Development of Statistical Models for Predicting Presence of Azeotropy at any Pressure

Abstract

Distillation is one of the dominating separation processes, but there are some problems that inseparable mixtures are formed in some cases. This phenomenon is called as azeotropy. It is essential to understand azeotropy in any distillation processes since azeotropes, i.e. inseparable mixtures, cannot be separated by ordinary distillation. In this study, to construct a model which predicts the azeotropic formation at any pressure, a novel approach is presented with support vector machine (SVM). The SVM method is used to classify data in the two classes, that is, azeotropes and nonazeotropes. 13 variables including pressure data were used as explanatory variables. From the result of comparing the SVM models which were constructed with data measured at 1atm and data measured at any pressure, the 1atm model shows a higher prediction performance to the data measured at 1atm than the any pressure model. Thus, for improving the performance of the any pressure model, we focused on intermolecular forces of solvents. The SVM models were constructed with only data of the solvents having same subgroups. The accuracy of the model increased and it is expected that this proposed method will be used to predict azeotropic formation at any pressure with high accuracy.