| Affiliation: | 1. Nev?ehir Haci Bektas Veli University, Faculty of Engineering-Architecture, Department of Metallurgy and Material Engineering, Nev?ehir 50300, Turkey;2. Nev?ehir Haci Bektas Veli University, Faculty of Engineering-Architecture, Department of Computer Engineering, Nev?ehir 50300, Turkey;1. Engineering Laboratory for Energy System Process Conversion & Emission Control Technology of Jiangsu Province, School of Energy and Mechanical Engineering, Nanjing Normal University, Nanjing 210042, China;2. Guangdong Provincial Key Laboratory of Plant Resources Biorefinery, Guangzhou 510006, China;3. Environmental and Renewable Energy Systems, Gifu University, Gifu 501-1193, Japan;4. Zhenjiang Institute for Innovation and Development, Nanjing Normal University, Zhenjiang 212050, China;1. JSC R&D Center at FGC UES, 22/3, Kashirskoye Shosse, Moscow 115201, Russia;2. LLC ITC “DonEnergoMash”, 344006, Rostov-on-Don, Suvorova St., 38a, office 13, Russia;3. LLC RPE “Donskie Technologii”, 346400, Novocherkassk, St. Mikhailovskaya, 164A, Russia;4. Federal State Budgetary Institution of Science “Federal Research Centre The Southern Scientific Centre of The Russian Academy of Sciences”, 344006, Rostov-on-Don, St. Chehova, 41, Russia;1. State Key Laboratory of Fluid Power & Mechatronic System, School of Mechanical Engineering, Zhejiang University, Hangzhou, China;2. College of Control Science and Engineering, Zhejiang University, Hangzhou, China;3. Jinhua HydroT Technology Co.Ltd, Jinhua, Zhejiang, China;4. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, China;1. College of Chemistry & Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, PR China;2. Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, PR China |
| Abstract: | 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 implemented 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 comparison with other modeling techniques, has an essential place in its field. |
