QSPR generalization of activity coefficient models for predicting vapor–liquid equilibrium behavior

Abstract

Multiphase equilibrium calculations are an integral part of the design and optimization of numerous chemical processes. Several accurate experimental techniques have been developed for measuring phase equilibrium data. However, experimental techniques are time consuming and costly. Hence, a need exists for reliable thermodynamic models capable of giving a priori predictions of the phase behavior of diverse systems in the absence of experimental data. Quantitative structure–property relationship (QSPR) modeling has the potential to provide reliable property estimates based on detailed chemical structure information. Although current QSPR models have been successful in providing reliable structure-based property predictions, they have been limited to estimating properties at a single temperature. Further, little work has been done on QSPR models for mixtures. In this work, we generalize excess Gibbs energy ( G E) models, such as the non-random two-liquid (NRTL) and universal quasi-chemical (UNIQUAC) models by developing structure-based model parameters using QSPR modeling. Specifically, integrated G E-QSPR models capable of a priori prediction of the vapor–liquid equilibrium (VLE) phase behavior are developed. These NRTL-QSPR and UNIQAC-QSPR model generalizations produced VLE predictions for a wide variety of systems (332 binaries) with an average absolute percent deviation (%AAD) of approximately 5%, which is about twice that from direct parameter regressions. In addition, our generalization strategy was successful in reducing the effects of the inherent parameter inter-correlation in the NRTL and the UNIQUAC model regressions.