Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

Abstract

The present work investigates the use of machine learning approaches for the prediction of hydrate formation pressure (HFP) in gas hydrate systems. Advanced machine learning models, including the decision tree regressor (DTR), random forest regressor (RFR), extreme gradient boosting (XGB), gradient boosting regressor (GBR), histogram gradient boosting regressor (HGBR), and CatBoost regressor (CB), are trained and evaluated on a large dataset consists of 3137 experimental data points. The models are evaluated using R-squared (R²), mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). The study indicates that for the intent of HFP prediction, CatBoost outperformed all other machine learning models. It demonstrated high accuracy on the testing set with an R² value of 0.9922, and with the lowest RMSE (1.61 × 10⁻³), MAE (7.90 × 10⁻⁴), and MSE (2.58 × 10⁻⁶), CatBoost strengthened its prediction ability.