Abstract
Azeotropic mixtures, an important class of technological fluids, constitute a challenge to theoretical modeling of their properties. The number of possible intermolecular interactions in multicomponent systems grows combinatorially as the number of components increases. Ab initio methods are barely applicable, because rather large clusters would need to be calculated, which is prohibitively time-consuming. The quantitative structure-property relationships (QSPR) method, which is efficient and extremely fast, could be a viable alternative approach, but the QSPR methodology requires adequate modification to provide a consistent treatment of multicomponent mixtures. We now report QSPR models for the prediction of normal boiling point:: of ternary azeotropic mixtures based on a training set of 78 published data points. A limited set of meticulously designed descriptors, together comprising the Universal Solvation Equation (J. Chem. Inf Model. 2009, 49, 634), was used to provide input parameters for multiple regression and neural network models. The multiple regression model thus obtained is good for explanatory purposes, while the neural network model provides a better quality of fit, which is as high as 0.995 in terms of squared correlation coefficient. This model was also properly validated and analyzed in terms of parameter contributions and their nonlinearity characteristics.