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Liquid transportation fuels require costly and time-consuming tests tocharacterize metrics, such as Research Octane Number (RON) for gasoline. Iffuel sale restrictions requiring use of standard Cooperative Fuel Researchtesting procedures do not apply, these tests may be avoided by usingmultivariate statistical models to predict RON and other quantities. Here weshow that an accurate statistical model for the RON of gasoline andgasoline-like fuels can be constructed by ensuring the representation of keyfunctional groups in the spectroscopic data set are used to train the model. Wefound that a principal component regression model for RON based on IRabsorbance and informed using neat and 134 mixtures of n-heptane, isooctane,toluene, ethanol, methylcyclohexane, and 1-hexene could predict RON for the 10Coordinating Research Council Fuels for Advanced Combustion Engine (FACE)gasolines and 12 FACE gasoline blends with ethanol within 34.8+/-36.1 onaverage and 51.2 in the worst case. We next studied the effect of adding 28additional minor components found in the FACE gasolines to the statisticalmodel, and determined that it was necessary to add additional representativesof the branched alkane and aromatics classes to reduce model error. Forexample, adding 2,3-dimethylpentane and xylene to the previous model allowed itto predict RON for the 22 target fuels within 0.3+/-4.4 on average and 7.9 inthe worst case. However, we determined that the specific choice of fuel inthose classes mattered less than ensuring the representation of the relevantfunctional group. This work builds upon previous efforts by creating modelsinformed by neat and surrogate fuels---rather than complex real fuels---thatcould predict the performance of complex unknown fuels.

Predicting fuel research octane number using Fourier-transform infrared absorption spectra of neat hydrocarbons