Improved QSPR generalized interaction parameters for the nonrandom two-liquid activity coefficient model

Phase equilibrium properties are essential in developing and optimizing numerous processes. The objective of this work is the application of a theory-framed quantitative structure-property relationship (QSPR) modeling approach to provide a priori vapor–liquid equilibrium (VLE) predictions. For this purpose, we apply the nonrandom two-liquid (NRTL) activity coefficient model to describe the phase behavior, and then use the QSPR methodology to generalize the substance-specific parameters of the model. Generalizing the parameters of a proven phase behavior model, such as the NRTL, will minimize the need for acquiring costly VLE experimental data for the systems of interest.The newly developed generalized NRTL-QSPR activity coefficient model constitutes a significant improvement over our previous generalization of the NRTL model. Specifically, an internally consistent generalization is provided for the NRTL interaction parameters using a more extensive database involving 578 binary systems. A non-linear QSPR model was developed for the NRTL parameters, where evolutionary algorithms combined with artificial neural networks were used to perform molecular descriptor reduction. The model predicts pressure and temperature of a binary VLE system within 6% and 0.6% average absolute deviation (AAD), respectively. Further, the generalized NRTL phase behavior predictions show a significant improvement over to the group contribution method, Universal Functional Activity Coefficient model (UNIFAC), which resulted in 9% AAD for pressure predictions.

► We generalized the interaction parameters of the NRTL activity coefficient model.
► Theory-framed quantitative structure-property relationship approach is employed.
► Three dimensional molecular descriptors were used instead of molecular fragments.
► Wrapper-based feature selection used with differential evolution and neural networks.
► The generalized model provided better property predictions than the UNIFAC model.