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

Combustion process can become more energy efficient and environment friendly if used with appropriate fuel additive. Discovery of fuel additive can be accelerated by applying hybrid approach of using of chemical kinetics and Machine Learning (ML). In this work, we present a framework that takes the robustness of Machine Learning and accuracy of chemical kinetics to predict the effect of fuel additive on autoignition process. We present a case of making predictions for Ignition Delay Time (IDT) of biofuel n-butanol (C4H9OH) with several fuel additives. The proposed framework was able to predict IDT of autoignition with high accuracy when used with unseen additives. This framework highlights the potential of ML to exploit chemical mechanisms in exploring and developing the fuel additives to obtain the desirable autoignition characteristics.