Prediction of CO2 solubility in potential blends of ionic liquids with Alkanolamines using statistical non-rigorous and ANN based modeling: A comprehensive simulation study for post combustion CO2 capture

Equilibrium CO2 solubility data plays a pivotal role in the process design of the post-combustion CO2 capture Unit. In the current study, statistical non-rigorous models are developed to predict the solubility of carbon dioxide (CO2) in different aqueous blends of ionic liquids (ILs) and blends of ILs with alkanolamines, ethanol, etc. These correlations are utilized to estimate the liquid phase CO2 loading as a function of CO2 partial pressure at different temperature conditions. The correlations can predict CO2 solubility in the liquid phase of different (ILs + alkanolamine + water/ethanol) in the temperature and CO2 partial pressure range of (298-353) K and (50-6200) kPa respectively. The CO2 mole fraction in the liquid phase pertaining to 19 different solvent formulations is considered in the range of (0-0.487). It has been found that there is a good agreement between the results of the proposed models and reported experimental data of CO2 solubility. In addition to this, the predictability of artificial neural network (ANN) for CO2 solubility is also tested for 22 systems of ionic liquid blends with amines. The main advantages associated with these proposed models are their underlying simplicity and minimal input data, namely temperature and pressure.

Automated summary

Statistical non-rigorous models are developed to predict CO2 solubility in different aqueous blends, which can estimate the liquid phase CO2 loading in different solvent formulations. The proposed models show good agreement with experimental data on CO2 solubility.