A Unified Volume Translation Model in Cubic Equations of State Applicable to Light-to-Heavy Compounds and Mixtures

Abstract Volume translation (VT) methods are widely used to improve the prediction accuracy of cubic equations of state (CEOSs); however, the existing VT models fail to reproduce the true critical molar volumes and often lose accuracy under extended temperature and pressure conditions. To overcome these limitations, we develop a unified distance-function-based VT model for CEOSs, leading to the volume-translated-rescaled CEOSs (VTR-CEOSs). These models not only exactly replicate the true critical molar volume but also maintain strong performance for both light-to-heavy pure compounds and mixtures over broad thermodynamic conditions. We then comprehensively compare the prediction capability of the proposed model against those of six representative volume translation models. The results demonstrate that our models can generally achieve the highest overall accuracy in predicting various properties. Moreover, the proposed VTR-CEOSs are verified to be thermodynamically consistent within the tested temperature and pressure ranges.