Effect of Boiling Point and Density Prediction Methods on Stochastic Reconstruction

Abstract

Stochastic reconstruction (SR) methods are used to generate a series of molecules that mimic the properties of complex mixtures using partial analytical data. Determining a quantitative composition using these methods is limited by the property prediction methods used. This paper addresses the use of two key measurements in the characterization of petroleum fractions, namely density and boiling point distributions. It is known that the different methods used in estimating these two basic properties have varying error rates. Boiling point prediction performances of the various group contribution methods were tested via the molecular library established for molecules that can be found present in the petroleum fractions. It has been observed that the combined use of these methods results in close to a 50% reduction in sum of squared errors than any single method. The predictive performances of the density calculation methods were similarly tested. The best-calculated density results were found via the Yen–Woods method with support from the linear mixing rule based on molar fractions.