Analysis and Prediction of Hydrogen Yield Using Artificial Neural Networks

Hydrogen is an efficient and clean energy carrier with vast promise in renewable energy systems. Precise prediction and analysis of hydrogen production from different production processes—e.g., biomass gasification, water electrolysis, and reforming—are critical in optimizing operational efficiency and upscaling renewable hydrogen technologies. This research investigates the application of Artificial Neural Networks (ANNs) for modeling and predicting hydrogen yield from important process parameters such as temperature, pressure, feedstock composition, catalyst type, and reaction time. A large dataset was developed from experimental data and literature reports, which was utilized to train and validate an ANN multi-layer feedforward model. The model yielded high prediction performance, with excellent agreement between predicted and actual yields, and outperformed conventional regression methods. Sensitivity analysis identified the most significant variables impacting hydrogen output, providing guidance for optimizing processes. The results indicate the efficacy of ANNs in modeling intricate nonlinear interactions in hydrogen production systems and justify their use as sound decision-making and control tools in green hydrogen generation technologies.