Implementation of CP-PC-SAFT and CS-SAFT-VR-Mie for Predicting Thermodynamic Properties of C1-C3 Halocarbon Systems. I. Pure Compounds and Mixtures with Nonassociating Compounds

This study compares the estimations of two predictive approaches, namely, CP-PC-SAFT and CS-SAFT-VR-Mie for vapor pressures, saturated and high-pressure densities, sound velocities, isobaric and isochoric heat capacities of 17 representative substituted fluoro- and chloromethanes, ethanes, ethenes, propanes, and propenes, whose data are available under a wide range of conditions. Predictions of vapor-liquid equilibria (VLE) in their binary systems comprising inter alia carbon dioxide, hydrocarbons, and light gas systems with k(12) = 0 are considered as well. These models do not incorporate associative and polar interactions, which allows parametrization based on the particularly limited amount of literature data. It is demonstrated that although the overall accuracies of the considered approaches are reasonably good, each of them has their advantages and disadvantages. For example, SAFT-VR-Mie typically predicts more accurately some VLE in the symmetric systems, while CP-PC-SAFT is advantageous in estimating phase behavior of the asymmetric ones. Both models can be applied for preliminary estimation of unavailable halocarbon data for which each of them exhibits a relative superiority. In addition to this, it is demonstrated that SAFT-VR-Mie attached by square gradient theory with predictive correlation for influence parameters accurately estimates surface tensions of the considered compounds.

Automated summary

This study compares the accuracy of two predictive models, CP-PC-SAFT and CS-SAFT-VR-Mie, for estimating various properties of different fluorinated and chlorinated hydrocarbons. The results show that each model has its own strengths and weaknesses, with CP-PC-SAFT performing better in asymmetric systems and SAFT-VR-Mie being more accurate in symmetric systems. Both models can be used for preliminary estimation of unknown halocarbon data.