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September 5, 2025Scientific ReportsOpen Access

The art of precision in unveiling hydrogen solubility in bines through data-driven modeling

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Authors

RHRaouf HassanMKMohammad Reza Kazemi

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Overview

Machine learning techniques improve predictions of hydrogen solubility, highlighting key relationships with temperature and pressure.

Key Points

  • CatBoost demonstrated the highest forecasting precision with an R-squared value of 0.9756 during testing, indicating strong predictive capability.
  • Advanced machine learning algorithms such as support vector regression and ensemble methods were employed to unravel complex interactions in solubility.
  • Data integrity was maintained using the Monte Carlo outlier detection algorithm, ensuring reliable model training and evaluation.
  • Sensitivity analyses identified significant relationships, with hydrogen solubility positively correlating with temperature and pressure, and negatively with salinity.

Cite This Study

Hassan et al. (2025) studied this question.

synapsesocial.com/papers/68bb3d552b87ece8dc955e80https://doi.org/10.1038/s41598-025-17340-3
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