Effect of Boiling Point and Density Prediction Methods on Stochastic Reconstruction

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.