Efficiency and exergy analysis of electrolyzer systems with artificial intelligence machine learning algorithms: A study on hydrogen production

Ahmet Elbir, Mehmet Erhan Şahin

Article ID: 11550
Vol 8, Issue 2, 2025

VIEWS - 303 (Abstract)

Abstract


This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.


Keywords


hydrogen production; water electrolysis; exergy analysis; electrolyzer efficiency; renewable energy integration; machine learning algorithms

Full Text:

PDF


References


Dincer I, Acar C. Review and evaluation of hydrogen production methods for better sustainability. International Journal of Hydrogen Energy. 2015; 40(34): 11094-11111. doi: 10.1016/j.ijhydene.2014.12.035 Al-Douri A, Groth KM. Hydrogen production via electrolysis: State-of-the-art and research needs in risk and reliability analysis. International Journal of Hydrogen Energy. 2024; 63: 775-785. doi: 10.1016/j.ijhydene.2024.03.188 Iyer RK, Prosser JH, Kelly JC, et al. Life-cycle analysis of hydrogen production from water electrolyzers. International Journal of Hydrogen Energy. 2024; 81: 1467-1478. doi: 10.1016/j.ijhydene.2024.06.355 Chung DH, Graham EJ, Paren BA, et al. Design Space for PEM Electrolysis for Cost-Effective H2 Production Using Grid Electricity. Industrial & Engineering Chemistry Research. 2024; 63(16): 7258-7270. Raji Asadabadi MJ, Balali A, Moghimi M, et al. Comparative analysis of a biomass-fueled plant coupled with solid oxide fuel cell, cascaded organic Rankine cycle and liquid hydrogen production system: Machine learning approach using multi-objective grey wolf optimizer. Fuel. 2025; 380: 133269. doi: 10.1016/j.fuel.2024.133269 Harvego EA, O’Brien JE, McKellar MG. System Evaluation and Life-Cycle Cost Analysis of a Commercial-Scale High-Temperature Electrolysis Hydrogen Production Plant. In: Proceedings of the 2012 International Mechanical Engineering Congress and Exposition (IMECE 2012). 9–15 November 2012; Houston, Texas, USA. pp. 875-884. Franco A, Giovannini C. Recent and Future Advances in Water Electrolysis for Green Hydrogen Generation: Critical Analysis and Perspectives. Sustainability. 2023; 15(24): 16917. doi: 10.3390/su152416917 Granovskii M, Dincer I, Rosen MA. Environmental and economic aspects of hydrogen production and utilization in fuel cell vehicles. Journal of Power Sources. 2006; 157(1): 411-421. doi: 10.1016/j.jpowsour.2005.07.044 Züttel A, Remhof A, Borgschulte A, et al. Hydrogen: the future energy carrier. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2010; 368(1923): 3329-3342. doi: 10.1098/rsta.2010.0113 Balat M. Potential importance of hydrogen as a future solution to environmental and transportation problems. International Journal of Hydrogen Energy. 2008; 33(15): 4013-4029. doi: 10.1016/j.ijhydene.2008.05.047 The National Research Council. The Hydrogen Economy: Opportunities, Costs, Barriers, and R&D Needs. National Academies Press; 2004. Holladay JD, Hu J, King DL, et al. An overview of hydrogen production technologies. Catalysis Today. 2009; 139(4): 244-260. doi: 10.1016/j.cattod.2008.08.039 Ni M, Leung M, Leung D. Technological development of hydrogen production by solid oxide electrolyzer cell (SOEC). International Journal of Hydrogen Energy. 2008; 33(9): 2337-2354. doi: 10.1016/j.ijhydene.2008.02.048 Bilgiç G, Öztürk B, Atasever S, et al. Prediction of hydrogen production by magnetic field effect water electrolysis using artificial neural network predictive models. International Journal of Hydrogen Energy. 2023; 48(53): 20164-20175. doi: 10.1016/j.ijhydene.2023.02.082 Ahmed R, Shehab SA, Elzeki OM, et al. An explainable AI for green hydrogen production: A deep learning regression model. International Journal of Hydrogen Energy. 2024; 83: 1226-1242. doi: 10.1016/j.ijhydene.2024.08.064 Alipour Bonab S, Waite T, et al. Machine learning-powered performance monitoring of proton exchange membrane water electrolyzers for enhancing green hydrogen production as a sustainable fuel for aviation industry. Energy Reports. 2024; 12: 2270-2282. doi: 10.1016/j.egyr.2024.08.028 Sirat A, Ahmad S, Ahmad I, et al. Integrative CFD and AI/ML-based modeling for enhanced alkaline water electrolysis cell performance for hydrogen production. International Journal of Hydrogen Energy. 2024; 83: 1120-1131. doi: 10.1016/j.ijhydene.2024.08.184 Panigrahy B, Narayan K, Ramachandra Rao B. Green hydrogen production by water electrolysis: A renewable energy perspective. Materials Today: Proceedings. 2022; 67: 1310-1314. doi: 10.1016/j.matpr.2022.09.254 Salari A, Shakibi H, Soleimanzade MA, et al. Application of machine learning in evaluating and optimizing the hydrogen production performance of a solar-based electrolyzer system. Renewable Energy. 2024; 220: 119626. doi: 10.1016/j.renene.2023.119626 Sendi M, Mersch M, Mac Dowell N. Electrolytic hydrogen production; how green must green be? iScience. 2025; 28(3): 111955. doi: 10.1016/j.isci.2025.111955 Koj JC, Zapp P, Wieland C, et al. Life cycle environmental impacts and costs of water electrolysis technologies for green hydrogen production in the future. Energy, Sustainability and Society. 2024; 14(1). doi: 10.1186/s13705-024-00497-6 Vignesh Kumar V, Madhesh K, Sanjay K, et al. A novel ensemble machine learning approach for optimizing sustainability and green hydrogen production in hybrid renewable-based organic Rankine cycle-operated proton exchange membrane electrolyser system. Renewable Energy. 2025; 242: 122369. doi: 10.1016/j.renene.2025.122369 El Jery A, Salman HM, Al-Khafaji RM, et al. Thermodynamics Investigation and Artificial Neural Network Prediction of Energy, Exergy, and Hydrogen Production from a Solar Thermochemical Plant Using a Polymer Membrane Electrolyzer. Molecules. 2023; 28(6): 2649. doi: 10.3390/molecules28062649 Glatzmaier G, Blake D, Showalter S. Assessment of Methods for Hydrogen Production Using Concentrated Solar Energy. National Renewable Energy Lab (NREL). 1998. Han J, Kamber M, Pei J, et al. Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann; 2011. Bishop CM. Pattern Recognition and Machine Learning. Springer; 2006. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer New York; 2009. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553): 436-444. doi: 10.1038/nature14539 Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. In: Proceedings of the 3rd International Conference for Learning Representations (ICLR 2015). Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016. Tang J, Guo K, Guan D, et al. A semi-vapor electrolysis technology for hydrogen generation from wide water resources. Energy & Environmental Science. 2024; 17(19): 7394-7402. doi: 10.1039/d4ee02722a Leng Y, Chen G, Mendoza AJ, et al. Solid-State Water Electrolysis with an Alkaline Membrane. Journal of the American Chemical Society. 2012; 134(22): 9054-9057. doi: 10.1021/ja302439z Badgett A, Brauch J, Thatte A, et al. Updated Manufactured Cost Analysis for Proton Exchange Membrane Water Electrolyzers. National Renewable Energy Laboratory (NREL). 2024. Lemus RG, Martínez Duart JM. Updated hydrogen production costs and parities for conventional and renewable technologies. International Journal of Hydrogen Energy. 2010; 35(9): 3929-3936. doi: 10.1016/j.ijhydene.2010.02.034 Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10); 2010. pp. 807-814. Ng A. Machine Learning Yearning: Technical Strategy for AI Engineers, in the Era of Deep Learning. Good Reads; 2017.



DOI: https://doi.org/10.24294/tse11550

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Author(s)

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.