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September 10, 2025Scientific ReportsOpen Access

Machine learning models for the prediction of hydrogen solubility in aqueous systems

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Authors

MMMehdi MalekiAAAli AkbariYKYousef Kazemzadeh

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Overview

Machine learning predictions improve hydrogen solubility understanding in saline aquifers, suggesting enhanced storage efficiency.

Key Points

  • Machine learning models predict hydrogen solubility, significantly enhancing storage efficiency in saline aquifers.
  • The best-performing model, random forest, achieved an R2 of 0.9810 for test data, highlighting its predictive capability.
  • Using various algorithms like ANN and SVM, the study explores complex relationships between hydrogen solubility and environmental factors.
  • These findings underscore the critical role of machine learning in optimizing hydrogen storage and improving reservoir management.

Cite This Study

Maleki et al. (2025) studied this question.

synapsesocial.com/papers/68c1d23054b1d3bfb60f7cechttps://doi.org/10.1038/s41598-025-16289-7
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