The statistical associating fluid theory for potentials of variable range (SAFT-VR)  is a widely used molecular-based equation of state that has been successfully applied to study a wide range of fluid systems. It provides a framework in which the effects of molecular shape and interactions on the thermodynamics and phase behavior of fluids can be separated and quantified. In the original approach, molecules were modeled as chains composed of identical segments, where the heterogeneity of molecules in terms of structure and functional groups was described implicitly through effective parameters, leading to the use of binary interaction parameters in order to describe mixture phase behavior in non-ideal systems. To overcome this limitation, in recent work the theory was extended to model chains composed of segments of different size and/or energy of interaction, thus enabling the development of a group-contribution SAFT-VR approach (GC-SAFT-VR) . In previous work, parameters for a range of different key functional groups (CH3, CH2, CH, CH2=CH, C=O, C6H5, CH3O and CH2O esters groups) have been determined by fitting to experimental vapor pressure and saturated liquid density data for a number of small molecules containing the functional groups of interest. Transferability of the parameters was tested by comparing the theoretical predictions with experimental data for pure fluids and binary mixtures not included in the fitting process, as well as by studying the VLE and LLE of small molecules in polymer systems . In this work, we further test the GC-SAFT-VR approach through the study of phase behavior in associating systems such as alcohols, amines, aldehydes, and carboxylic acids, and their mixtures. New functional groups (OH, NH2, CH=O, COOH) are defined and their molecular parameters characterized by again fitting to experimental vapor pressure and saturated liquid density data for selected small molecules. Transferability of the parameters is tested by comparing the theoretical predictions with experimental data for pure fluids and binary mixtures not included in the fitting process. The GC-SAFT-VR approach is found to predict the phase behavior of the systems studied in excellent agreement with experimental data without adjusting the group parameters to binary mixture data; thus achieving the goal of developing a predictive tool for studying a wide range of fluid phase equilibria.
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