Prediction of CO solubility in Ionic liquids for CO capture using deep learning models.

Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO). The prediction of CO solubility in ILs is crucial for optimizing CO capture processes. This study investigates the use of deep learning models for CO solubility prediction in ILs with a comprehensive dataset of 10,116 CO solubility data in 164 kinds of ILs under different temperature and pressure conditions.

Thermal degradation of aqueous 2-aminoethylethanolamine in CO capture; identification of degradation products, reaction mechanisms and computational studies.

Amine degradation is the main significant problems in amine-based post-combustion CO capture, causes foaming, increase in viscosity, corrosion, fouling as well as environmental issues. Therefore it is very important to develop the most efficient solvent with high thermal and chemical stability. This study investigated thermal degradation of aqueous 30% 2-aminoethylethanolamine (AEEA) using 316 stainless steel cylinders in the presence and absence of CO for 4 weeks.