A New Fast Estimation Method for Critical Properties of Mixtures Based on the Modified Redlich–Kister Method-Part 2: Prediction of Critical Properties for Binary and Ternary Mixtures

The vapor–liquid critical properties of mixtures, which represent the end points of vapor–liquid equilibrium curves, are crucial for the development of next-generation environmentally friendly working fluids and advancements in supercritical fluid technology. Experimental measurement and theoretical prediction are the main means to obtain the critical properties. However, the existing theoretical prediction methods have the problems of low accuracy, especially when predicting critical volumes of binary mixtures and critical properties of ternary mixtures. Moreover, in most prediction methods, all the data are used to fit the adjustable parameters. No prediction data set was added to test its prediction ability. In this work, new prediction models for critical properties, including critical temperature, critical pressure, and critical volume of binary mixtures and ternary mixtures, were proposed. New methods extend our previous work (Tang et al.’s model) to the prediction of critical volumes and critical properties of ternary mixtures and accurately evaluate the extrapolation ability. New prediction models inherit many advantages of Tang et al.’s model, including considering the effect of molecular polarity to some extent, possessing four fewer adjustable parameters, a simpler form in use, and not requiring the critical volume data of pure substances when predicting critical temperatures and critical pressures of mixtures. Moreover, three new methods possess fast prediction abilities for critical properties of mixtures, showing higher accuracy in predicting critical properties of binary and ternary mixtures in the fitting data set and prediction data set. When predicting critical temperatures and critical pressures, new models can be applied to binary and ternary mixtures consisting of methane-free alkanes, alkenes, alkynes, alicyclic hydrocarbons, benzene and its derivatives, NH3, CO2, halogenated hydrocarbon, N2O, Kr, Xe, sulfur-compounds, and oxygen-containing organic compounds. Notably, new methods are applicable only to class I (continuous curve of the critical point of two pure components) of the vapor–liquid critical locus, classified by Van Konynenburg and Scott. Class I covers many systems and most of the available critical property experimental data. Almost all systems can be covered in predicting critical volumes. About 9000 critical data points including critical temperatures, critical pressures, and critical volumes for binary mixtures and ternary mixtures are collected. About 80% of the data for binary mixtures were used to fit the empirical parameters. The remaining data were used to test the prediction ability for binary and ternary mixtures for three new prediction methods. The prediction model 1 shows the highest correlation and prediction accuracy among the three prediction models. The average absolute relative deviations are 1.13, 3.91, and 5.90 when correlating critical temperatures, critical pressures, and critical volumes; 1.43, 4.94, and 6.93 when predicting critical temperatures, critical pressures, and critical volumes of binary mixtures; and 1.12 and 3.99 when predicting critical temperature and critical pressures of ternary mixtures.