Octane Prediction from Infrared Spectroscopic Data

Abstract

A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline-ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (fuels for advanced combustion engines) gasoline blends were calculated using PIONA (paraffin, isoparaffin, olefin, naphthene, and aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial neural network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity.