  Design and optimization of higher efficiency, lower-emission internal
combustion engines are highly dependent on fuel chemistry. Resolving chemistry
for complex fuels, like gasoline, is challenging. A solution is to study a fuel
surrogate: a blend of a small number of well-characterized hydrocarbons to
represent real fuels by emulating their thermophysical and chemical kinetics
properties. In the current study, an existing gasoline surrogate formulation
algorithm is further enhanced by incorporating novel chemometric models. These
models use infrared spectra of hydrocarbon fuels to predict octane numbers, and
are valid for a wide array of neat hydrocarbons and mixtures of such. This work
leverages 14 hydrocarbon species to form tailored surrogate palettes for the
Fuels for Advanced Combustion Engine (FACE) gasolines, including candidate
component species not previously considered: n-pentane, 2-methylpentane,
1-pentene, cyclohexane, and o-xylene. We evaluate the performance of "full" and
"reduced" surrogates for the 10 fuels for advanced combustion engine (FACE)
gasolines, containing between 8-12 and 4-7 components, respectively. These
surrogates match the target properties of the real fuels, on average, within 5
%. This close agreement demonstrates that the algorithm can design surrogates
matching the wide array of target properties: octane numbers, density,
hydrogen-to-carbon ratio, distillation characteristics, and proportions of
carbon-carbon bond types. We also compare our surrogates to those available in
literature (FACE gasolines A, C, F, G, I and J). Overall, the approach
demonstrated here offers a promising method to better design surrogates for
gasoline-like fuels with a wide array of properties.
