Determination of hydrogen production in power plant using predictive machine learning methods
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Leijuan Ma
and
Kai Yuan
Abstract
With increasing global warming, the focus on sustainable and eco-friendly energy solutions is raised. The production of hydrogen is identified to be extremely important due to its role in efforts to reduce greenhouse gas emissions, hence mitigating climate change. Predicting the amount of hydrogen production accurately in this context is essential. This requires developing and/or applying complex predictive tactics to improve hydrogen production related to reliability and efficiency in thermal power plants. Hydrogen production quantification in thermal power plants using advanced predictive machine learning (ML) tactics is the focus of this exploration. The CatBoost, Random Forest (RF), Long Short-Term Memory (LSTM), and Bootstrap aggregating tactics are used to project hydrogen production. On the other hand, the Firefly algorithm is utilized to fine-tune the hyperparameters of these schemes. Predictions are made on a simulated dataset collected from the recommended power plant, and the accuracy of the schemes’ predictions is assessed using different evaluation metrics. Regarding the results, it turns out that the Firefly-CB model performs very well, having near-perfect R 2 value of 0.9764, proving the capability of predicting hydrogen with small errors. Nevertheless, the Firefly-LSTM model performs poorly with the lowest R 2 = 0.5221 and highest errors during data test. The productivity of Firefly-CB is also compared with that of other advanced optimizers and ensemble schemes, in which Firefly-CB again reveals its strength in the recommended cycle’s hydrogen prediction. These findings from the study demonstrate that hybrid ML schemes can significantly improve the prediction of hydrogen production at power stations, contributing to efficient and sustainable energy management.
Funding source: Henan Province high-tech field science and technology research project, Design and Implementation of a Digital Campus System Based on the Internet of Things
Award Identifier / Grant number: 132102210467
Acknowledgments
We would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Leijuan Ma and Kai Yuan”. Also, the first draft of the manuscript was written by Leijuan Ma. Kai Yuan commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The authors declare no competing interests.
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Research funding: This work was supported by Henan Province high-tech field science and technology research project, Design and Implementation of a Digital Campus System Based on the Internet of Things, 132102210467.
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Data availability: The authors do not have permissions to share data.
References
1. Gomiero, T. Large-scale biofuels production: a possible threat to soil conservation and environmental services. Appl Soil Ecol 2018;123:729–36. https://doi.org/10.1016/j.apsoil.2017.09.028.Search in Google Scholar
2. Huo, J, Peng, C. Depletion of natural resources and environmental quality: prospects of energy use, energy imports, and economic growth hindrances. Resour Policy 2023;86:104049. https://doi.org/10.1016/j.resourpol.2023.104049.Search in Google Scholar
3. Rosen, MA, Koohi-Fayegh, S. The prospects for hydrogen as an energy carrier: an overview of hydrogen energy and hydrogen energy systems. Energy Ecol Environ 2016;1:10–29. https://doi.org/10.1007/s40974-016-0005-z.Search in Google Scholar
4. Yue, M, Lambert, H, Pahon, E, Roche, R, Jemei, S, Hissel, D. Hydrogen energy systems: a critical review of technologies, applications, trends and challenges. Renew Sustain Energy Rev 2021;146:111180. https://doi.org/10.1016/j.rser.2021.111180.Search in Google Scholar
5. Zohuri, B. The chemical element hydrogen. In: Hydrogen energy: challenges and solutions for a cleaner future. Cham, Switzerland: Springer Cham; 2019:1–35 pp.10.1007/978-3-319-93461-7_1Search in Google Scholar
6. Ishaq, H, Dincer, I, Crawford, C. A review on hydrogen production and utilization: challenges and opportunities. Int J Hydrogen Energy 2022;47:26238–64. https://doi.org/10.1016/j.ijhydene.2021.11.149.Search in Google Scholar
7. Cetinkaya, E, Dincer, I, Naterer, GF. Life cycle assessment of various hydrogen production methods. Int J Hydrogen Energy 2012;37:2071–80. https://doi.org/10.1016/j.ijhydene.2011.10.064.Search in Google Scholar
8. Sangesaraki, AG, Gharehghani, A, Mehrenjani, JR. 4E analysis and machine learning optimization of a geothermal-based system integrated with ejector refrigeration cycle for efficient hydrogen production and liquefaction. Int J Hydrogen Energy 2023;48:31875–904. https://doi.org/10.1016/j.ijhydene.2023.04.343.Search in Google Scholar
9. Bade, SO, Tomomewo, OS, Meenakshisundaram, A, Ferron, P, Oni, BA. Economic, social, and regulatory challenges of green hydrogen production and utilization in the US: a review. Int J Hydrogen Energy 2023;49:314–35.10.1016/j.ijhydene.2023.08.157Search in Google Scholar
10. Xu, L, Wang, Y, Shah, SAA, Zameer, H, Solangi, YA, Walasai, GD, et al.. Economic viability and environmental efficiency analysis of hydrogen production processes for the decarbonization of energy systems. Processes 2019;7:494. https://doi.org/10.3390/pr7080494.Search in Google Scholar
11. Maka, AOM, Mehmood, M. Green hydrogen energy production: current status and potential. Clean Energy 2024;8:1–7. https://doi.org/10.1093/ce/zkae012.Search in Google Scholar
12. H Council. Hydrogen insights 2022 2022.Search in Google Scholar
13. Marouani, I, Guesmi, T, Alshammari, BM, Alqunun, K, Alzamil, A, Alturki, M, et al.. Integration of renewable-energy-based green hydrogen into the energy future. Processes 2023;11:2685. https://doi.org/10.3390/pr11092685.Search in Google Scholar
14. Bassey, KE, Ibegbulam, C. Machine learning for green hydrogen production. Comput Sci IT Res Journal 2023;4:368–85. https://doi.org/10.51594/csitrj.v4i3.1253.Search in Google Scholar
15. Forootan, MM, Larki, I, Zahedi, R, Ahmadi, A. Machine learning and deep learning in energy systems: a review. Sustainability 2022;14:4832. https://doi.org/10.3390/su14084832.Search in Google Scholar
16. Perera, ATD, Kamalaruban, P. Applications of reinforcement learning in energy systems. Renew Sustain Energy Rev 2021;137:110618. https://doi.org/10.1016/j.rser.2020.110618.Search in Google Scholar
17. Zhu, C, Renbo, X, Xu, H, Jian, W. A scalable and reconfigurable machine learning and deployment platform for power plant. In: IOP Conference Seriese earth and environmental science. Bristol, England: IOP Publishing; 2022:12004 p.10.1088/1755-1315/1044/1/012004Search in Google Scholar
18. Kabir, MM, Roy, SK, Alam, F, Nam, SY, Im, KS, Tijing, L, et al.. Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy. Desalination 2023;567:116992. https://doi.org/10.1016/j.desal.2023.116992.Search in Google Scholar
19. Cheng, G, Luo, E, Zhao, Y, Yang, Y, Chen, B, Cai, Y, et al.. Analysis and prediction of green hydrogen production potential by photovoltaic-powered water electrolysis using machine learning in China. Energy 2023;284:129302. https://doi.org/10.1016/j.energy.2023.129302.Search in Google Scholar
20. Javaid, A, Javaid, U, Sajid, M, Rashid, M, Uddin, E, Ayaz, Y, et al.. Forecasting hydrogen production from wind energy in a suburban environment using machine learning. Energies (Basel) 2022;15:8901. https://doi.org/10.3390/en15238901.Search in Google Scholar
21. Ozbas, EE, Aksu, D, Ongen, A, Aydin, MA, Ozcan, HK. Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms. Int J Hydrogen Energy 2019;44:17260–8. https://doi.org/10.1016/j.ijhydene.2019.02.108.Search in Google Scholar
22. Mehrenjani, JR, Gharehghani, A, Sangesaraki, AG. Machine learning optimization of a novel geothermal driven system with LNG heat sink for hydrogen production and liquefaction. Energy Convers Manag 2022;254:115266. https://doi.org/10.1016/j.enconman.2022.115266.Search in Google Scholar
23. Shakibi, H, Assareh, E, Chitsaz, A, Keykhah, S, Behrang, M, Golshanzadeh, M, et al.. Exergoeconomic and optimization study of a solar and wind-driven plant employing machine learning approaches; a case study of Las Vegas city. J Clean Prod 2023;385:135529. https://doi.org/10.1016/j.jclepro.2022.135529.Search in Google Scholar
24. Kim, JS. Covid-19 prediction and detection using machine learning algorithms: Catboost and linear regression. Am J Theor Appl Stat 2021;10:208. https://doi.org/10.11648/j.ajtas.20211005.11.Search in Google Scholar
25. Ibrahim, AA, Ridwan, RL, Muhammed, MM, Abdulaziz, RO, Saheed, GA. Comparison of the CatBoost classifier with other machine learning methods. Int J Adv Comput Sci Appl 2020;11:738–48. https://doi.org/10.14569/ijacsa.2020.0111190.Search in Google Scholar
26. Friedman, JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2001;29:1189–232. https://doi.org/10.1214/aos/1013203451.Search in Google Scholar
27. Breiman, L. Random forests. Mach Learn 2001;45:5–32. https://doi.org/10.1023/a:1010933404324.10.1023/A:1010933404324Search in Google Scholar
28. Ghosh, SK, Janan, F. Prediction of student’s performance using random forest classifier. In: Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management. Singapore: IEOM Society International; 2021:7–11 pp.10.46254/AN11.20211238Search in Google Scholar
29. Cuk, A, Bezdan, T, Jovanovic, L, Antonijevic, M, Stankovic, M, Simic, V, et al.. Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics. Sci Rep 2024;14:4309. https://doi.org/10.1038/s41598-024-54680-y.Search in Google Scholar PubMed PubMed Central
30. Breiman, L. Bagging predictors. Mach Learn 1996;24:123–40. https://doi.org/10.1007/bf00058655.Search in Google Scholar
31. Fister, I, Fister, IJr, Yang, X-S, Brest, J. A comprehensive review of firefly algorithms. Swarm Evol Comput 2013;13:34–46. https://doi.org/10.1016/j.swevo.2013.06.001.Search in Google Scholar
32. Pal, SK, Rai, CS, Singh, AP. Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. Int J Intell Syst Appl 2012;4:50. https://doi.org/10.5815/ijisa.2012.10.06.Search in Google Scholar
33. Jones, KO, Boizanté, G. Comparison of firefly algorithm optimisation, particle swarm optimisation and differential evolution. In: Proceedings of the 12th International Conference on Computer Systems and Technologies. New York, USA: Association for Computing Machinery; 2011:191–7 pp.10.1145/2023607.2023640Search in Google Scholar
34. Particle swarm optimization vs. genetic algorithms vs. simulated annealing. https://aicompetence.org/particle-swarm-optimization-vs-genetic-algorithm/?utm_source=chatgpt.com [Accessed 15 Mar 2025].Search in Google Scholar
35. Abubakar, M, Che, Y, Zafar, A, Al-Khasawneh, MA, Bhutta, MS. Optimization of solar and wind power plants production through a parallel fusion approach with modified hybrid machine and deep learning models. Intell Data AnalArticle no. 1088467X241312592, 2025.10.1177/1088467X241312592Search in Google Scholar
36. Zhao, S, Li, J, Chen, C, Yan, B, Tao, J, Chen, G. Interpretable machine learning for predicting and evaluating hydrogen production via supercritical water gasification of biomass. J Clean Prod 2021;316:128244. https://doi.org/10.1016/j.jclepro.2021.128244.Search in Google Scholar
37. Alhussan, AA, El-Kenawy, ESM, Saeed, MA, Ibrahim, A, Abdelhamid, AA, Eid, MM, et al.. Green hydrogen production ensemble forecasting based on hybrid dynamic optimization algorithm. Front Energy Res 2023;11:1221006. https://doi.org/10.3389/fenrg.2023.1221006.Search in Google Scholar
38. Kshanh, I, Tanaka, M. Evaluation of sustainable hydrogen production technologies by fuzzy AHP analysis with bootstrapping. Discov Anal 2024;2:17. https://doi.org/10.1007/s44257-024-00025-y.Search in Google Scholar
39. What is bagging? https://www.ibm.com/think/topics/bagging?utm_source=chatgpt.com [Accessed 15 Mar 2025].Search in Google Scholar
40. Wijaya, CY. Comparing model ensembling: bagging, boosting, and stacking - NBD lite #7. https://www.nb-data.com/p/comparing-model-ensembling-bagging?utm_source=chatgpt.com [Accessed 15 Mar 2025].Search in Google Scholar
41. Rout, S. Ensemble learning techniques: boosting, bagging, and stacking explained. https://www.interviewnode.com/post/ensemble-learning-techniques-boosting-bagging-and-stacking-explained?utm_source=chatgpt.com [Accessed 15 Mar 2025].Search in Google Scholar
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- Determination of hydrogen production in power plant using predictive machine learning methods
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Articles in the same Issue
- Frontmatter
- Research Articles
- Data-driven support vector regression-based hybrid models for prediction of syngas production in the gasification process of biomass
- Determination of hydrogen production in power plant using predictive machine learning methods
- Technical Note
- Response surface methodology optimization of dye adsorption by palm fatty acid distillate adsorbent
- Research Articles
- Raising pros and cons of falling film and packed column for absorption of NH3 by a NH3–H2O solution
- Predicting crude unit failures and production impact using lagging maintenance indicators in oil refineries
- Exploring the anticancer potential of some azaflavanones derivatives through molecular docking studies
- Classification of water quality based on aesthetic and chemical parameters
- Development of an optimized fractional-order controller featuring dead-time and disturbance compensation
