Multivariate models based on infrared spectra as a substitute for oil property correlations to predict thermodynamic properties: evaluated on the basis of the narrow-boiling fractions of Kukersite retort oil

. This article investigates a potential for using models based on infrared spectra to predict basic thermodynamic properties of narrow boiling range oil fractions or pseudocomponents. The work took advantage of the simultaneous availability of a property database of narrow boiling range fractions of Kukersite oil shale retort oil (from the industrial retorting process) together with infrared spectra of these fractions. The work was based on the hypothesis that the models based on infrared spectra could potentially be used to reduce experimental data when developing other predictive methods, or even as a substitute for other prediction methods. In this study four basic oil properties, which are often used to predict other thermodynamic properties, were predicted from infrared spectra using support vector regression. These were specific gravity, refractive index parameter, average boiling point and molecular weight. According to bulk property prediction approach these selected properties can be grouped into energy parameters (two former) and size parameters (two latter). It was found that, for distillation fractions with varying compositions, both the energy parameters (specific gravity, refractive index) as well as the size parameters (molecular weight, average boiling point) can be predicted from Fourier transform infrared (FTIR) spectra, and that the accuracy of the predictions based on infrared spectra was comparable with the accuracies of petroleum bulk property correlations. Thus, infrared spectra can provide a convenient alternative in the thermodynamic property prediction field because they can be easily measured and correlated to a wide variety of properties.