Novel correlations for prediction of SARA contents of vacuum residues

Abstract

Vacuum residues (VRs) are among the heaviest petroleum fractions, and their proper characterization is of vital importance for a better understanding of their structure. Saturate, aromatic, resin, and asphaltene (SARA) analysis is widely used due to the unique information that can provide about chemical nature of petroleum fractions. However, the analysis is time-consuming and expensive, which may restrict its application. In this study, a new set of correlations has been developed for facile prediction of VRs’ SARA values. The correlations have been obtained through multi-linear (MLR) regression based on density, viscosity, and Conradson carbon residue (CCR) variables. The saturate and asphaltene contents are predicted by linear regression while the aromatic and resin contents are predicted by linear regression of asphaltene to aromatic (Asph/Aro) and asphaltene to resin (Asph/Res) ratios, respectively, and hence, is considered non-linear regression. The predicted SARA values are in good agreement with the measured values. The model is validated by a testing set of data from various worldwide VRs. The average absolute relative errors (AARE) of presented correlations for saturate, aromatic, resin, and asphaltene predictions are 0.1134, 0.0695, 0.2019, and 0.1736, respectively. Root mean squared errors (RMSE) of saturate, aromatic, resin, and asphaltene prediction are 3.9883, 3.9502, 2.2722, and 1.5288, respectively.