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Abstract

A Fourier-Transform Raman spectrometer was used to collect the Raman spectra of (208) commercial petroleum fuels. The individual motor and research octane numbers (MON and RON, respectively) were determined experimentally using the industry standard ASTM knock engine method. Partial Least Squares (PLS) regression analysis can be used to build regression models which correlate the Raman spectra (175) of the fuels with the experimentally determined values for MON, RON, and pump octane number (the average of MON and RON) of the fuels. Each of the models was validated using leave-one-out validation. The standard errors of validation (SEV) are 0.415, 0.535, and 0.410 octane numbers for MON, RON, and pump octane number, respectively. By comparing the standard error of validation to the standard deviation for the experimentally determined octane numbers, it is evident that the accuracy of the Raman determined values is limited by the accuracy of the training set used in creating the models. The Raman regression models were used to predict the octane numbers for the fuels which were not used to build the models. The results compare favorably with the leave-one-out validation. Also, it is demonstrated that the experimentally determined Reid Vapor Pressures are highly correlated with the Raman spectra of the fuel samples and can be predicted with a standard error of 0.568 psi.

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