Hybrid machine learning-based model for solubilities prediction of various gases in deep eutectic solvent for rigorous process design of hydrogen purification

Abstract

Deep eutectic solvents (DES) are used as a green sustainable alternative to room temperature ionic liquids (RTILs), given their low cost and environmentally friendly nature. In this work, solubilities of CO2, CO, CH4, H-2 and N-2 gases in choline chloride/urea (ChCl/Urea) based DES is investigated. Experimental solubility data from literature is used to train machine learning models to predict the solubilities of different gases in ChCl/Urea at temperatures ranging from 298.15 K to 372.15 K and pressure ranging from 0.01 to 5 MPa. In this context, a Support Vector Machine and Long Short-term Memory Auto Encoder-based hybrid machine learning model is proposed. The hybrid model exhibited excellent prediction accuracy with low root mean square error values of 0.000985, 0.00055, 0.00037, 0.000583 and 0.000164 for CO2, CO, CH4, H-2 and N-2, respectively. The predicted solubility data is regressed in commercial software Aspen Plus V11 for process design of H-2 separation from gaseous feed mixture. Complex feed mixture consisting of CO2, CH4, H-2, CO, and N-2 is absorbed in ChCl/Urea. As a result, hydrogen is recovered and purified from complex feed mixture at specific energy consumption of 6.03 kWh/kgH(2). Furthermore, carbon removal is observed as > 99% from feed gas at the expense of 3.02 MJ/kgCO(2).