Prediction of hydrogen production by magnetic field effect water electrolysis using artificial neural network predictive models |
| |
| Affiliation: | 1. Department of Metallurgy and Material Engineering, Faculty of Engineering-Architecture, Nev?ehir Hac? Bektas Veli University, Nev?ehir, Turkey;2. Department of Computer Engineering, Faculty of Engineering-Architecture, Nev?ehir Hac? Bektas Veli University, Nev?ehir, Turkey;3. Department of Energy Systems Engineering, Faculty of Engineering and Natural Sciences, Ankara Y?ld?r?m Beyaz?t University, Ankara, Turkey;1. Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal;2. MARETEC/DEM - Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal;3. Center IN+, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal;1. Shenyang University of Chemical Technology, Shenyang 110142, China;2. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, 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. Federal Scientific Agroengineering Center VIM, 1st Institutsky Proezd, 5, 109428, Moscow, Russia;2. Russian University of Transport, 127994, Moscow, Russia |
| |
| Abstract: | Developing an efficient water electrolysis (WE) configuration is essential for high-efficiency hydrogen evolution reaction (HER) activity. In this regard, it has been proven that adding a magnetic field (MF) to the electrolysis system greatly improves the hydrogen output rate. In this study, we developed a method based on a machine learning approach to further improve the hydrogen production (HP) system with MF effect WE. An artificial neural network (ANN) model was developed to estimate the effect of input parameters such as MF, electrode material (cathode type), electrolyte type, supplied power (onset voltage), surface area, temperature, and time on HP in different electrolyzer systems. The network was built using 104 experimental data sets from various electrolysis studies. In the study, the percentage contributions of the input parameters to the HP rate and the optimum network architecture to minimize computation time and maximize network accuracy are presented. The model architecture of 7–12–1 was obtained using the best-hidden neurons. The Levenberg-Marquardt (LM) algorithm was used to train the multi-layer feed-forward neural network. Moreover, the utilization of a range of categorical variables to improve ANN prediction accuracy is a significant novelty in this work. Results demonstrated that the output of the trained ANN model fitted well with the experimental data. The test's correlation coefficient (R) and mean squared error (MSE) were 0.973 and 0.01125, respectively, confirming its powerful predictive performance. This ANN application is the first novel viable model to perform prediction using a neural network algorithm in the electrolysis process for MF effect HP using both categorical and continuous data inputs. |
| |
| Keywords: | Hydrogen production Water electrolysis Magnetic field Artificial neural networks |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
