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Combustion process can become more energy efficient and environment friendlyif used with appropriate fuel additive. Discovery of fuel additive can beaccelerated by applying hybrid approach of using of chemical kinetics andMachine Learning (ML). In this work, we present a framework that takes therobustness of Machine Learning and accuracy of chemical kinetics to predict theeffect of fuel additive on autoignition process. We present a case of makingpredictions for Ignition Delay Time (IDT) of biofuel n-butanol ($C_4H_9OH$)with several fuel additives. The proposed framework was able to predict IDT ofautoignition with high accuracy when used with unseen additives. This frameworkhighlights the potential of ML to exploit chemical mechanisms in exploring anddeveloping the fuel additives to obtain the desirable autoignitioncharacteristics.

Enhancing Autoignition Characteristics: A Framework to Discover Fuel Additives and Making Predictions Using Machine Learning