Machine Learning-Driven Prediction of CO<sub>2</sub> Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach

Machine Learning-Driven Prediction of CO<sub>2</sub> Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach

The solubility of CO<sub>2</sub> in brine systems is critical for both carbon storage and enhanced oil recovery (EOR) applications. In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this impo...

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Bibliographic Details
Main Authors: Seyed Hossein Hashemi, Farshid Torabi, Paitoon Tontiwachwuthikul
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Energies
Subjects:
CO<sub>2</sub> solubility
Gaussian process regression
Grey Wolf Optimizer
ARD kernels
carbon storage
enhanced oil recovery
Online Access:https://www.mdpi.com/1996-1073/18/15/4205
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