CO2 gas-liquid equilibrium study and machine learning analysis in MEA-DMEA blended amine solutions

The combination of primary and tertiary amines represents a promising approach to improve sorbent performance in CO2 capture by enhancing absorption efficiency and reducing regeneration energy. This study focuses on investigating the absorption performance of binary mixtures of ethanolamine (MEA) and N,N-dimethylethanolamine (DMEA) at temperatures between 298–323 K and CO2 partial pressures between 5–60 kPa. The species generated during the absorption were analyzed using 13C NMR spectroscopy, to clarify the intricate role of MEA and DMEA in the capture process. A developed excess property model for MEA-DMEA, based on excess CO2 loading, predicted equilibrium CO2 solubility data with an average absolute relative deviation (AARD) of 1.6 %. Additionally, the machine learning models XGBoost, RBFNN, and SVR were applied, providing AARD values between 0.86 % and 1.28 %, demonstrating strong agreement between experimental and predicted outcomes. These comprehensive findings enhance our understanding of mixed amines’ mechanisms and practical applications, contributing to ongoing research development.