Vapor pressure of pure substances is a crucial piece of information for many industrial applications. A recently developed PR+COSMOSAC equation of state (EOS) with its molecular interaction parameters determined from quantum mechanical calculations has been shown to provide reasonable prediction accuracy for vapor pressure of almost any chemical species without the issue of missing parameters. In this work, we introduce two modifications to improve its prediction accuracy on the vapor pressure. The overall deviation in pressure for 1124 pure liquids from the modified version is 138%, which is about 1/4 of that from the original model 553%. In particular, the accuracy for the vapor pressure near triple point shows major improvement, with the average error reduced from 1062% to 233%.The sublimation pressure can also be estimated providing that the melting temperature and enthalpy of fusion are available. The average deviation in sublimation pressure from the modified PR+COSMOSAC EOS for 1140 substances is 412%, which is only 1/3 of that from the original model (1249%). This model is capable of providing both the vapor pressure and sublimation pressure over a wide range of conditions (from the critical point to below the triple point). It is particularly useful when experimental data are not available.

No abstract

The PR+COSMOSAC EOS has been shown to be able to utilize quantum mechanical calculation results to predict the thermodynamic properties and fluid phase equilibrium with the only input of molecular structure. In this study, two modifications are introduced to further improve its accuracy in predicting vapor pressures of pure fluids. The average logarithmic deviation in vapor pressure (ALD-P) from the triple-point temperature to the normal boiling temperature for 1124 substances is reduced from 0.321 to 0.256 (or from 109.4% to 80.2%) (a reduction of 20% in ALD-P), while ALD-P from the normal boiling temperature to the critical temperature remains similar. The average absolute deviation (AAD) in the normal boiling temperature for 1405 substances is reduced from 16.32 to 14.25 K. Furthermore, its accuracy in predicting the critical properties and sublimation pressures (1140 substances) of pure fluids and vapor–liquid equilibrium of binary mixtures (1118 systems) is investigated and compared with the previous versions of PR+COSMOSAC. The accuracy of the revised PR+COSMOSAC EOS is generally improved, and the effect of each modification on the accuracy is discussed. This model is particularly useful when no experimental data are available.

No abstract

The predictive capability of gas and liquid solubility in organic polymers is examined based on the combination of the PR+COSMOSAC equation of state (EOS) and the COSMO-SAC liquid model through three different excess Gibbs free energy based mixing rules, modified Huron-Vidal (MHV1), Wong-Sandler (WS), and self-consistent mixing rule (SCMR). Using 81 binary systems consisting of 23 gas molecules and 22 polymers (81 data points) with temperatures ranging from 298 to 461 K, it is found that WS and SCMR can provide reasonable prediction accuracy (RMSE­(log10 k H) = 0.746 and 1.725, respectively) for the Henry’s law parameter in polymers, while the MHV1 mixing rule results in a much larger error (RMSE (log10 k H) = 3.118) compared to experiment. The WS and SCMR, but not MHV1, provide a converged value of Henry’s law parameter of gas in polymers as the molecular weight of the polymer increases. We further propose a modification to the SCMR (mSCMR) that results in significant improvement in the solubility prediction in polymers (RMSE (log10 k H) = 0.305) and the binary vapor–liquid equilibrium for common molecules. In this new approach, referred to as PRCS/mSCMR/COSMOSAC, all species-dependent parameters are determined from quantum mechanical (QM) calculations, and no adjustable parameters are required for the gas–polymer binary pairs. We believe that this new method may provide useful assistance to the development of polymer membrane-based gas separation processes especially when experimental information is not yet available.