A modeling-optimization framework for assessment of CO2 absorption capacity by novel amine solutions: 1DMA2P, 1DEA2P, DEEA, and DEAB

In this study, a modeling-optimization framework was developed to assess absorption capacity of CO2 by four promising tertiary amines in CO2 capture, namely, 1-dimethylamino-2-propanol (1DMA2P), 1-diethylamino-2-propanol (1DEA2P), 2-(diethylamino)ethanol (DEEA), and 4-diethylamino-2-butanol (DEAB). The purpose of this developed framework is to study the simultaneous effect of all solubility parameters including CO2 partial pressure, temperature, and amine concentration on the absorption capacity in terms of CO2 loading. In this framework, an orthogonal array design (OAD) method (a statistical method) was used for optimization, and Kent-Eisenberg (K-E), modified Kent-Eisenberg (M-K-E), and Deshmukh-Mather (D-M) models (thermodynamic models) were applied to predict CO2 loading of amine solutions. In addition, the back-propagation neural network model was applied and the results were compared with thermodynamic models. The D-M model was used to predict the response values (CO2 loading) in the OAD method. The results showed that the D-M model was superior to other thermodynamic models in the prediction of CO2 loading data with average absolute relative deviations (AARDs) of 2.89%, 3.59%, 1.76%, and 2.3% for DEEA, 1DMA2P, DEAB, and 1DEA2P solutions, respectively. The OAD results showed that all solubility parameters had significant effects on CO2 loading, and the statistically significant order of parameters affecting absorption capacity was CO2 partial pressure > amine concentration > temperature.