Empirical Vapor Pressure Prediction from Cubic Equations of State Using Similarity Variables

Vapor pressure prediction is important in a broad range of industrial applications, but general methods applying equations of state require iterative algorithms. In this work, a novel method for vapor pressure calculations was applied to the Soave-Redlich-Kwong (SRK) and Peng-Robinson (PR) equations of state. The approach used to solve this problem was to determine an empirical correlation between two similarity variables. These were an adjusted temperature variable which combined the effects of reduced temperature and acentric factor, and an adjusted pressure variable which combined reduced pressure and reduced temperature. A standard iterative vapor pressure algorithm using the EOS and a fugacity criterion was restated in terms of the new variables to generate a data set, which was fit to a variety of empirical expressions. Several promising empirical expressions were generated in this manner, and the deviation between their predictions and the numerical results from the algorithm were quantified. These expressions were capable of matching the vapor pressure predictions of SRK and PR with 0.1 percent relative deviation for values of adjusted temperature from the critical point down to 0.255. These results allow accurate estimation of vapor pressures by direct substitution into simple expressions without iterative algorithms.