Dynamics of Gas Generation in Porous Electrode Alkaline Electrolysis Cells: An Investigation and Optimization Using Machine Learning
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
- Damien Le Bideau & Philippe Mandin & Mohamed Benbouzid & Myeongsub Kim & Mathieu Sellier & Fabrizio Ganci & Rosalinda Inguanta, 2020. "Eulerian Two-Fluid Model of Alkaline Water Electrolysis for Hydrogen Production," Energies, MDPI, vol. 13(13), pages 1-14, July.
- Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
- Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
- El-Askary, W.A. & Sakr, I.M. & Ibrahim, K.A. & Balabel, A., 2015. "Hydrodynamics characteristics of hydrogen evolution process through electrolysis: Numerical and experimental studies," Energy, Elsevier, vol. 90(P1), pages 722-737.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Mohamed-Amine Babay & Mustapha Adar & Ahmed Chebak & Mustapha Mabrouki, 2024. "Comparative sustainability analysis of serpentine flow-field and straight channel PEM fuel cell designs," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3954-3970, August.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
- Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
- Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
- Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
- Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
- Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
- Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
- Miguel G. Folgado & Veronica Sanz, 2022. "Exploring the political pulse of a country using data science tools," Journal of Computational Social Science, Springer, vol. 5(1), pages 987-1000, May.
- Aryanpur, Vahid & Fattahi, Mahshid & Mamipour, Siab & Ghahremani, Mahsa & Gallachóir, Brian Ó & Bazilian, Morgan D. & Glynn, James, 2022. "How energy subsidy reform can drive the Iranian power sector towards a low-carbon future," Energy Policy, Elsevier, vol. 169(C).
- Kubitza, Dennis Oliver & Weßling, Katarina, 2025. "Whole Lotta Training - Studying School-to-Training Transitions by Training Artificial Neural Networks," EconStor Preprints 310974, ZBW - Leibniz Information Centre for Economics.
- Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
- Veronika Groma & Endre Börcsök & Christian Oltra & Chiara Bustreo & Adrián T. Terjék & János Osán, 2024. "Evaluation of Energy-Production Preferences Using ANP Methodology Based on a Comprehensive Residential Survey," Energies, MDPI, vol. 17(15), pages 1-15, July.
- Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
- Gulay, Emrah & Sen, Mustafa & Akgun, Omer Burak, 2024. "Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models," Energy, Elsevier, vol. 286(C).
- Gabriela Elena Badea & Cristina Hora & Ioana Maior & Anca Cojocaru & Calin Secui & Sanda Monica Filip & Florin Ciprian Dan, 2022. "Sustainable Hydrogen Production from Seawater Electrolysis: Through Fundamental Electrochemical Principles to the Most Recent Development," Energies, MDPI, vol. 15(22), pages 1-31, November.
- Wadim Strielkowski & Svetlana Zenchenko & Anna Tarasova & Yana Radyukova, 2022. "Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
- Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
- Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
- Liu, Xiaomei & Li, Sihan & Gao, Meina, 2024. "A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast," Energy, Elsevier, vol. 296(C).
- Giovanni Franco, 2024. "The return of non-probability sample: the electoral polls at the time of internet and social media," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3811-3830, August.
More about this item
Keywords
alkaline water electrolysis; hydrogen; bubble dispersion; ANN; ensembled tree model; MATLAB; COMSOL;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5365-:d:1193971. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.