Octane prediction from infrared spectroscopic data

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22 Scopus citations


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. ©
Original languageEnglish (US)
Pages (from-to)817-826
Number of pages10
JournalEnergy and Fuels
Issue number1
StatePublished - Jan 1 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was funded by the Office of Sponsored Research at King Abdullah University of Science and Technology (KAUST). We are thankful to Prof. Mani Sarathy and Dr. Abdul Gani Abdul Jameel for helpful discussions.


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