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January 1, 2025International Journal of Energy ResearchOpen Access

Optimization‐Based Comparative Study of Machine Learning Methods for the Prediction of Hydrogen Production From Biogas

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Authors

KUKaleem UllahMAMuazzam ArshadZJZainab Javed

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Overview

Analysis demonstrates improved hydrogen production using optimization in biogas systems, suggesting enhanced sustainability.

Key Points

  • The random forest model achieved the highest R-squared value of 0.98, outpacing other machine learning methods.
  • Performance metrics included mean squared error values, with the random forest model showing the lowest at 0.0695.
  • Analysis utilized a surrogate-based methodology with genetic algorithm and Nelder–Mead techniques for optimization of hydrogen production.
  • Findings highlight the potential for industry implementation of machine learning in renewable energy processes.

Cite This Study

Ullah et al. (2025) studied this question.

synapsesocial.com/papers/68af751a7567bf4f94fee321https://doi.org/10.1155/er/8940534
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning-Guided Inverse Analysis for Optimal Catalytic Pyrolysis Parameters in Hydrogen Production from Biomass2026
  2. 2Mathematical modeling and machine learning approaches for biogas production from anaerobic digestion: A review2025
  3. 3Predicting ultimate hydrogen production and residual volume during cyclic underground hydrogen storage in porous media using machine learning2025
  4. 4Augmented machine learning with limited data for hydrogen yield prediction in wastewater dark fermentation2025
  5. 5Machine learning models for the prediction of hydrogen solubility in aqueous systems2025 · 10 citations