A generic machine learning model for CO2 equilibrium solubility into blended amine solutions

In this work, three machine learning methods were employed to predict carbon dioxide (CO2) equilibrium solubility in blended amine solutions consisting of ethanolamine (MEA), N,N-diethylethanolamine (DEEA) and methyldiethanolamine (MDEA). Three machine learning algorithms, Radial Basis Function Neural Network (RBFNN), Support Vector Machine Regression (SVR) and Extreme Gradient Boosting (XGBoost), were used to fit the experimental results. We found that the predicted values of the three models developed for the CO2 equilibrium solubility were in good agreement with the experimental results, and the comparison of the mean absolute percentage error (MAPE) and root mean square error (RMSE) results showed that XGBoost had the best prediction accuracy with the MAPE of less than 1%. Four different amine blends were then used to evaluate the expandability of the XGBoost model, namely MEA and 1-dimethylamino-2-propanol (1DMA2P), MDEA and piperazine (PZ), diethylenetriamine (DETA) and PZ, and MEA and triethanolamine (TEA); the MAPE between the experimental and predicted results were 0.30%, 0.91%, 0.86% and 1.63%, respectively. The results obtained showed that the XGBoost model has enormous potential for application in dealing with the CO2 equilibrium solubility in blended amine solutions.