A Comparative Study of QSPR Generalized Activity Coefficient Model Parameters for Vapor–Liquid Equilibrium Mixtures

Generalized activity coefficient models are often essential for predicting the extent of liquid nonideality in a mixture in the absence of experimental data. This work is focused on generalizing the interaction parameters of three widely used activity coefficient models, nonrandom two-liquid (NRTL), universal quasi-chemical (UNIQUAC), and Wilson. Specifically, we applied a theory-framed quantitative structure–property relationship (TF-QSPR) modeling approach for the purpose of generalization. In this modeling approach, theoretical frameworks, such as the NRTL model, are used to describe the phase behavior properties, and QSPR methodology is used to generalize the binary interaction parameters of the models. In this study, a binary VLE database consisting of 916 systems was compiled and employed to develop the QSPR models. Interaction parameters of the NRTL, UNIQUAC, and Wilson models were determined by performing data regression analyses. QSPR models were developed to predict the interaction parameters found in the regression analyses. The structural descriptors of the molecules were used as inputs in the QSPR models. The phase equilibria properties estimated using the generalized QSPR models resulted in about 2 times the error as compared to the results found in the data regression analyses. Overall, the quality of property predictions from the QSPR models is comparable to those of the UNIFAC-2006 group-contribution model when all of its group-interaction parameters are available; however, the UNIFAC model produced worse predictions when such parameters are lacking. Thus, our methodology offers a viable complement when UNIFAC parameters are missing