Performance evaluation of advanced machine learning methodologies in simulating hydrogen chloride (HCl) absorption by deep eutectic solvents

This work aims to construct the physical-based machine learning (ML) approach for estimating the hydrogen chloride (HCl) solubility in eleven different deep eutectic solvents (DESs). All DES-HCl equilibrium records versus temperatures and pressures are gathered from the literature to ensure ML universality. Then, a well-known feature selection technique was applied to identify the physically meaningful descriptors for the hydrogen bond acceptors (HBA) and hydrogen bond donors (HBD). Afterward, temperature, pressure, selected meaningful physical descriptors, and HBA/HBD molar concentrations are utilized to design ML tools to compute HCl solubility in DESs. Diverse MLs, including extreme learning machines, convolutional neural networks, long short-term memory, cascade neural networks, and extreme gradient boosting (XGBoost), are tested, and their accuracy is compared to select the best candidate. The XGBoost model predictions for HCl solubility in DESs are in acceptable agreement with the literature records. This model precisely simulates 417 DES-HCl equilibrium records in the temperature range of 293-348K and pressures of up to 127 kPa with a correlation coefficient of 0.99621, mean absolute percentage deviation of 3.04, mean absolute error of 0.448, and root mean square error of 0.729. Results of the designed XGBoost model prove that acetamide-ethylene glycol (1:1), which can averagely dissolve up to 32.47wt% (weight percent) of HCl, is the best DES for the HCl capture and recovery. After approving the XGBoost model reliability with different visual and numerical inspections, the Leverage method also justified its validity domain by more than 97.6%.