An ebulliometer designed with automated feeding was tested and verified in this work. The ebulliometer is made of stainless steel. The feeding of the substances is automatically controlled with a computer. In the equipment, both the liquid and vapor phases are recirculated.  The binary mixtures isobutyl acetate + 1–propanol and isobutyl acetate + 2–propanol were studied and the vapor–liquid equilibrium of these mixtures was determined at 101 kPa. The isobaric T–x–y data are reported, including the azeotropic point of the isobutyl acetate + 1–propanol binary system. The calculations have been carried out considering the non–ideal vapor phase and the activity coefficients of the liquid phase have been obtained. The thermodynamic consistency of the system was verified with the Van Ness point–to–point test. The f–f approach was used to evaluate the data reproducibility by considering the Perturbed Chain–Statistical Associating Fluid Theory (PC–SAFT).

In this paper, the vapor-liquid equilibrium (VLE) properties of polar and nonpolar fluids are modeled by the use of a statistically-based equation of state (EOS). The equation of state used in this work is that of Ihm-Song-Mason (ISM) EOS. An alternative approach is to revise the isothermal integration on liquid. In this respect, a temperature-dependent revision factor b (T) is introduced to the liquid fugacity coefficient expression which was already derived from traditional isothermal integration. By this modification, the vapor pressure of pure liquids can be successfully reproduced using ISM EOS. This equation of state originally cannot predict the vapor pressure of fluids. The novelty of the present work is to introduce a method such that the ISM EOS is able to predict vapor pressure of pure substances and mixtures. Furthermore, the revisionzaSX method is extended to represent the VLE properties of binary mixtures including noble gases, refrigerants and hydrocarbons. Our computation results on the VLE and PVT behaviors of 24 pure substances and 9 binary mixtures are impressive.

In this paper, the CORGCEOS is combined with an Excess-Gibbs-Energy model such as UNIFAC in order to extend the capabilities and improve the accuracy of the CORGCEOS in predicting the vapor-liquid equilibrium behavior of mixtures containing polar components. The CORGCEOS is used together with the PSRK mixing rule and the original UNIFAC is applied as Excess-Gibbs- Energy model in the proposed approach. Comparison between this work (CORGC/UNIFAC) and the original CORGC with experimental data of several polar mixtures are presented. The outcomes indicate that CORGC/UNIFAC provides better vapor-liquid equilibria results than the original CORGC and the accuracy of binary polar systems is nearly twice that of the original CORGC. The required missing interaction parameters of the original UNIFAC model are fitted with experimental data and the missing interaction parameters for unlike pairs of groups in the original CORGCEOS are also determined. The model parameters are determined from VLE of binary mixtures and are reported. These parameters are, then, used without any further adjustment to predict VLE of multicomponent mixtures, for which more accurate results in comparison to the original CORGC are observed.

The chemical and biological pathways for acetic acid production involve the separation of acetic acid from water. Azeotropic distillation and liquid-liquid extraction are used to separate acetic acid from water with conventional solvents, as simple distillation is impractical due to pinch point near the water edge and requires more energy to separate these two components. Methyl tert-butyl ether (MTBE) is a more viable solvent due to its low energy consumption during recovery. To compute isobaric vapor-liquid equilibrium (VLE) data for a binary mixture of methyl tert-butyl ether (MTBE) + acetic acid (AA) system, the group contribution method, UNIFAC was used. The thermodynamic consistency tests such as Redlich and Kister's and Wisniak's L-W were used to check the validity of these data. Wilson, UNIQUAC, and NRTL excess Gibbs energy models were used to determine the binary interaction parameters of these models. VLE data obtained in this work can be used to design the distillation and extraction system to recover and purify MTBE.

Study of vapor-liquid and liquid – liquid Equilibrium plays an important role in the design, optimization and control of separation processes. In this research phase equilibrium of binary systems (1-propanol, water and 1-propanol, ethyl acetate) also ternary systems (water, ethylene glycol, 2-ethyl 1-hexanol and water, ethylene glycol, 1-heptanol) using thermodynamic models of NRTL and UNIQUAC were studied. Also Adaptive Nero-Fuzzy Inference System (ANFIS) and group method of Data handling (GMDH-type neural network) were used for modeling of systems. The VLE graphs (temperature based on mole fraction of vapor-liquid phases) for binary systems and LLE graphs for ternary systems at various temperatures by thermodynamics models were drawn. Accuracy of thermodynamic models, ANFIS model and GMDH type-Neural Network for the binary and ternary systems studied and compared with experimental data. Comparison of results shows that NRTL models have good fitness with the experimental data and the minimum error is related to the ANFIS Statistical model.

By proposing a predictive method with no adjustable parameter and by using infinite dilution activity coefficients of components in binary mixtures obtained from UNIFAC model, the binary interaction parameters (K12) in van der Waals mixing rule (vdWMR) and Orbey-Sandler mixing rule (OSMR) have been evaluated. The predicted binary interaction parameters are used in Peng-Robinson-Stryjek-Vera equation of state (PRSV-EoS) to obtain the vapor-liquid equilibrium (VLE) for six strongly polar and asymmetric binary mixtures. The binary interaction parameters evaluated by correlation of VLE experimental data and by PRSV EoS are also used in VLE calculations of the same binary mixtures. The average absolute deviations (AADs%) of the calculated results by both predictive and correlative methods are reported. The results indicate that although the correlative method has a more flexibility to fit the VLE experimental data, the AAD% of the predictive method is comparable and in some cases even better than that of correlative method.