Predicting CO2 Solubility in Brine for Carbon Storage with a Hybrid Machine Learning Framework Optimized by Ant Colony Algorithm

Predicting carbon dioxide (CO2) solubility in brine is critical for carbon capture and storage. This study employs the Ant Colony Optimization (ACO) algorithm to enhance the predictive accuracy of four machine learning models: Neural Network (NN), Decision Tree (DT), Support Vector Regression (SVR), and Gradient Boosting Machine (GBM). The models were trained and validated on a mineral compound dataset. Performance was evaluated using the coefficient of determination (R2) and error metrics including RMSE and MAE. The GBM model achieved the highest test accuracy (R2 = 0.986) with low errors (RMSE = 0.0478, MAE = 0.0362), demonstrating superior ability to model complex, non-linear relationships with minimal overfitting. The optimized NN, featuring three layers and fifteen neurons, delivered strong performance (R2 = 0.930) with balanced errors across datasets. The DT model offered excellent interpretability and a strong test score (R2 = 0.912), while the SVR model provided robust generalization (R2 = 0.889). The results indicate that ACO is an effective tool for hyperparameter tuning across diverse model architectures. For maximum accuracy, GBM is recommended, whereas DT is ideal when interpretability is required. The NN presents a strong middle-ground option with competitive accuracy. This comparative framework assists in selecting the optimal model based on specific project priorities of accuracy, transparency, or computational efficiency for geochemical forecasting.