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Strict regulations on air pollution motivates clean combustion research for fossil fuels. To numerically mimic real gasoline fuel reactivity, surrogates are proposed to facilitate advanced engine design and predict emissions by chemical kinetic modelling. However, chemical kinetic models could not accurately predict non-regular emissions, e.g. aldehydes, ketones and unsaturated hydrocarbons, which are important air pollutants. In this work, we propose to use machine-learning algorithms to achieve better predictions. Combustion chemistry of fuels constituting of 10 neat fuels, 6 primary reference fuels (PRF) and 6 FGX surrogates were tested in a jet stirred reactor. Experimental data were collected in the same setup to maintain data uniformity and consistency under following conditions: residence time at 1.0 second, fuel concentration at 0.25%, equivalence ratio at 1.0, and temperature range from 750 to 1100K.

An earlier paper has shown the ability to predict the phasing of knock onset in a gasoline PFI engine using a simple ignition delay equation for an appropriate surrogate fuel made up of toluene and PRF (TPRF). The applicability of this approach is confirmed in this paper in a different engine using five different fuels of differing RON, sensitivity, and composition - including ethanol blends. An Arrhenius type equation with a pressure correction for ignition delay can be found from interpolation of previously published data for any gasoline if its RON and sensitivity are known. Then, if the pressure and temperature in the unburned gas can be estimated or measured, the Livengood-Wu integral can be estimated as a function of crank angle to predict the occurrence of knock. Experiments in a single cylinder DISI engine over a wide operating range confirm that this simple approach can predict knock very accurately.