Modeling CO2 loading capacity of triethanolamine (TEA) aqueous solutions via a deep learning approach

Capturing carbon dioxide (CO 2 ) from natural gas is essential for reducing CO 2 emissions as a greenhouse gas as well as increasing the gas heating value. Absorption of CO 2 by amine solutions is a method that has seen a widespread use as a CO 2 collection technique. In this research, advanced ML approaches are utilized to simulate the CO 2 loading capacity of triethanolamine (TEA) aqueous solutions as a function of system temperature, CO 2 partial pressure, and amine concentration in the aqueous phase. Deep neural network (DNN), Gaussian process regressor (GPR), deep belief network (DBN), and bagging regressor (BR) are the models that have been developed. The DNN model with the coefficient of determination (R 2 ) of 0.9990 as well as the root mean square error (RMSE) of 0.0073 outperformed other models in terms of accuracy and validity. The R 2 values of 0.9911, 0.9861, and 0.9745 for DBN, BR, and GPR, accordingly, showed that other models could likewise perform with high accuracy. Moreover, trend analysis of the results confirmed that the DNN method can correctly estimate the behavior of CO 2 loading with variations in the input parameters. Furthermore, sensitivity analysis showed that temperature has a decreasing effect on the CO 2 loading, while amine concentration and CO 2 partial pressure exhibited to have an incremental effect. Herein, the temperature had the greatest influence on the amount of CO 2 loading. The final stage of the research was the leverage approach, which stated that about 99 % of the data are statistically valid and the DNN model is reliable to be replaced by experimental approaches in the prediction of CO 2 absorption into certain type of solutions. • CO 2 loading capacity of triethanolamine (TEA) aqueous solutions was modeled using a large data bank. • DNN, GPR, DBN, and BR were used for modeling. • CO 2 partial pressure, temperature, and amine concentration were used as models' inputs. • The DNN model with R 2 of 0.9990 and RMSE of 0.0073 outperformed the other models. • The leverage approach was used to find the applicability domain of the DNN model.