Several carbon capture techniques have been developed in response to the notable rise of atmospheric carbon dioxide ( CO2 )

levels. The utilization of diethanolamine (DEA) as an absorption method is prevalent in various industries due to its high

reactivity and cost-effi ciency. Hence, comprehending the equilibrium solubility of CO2 in DEA solutions is an essential step

in developing and optimizing absorption procedures. In order to predict the CO2 loading capacity in the DEA solutions,

four advanced deep learning and machine learning models were developed: recurrent neural networks (RNN), deep neural

networks (DNN), random forest (RF), and adaBoost-support vector regression (AdaBoost-SVR). The models predict the

capacity of CO2 loading as a function of temperature, CO2 partial pressure, and the concentration of DEA in the solution.

Intelligent models were developed employing an extensive database which includes new experimental data points published

within recent years, which were not considered in the previous studies. The RNN model was found to outperform other

models based on graphical and statistical assessments, as evidenced by its lower root mean square error ( RMSE = 0.285 )

and standard deviation ( SD = 0.032 ), and higher determination coeffi cient ( R2

= 0.992 ). While the RNN model resulted in

the highest accuracy in predicting CO2 absorption, the DNN, RF, and AdaBoost-SVR models also demonstrated satisfactory

accuracy in predicting CO2 solubility, placed in the following ranking. A sensitivity analysis was performed on the four

developed models, revealing that the CO2 partial pressure has the strongest eff ect on the CO2 loading capacity. Furthermore,

a trend analysis was performed on the RNN model, demonstrating that the developed model has a high degree of accuracy

in following physical trends. The binary interaction analysis was conducted with two varying parameters and one constant

parameter in the RNN model through 3-D image plots, which illustrated the simultaneous eff ect of two independent parameters

on CO2 loading. Finally, outlier detection was conducted by employing the Leverage method to fi nd outlier data points

in the data bank, demonstrating the applicability domain of intelligent models.

Carbon capture · Diethanolamine (DEA) · CO 2 loading capacity · Recurrent Neural Networks · Intelligent model

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