease of use, especially for systems influenced by variable physical and chemical conditions. The study further explores two-phase flow modeling, mesh independence testing, and the influence of gas bubbles on electrolyte conductivity. The findings contribute to the development of efficient, scalable PEC systems for clean hydrogen production and offer a foundation for future integration of hybrid simulation and AI techniques in renewable energy research.