All modern process simulators rely on thermodynamic methods to estimate physical properties and calculate phase equilibria. The critical properties of individual components or pseudo-components, which represent undefined mixtures, play a crucial role in these calculations. However, the chemical compositions and characteristics of whole crude oils, petroleum fractions, and fuels, which are very complex mixtures of individual hydrocarbons, can vary significantly depending on the specific crude oil and the processing involved. For instance, straight-run petroleum fractions differ from those obtained through cracking processes due to differences in unsaturated hydrocarbon content. Consequently, effective methods for predicting critical temperature and pressure must account for a wide range of compositional scenarios. To address this challenge, we utilized a database of 176 individual hydrocarbons to evaluate the existing correlations for critical temperature and pressure calculations. Intercriteria analysis was performed to evaluate the relations between the different variables to be used for critical temperature and pressure predictions. Additionally, we proposed new correlations and ANN models for these properties and assessed their performance. Our study aims to provide robust predictive models that can accurately estimate critical properties across diverse petroleum fractions and compositions.
