Binary azeotropes

which contain two chemicals with a relative volatility of 1

are very common in the chemical industry. Understanding azeotropes is essential for effectively separating binary azeotropes containing lower alcohols. Experimental techniques and

ab initio

approaches can produce accurate results; however

these two processes are time consuming and labor intensive. Although thermodynamic equations such as UNIFAC are widely used

experimental values are required

and it is difficult to choose the best groups to represent a complex system. Because of their high efficiency and fast calculation speed

quantitative structure-property relationship (QSPR) tools were used in this work to predict the azeotropic temperatures and compositions of binary azeotropes containing lower alcohols. The QSPR models for 64 binary azeotropes based on centroid approximation and weighted-contribution-factor approximation were established using the genetic function approximation (GFA) procedure in Materials Studio software

and a leave-one-out cross-validation procedure was conducted. External tests of an additional 16 azeotropes were also investigated

and high determination coefficient values were obtained. The best QSPR models were explained in terms of the molecular structure of the azeotropes

and good predictive ability was obtained within acceptable prediction error levels.