Robust development of data-driven models for methane and hydrogen mixture solubility in brine
Abstract Within the domain of hydrogen storage initiatives inside subterranean structures, the accurate estimation of solubility of methane and hydrogen mixtures in brine becomes vital. In this paper, we aim to form robust data-driven intelligent algorithms founded on various machine learning method...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
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| Series: | Geomechanics and Geophysics for Geo-Energy and Geo-Resources |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40948-025-00947-1 |
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