Generalized Activity Coefficient Models for VLE and LLE Property Predictions

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.

This work is focused on developing improved generalized models for Vapor-liquid equilibria (VLE) and Liquid-liquid equilibria (LLE) property predictions using a theory-framed QSPR modeling approach. Our newly developed non-linear NRTL-QSPR models provide a priori predictions for the NRTL binary model parameters for a wide variety of VLE and LLE systems. The developed VLE-NRTL-QSPR and LLE-NRTL-QSPR models predict equilibrium properties within 2 to 3 and 2 to 3.6 times the errors obtained from direct NRTL parameter regressions respectively. The overall property predictions for VLE and LLE systems result in significant improvement compared to the commonly used group contribution method (UNIFAC).

Conclusions:

Our current research is yielding several significant findings, including:

Theory-framed QSPR modeling is effective in generalizing phase equilibria models.

Functional group counts, ring descriptors and position of fragments were found to be significant attributes in determining the NRTL model parameters.

The generalized, non-linear QSPR model for the NRTL parameters produced VLE and LLE predictions within 2-3 times the average absolute percent error of the data regressions.

 

 

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