Recent advances in artificial neural network research for modeling hydrogen production processes

Artificial Neural Networks (ANN) have been widely used by scientists in a variety of energy modes (biomass, wind, solar, geothermal, and hydroelectric). This review highlights the assistance of ANN for researchers in the quest for discovering more advanced materials/ processes for efficient hydrogen production (HP). The review is divided into two parts in this context. The first section briefly mentions, in terms of technologies, economy, energy consumption, and costs symmetrically outlined the advantages and disadvantages of various HP routes such as fossil fuel/biomass conversion, water electrolysis, microbial fermentation, and photocatalysis. Subsequently, ANN and ANN hybrid studies imple-mented in HP research were evaluated. Finally, statistics of hybrid studies with ANN are given, and future research proposals and hot research topics are briefly discussed. This research, which touches upon the types of ANNs applied to HP methods and their com-parison with other modeling techniques, has an essential place in its field. & COPY; 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.