Statistical regression of binary vapor–liquid equilibrium data for ternary phase equilibrium predictions

Abstract

High-pressure vapor–liquid equilibrium data of more than 50 binary systems were correlated by a DDLC (density-dependent local-composition) model incorporated into the Soave–Redlich–Kwong equation of state. The Error-Propagation-Law Method based on the maximum likelihood principle and the simple least-squares method were applied to data reduction. Fitting accuracies of the DDLC model by statistical regression were found better than those obtained by the least-squares as well as those of the SRK equation of state by both methods. However, no improvements were obtained for the original SRK equation by the statistical method. Further, vapor–liquid equilibrium behaviors of eight ternary systems were predicted by utilizing the binary interaction parameters of both models obtained from experimental data of the constituent binaries by both statistical and conventional methods, respectively. Results showed that better prediction accuracies were obtained for the DDLC model by statistical regression. Similarly, no improvements were found for the SRK equation of state by statistical regression. In addition, the superiority of the statistical regression over the conventional method was demonstrated by various simulated data.