Progress of artificial neural networks applications in hydrogen production

The demand for green energy is expanding, and it seems that hydrogen is the best option that can be produced and stored in large quantities. Hydrogen is a promising energy carrier that has various advantages compared to other energy sources. Accordingly, hydrogen is gaining significant attention as a green alternative for transportation, energy sector, and energy storage. The hydrogen-based energy system consists of four major stages: production, storage, safety, and utilization. Artificial neural networks (ANN) is effectively used in predicting optimal operational parameters for hydrogen production from different methods. This review summarizes the different hydrogen production methods. Then it discusses the progress done in the application of ANNs in hydrogen production technologies to maximize the hydrogen productivity and decreasing its cost. The coefficient of determination (R-2) and mean squared error (MSE) are used as performance criteria to evaluate the performance of the ANN applied in the different production methods. Future research recommendations and hot research topics are introduced as well.(C) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.