Determination of hydrogen production in power plant using predictive machine learning methods
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Leijuan Ma
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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.
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Artikel in diesem Heft
- 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
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- Predicting crude unit failures and production impact using lagging maintenance indicators in oil refineries
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Artikel in diesem Heft
- 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
