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

TL;DR

This study develops a spectroscopic-based statistical model to accurately predict the Research Octane Number of gasoline fuels, reducing reliance on costly traditional testing by focusing on key functional groups in IR spectra.

Contribution

The paper introduces a principal component regression model trained on neat and surrogate hydrocarbons that effectively predicts RON for complex gasoline blends, emphasizing the importance of functional group representation.

Findings

Model predicts RON within 34.8+/-36.1 on average for test fuels.

Adding specific minor components improves prediction accuracy.

Representation of functional groups is more critical than specific fuel composition.

Abstract

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