CO2 gas-liquid equilibrium study and machine learning analysis in MEA-DMEA blended amine solutions
Haonan Liu, Francesco Barzagli, Li Luo, Xin Zhou, Jiaofei Geng, Chao’en Li, Min Xiao, Rui Zhang
Abstract
The combination of primary and tertiary amines represents a promising approach to improve sorbent performance in CO 2 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 CO 2 partial pressures between 5–60 kPa. The species generated during the absorption were analyzed using 13 C NMR spectroscopy, to clarify the intricate role of MEA and DMEA in the capture process. A developed excess property model for DEA-DMEA, based on excess CO 2 loading, predicted equilibrium CO 2 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.