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Hansen solubility parameters (HSP) provide essential information on the nature of solvents, being a useful tool in their selection for product and process design. In this work, linear models were developed to estimate HSP based on the use of COSMO-RS (conductor-like screening model for realistic solvents) descriptors. Hansen solubility parameters for 195 compounds were obtained from the literature and classified into two categories: training set (133 compounds) and testing set (62 compounds). Then a factorial regression was carried out to predict dispersion, hydrogen-bonding interaction, and polar contribution of HSP (δD, δH, and δP, respectively) based on σ-moments and energy COSMO-RS descriptors. The description of the dispersion contribution was the least successful due to the low variability of the experimental data, despite the database containing compounds of a widely different chemical nature. The models obtained proved to be more than simple mathematical equations since a physical meaning is achieved when COSMO-RS descriptors are used. The models developed were then applied to the test database. An excellent performance was observed, with δH showing the highest R2 (0.90), and the MAE obtained were 0.98, 1.74, and 1.44 MPa1/2 for δD, δH, and δP, respectively, which are lower than those found in previous works. Finally, HSP data for caffeine, nicotine, paracetamol, and d-camphor were estimated. The results were close to the literature data and those predictive by the HSPiP software. Moreover, it was shown that δP and δH drive the molecules' polarity, impacting their log Kow (a quantitative measure of polarity). This work shows that COSMO-RS descriptors are a tool to predict HSP through linear models, opening new doors in the screening, design, and selection of solvents to be used in chemical processes.
Wojeicchowski et al. (Wed,) studied this question.