Prediction of true critical temperature of multi-component mixtures: Extending fast estimation methods

Various fast estimation methods such as Chueh–Prausnitz, Grieves–Thodos, modified Wilson, and Redlich–Kister are available for predicting vapor–liquid critical temperature of fluid mixtures. In order to use these methods for a multi-component mixture, interaction parameters are required for each binary set of components. In the present work, 4524 experimental data on true critical temperature of 571 binary mixtures were collected through comprehensive literature search. Based on this data set, the available fast estimation methods were extended and new interaction parameters for each binary set were introduced. In order to extend the applicability of these methods, new correlations were developed to estimate interaction parameters. Finally, the capability of different methods was evaluated for predicting true critical temperature of 68 different multi-component mixtures with 427 data points.