Prognostication of advanced CO(2) capture using tunable solvents with an ensemble learning-based decision tree model.

This study presents a robust method for predicting CO(2) solubility in Deep Eutectic Solvents (DESs) using the stochastic gradient boosting (SGB) algorithm. DESs, promising green solvents for CO(2) capture, require precise solubility data for practical applications in industrial and environmental settings. The model incorporates key parameters such as temperature, pressure, mole percent of salt and hydrogen bond donor (HBD) compounds, HBD melting points, molecular weights of salts and HBDs, and other critical factors. Using a dataset of 1951 experimental data points spanning temperatures (293.15-343.15 K) and pressures (26.3-12,730 kPa), the SGB model demonstrated excellent predictive accuracy, achieving an R(2) of 0.9928 and an AARD% of 2.3107. Variable importance analysis identified pressure as the most influential factor. The model's applicability, confirmed through William's plot, encompassed 97.5% of data points within a safety margin, ensuring reliability, versatility, and broad applicability. Moreover, the SGB model outperformed previous methods, including ANN, RF, and thermodynamic models like PR-EoS and COSMO-RS, as validated by statistical metrics. This research highlights the SGB model's potential as a superior and practical tool for evaluating CO(2) solubility in DESs, advancing the field of green solvent development for sustainable and efficient CO(2) capture technologies.