Correlation of PVR, Octane Numbers and Distillation Curve of Gasoline with Data from a Thermal Wave Interferometer

Correlations among thermophysical properties, ignition quality, volatility, chemical composition, and kinematic viscosity of petroleum distillates

“…The results showed that thermal diffusivi-92 ties can be used to distinguish among different gasoline grades.This capability probably arises from the clear correlation between 94 the thermal diffusivity and motor octane number of these fuels 95 that was identified by Vargas et al[10,11].Generalizing from the results obtained for gasoline, it seems 97 likely that thermophysical properties are also related to distillate 98 fuel quality. Seventeen oil sands-derived distillate streams were 99 characterized in the current investigation.…”

Octane Prediction from Infrared Spectroscopic Data

“…Another frontier of research for fuel property prediction comprises chemometric based studies. Analytical techniques such as FT-Raman spectroscopy, infrared spectroscopy, − near-IR and laser-induced fluorescence, dispersive fiber-optic Raman spectroscopy, dielectric spectroscopy, flame spectroscopy, thermal wave interferometer, and distillation curves have been used to predict octane number by multivariate methods. The standard in all of these studies is the partial least-squares regression (PLSR) algorithm or principal component regression (PCR).…”

Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks

“…Several correlations and methods have been reported in the literature to predict the ON of pure hydrocarbons, − PRFs, , toluene primary reference fuels (TPRFs), − gasoline compounds, naphtha, , gasolines, − gasoline with ethanol, ,− and petroleum fractions . The inputs for these models have been generated by utilizing different analytical techniques such as Fourier transform infrared (FT-IR) spectroscopy, ,,, flame emission spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, ,, dispersive fiber-optic Raman spectroscopy, dielectric spectroscopy, gas chromatography, distillation curves, thermal wave interferometry and ignition delay time (IDT) measured in an ignition quality tester (IQT) . The data from these techniques have been analyzed by a number of statistical and theoretical methods such as multiple linear regression (MLR), partial least-squares (PLS), quantitative structure property relationship (QSPR), , response surface methodology, , and artificial neural networks (ANNs) ,− to process the data and yield the prediction models.…”