Abstract
Hydrogen offers significant potential as a sustainable energy source. However, its storage and transportation pose challenges due to its volatility and low density. Subsurface geological formations, such as shale, sandstone, and coal, have been investigated as potential storage sites for hydrogen. Precise estimation of hydrogen adsorption in these formations necessitates a thorough understanding of kerogens, organic-rich sedimentary rocks prevalent in unconventional formations. In this study, we introduce a novel approach employing Extreme Gradient Boosting (XGBoost), Random forest (RF), Light gradient Boosting (LGBM), and Natural gradient boosting (NGBM) algorithms to intelligently predict hydrogen adsorption in various kerogen types. Among these, the NGBM algorithm, utilizing three input features (temperature, pressure, and kerogen types), demonstrated the highest predictive accuracy, achieving an R2 value of 0.989, RMSE of 0.062, and MAE of 0.037 for all data samples. SHAP diagram analysis identified kerogen types as the most influential parameter within the NGBM model. The presented approach has implications beyond energy storage, highlighting the significance of advanced technologies in addressing complex energy challenges. Precise hydrogen adsorption estimation in subsurface formations is vital for developing sustainable energy solutions, and our approach has the potential to expedite progress in this field. Interdisciplinary collaboration among geologists, chemists, and data scientists is crucial for devising innovative solutions for sustainable energy.
Original language | English (US) |
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Pages (from-to) | 1000-1009 |
Number of pages | 10 |
Journal | International Journal of Hydrogen Energy |
Volume | 57 |
DOIs | |
State | Published - Feb 29 2024 |
Bibliographical note
Publisher Copyright:© 2024 Hydrogen Energy Publications LLC
Keywords
- Artificial intelligence
- Hydrogen adsorption
- NGBM
- RF
- Underground hydrogen storage
- XGBoost
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Fuel Technology
- Condensed Matter Physics
- Energy Engineering and Power Technology