Predicting octane number from species profiles: A deep learning model

Abstract

Recognizing that the calibration of octane number (ON) of a fuel by standard experimental testings is often challenging due to the lack of samples and the complexity of the experimental operating conditions, we propose herein the use of convolutional neural network (CNN) method for its prediction based on the time-resolved information contained in the profiles of some small combustion species (e.g., OH, HO2, CH2O) involved in constant volume autoignition. The approach first pre-processes the species profiles obtained from experiments or simulations as input parameters and then uses convolutional neural networks for feature extraction. The obtained features are concatenated with the corresponding temperature, pressure, and ignition delay time and fed into a multilayer perceptron neural network for ON prediction. The method is validated on data sets consisting of fuel blends and various single components, including alkanes, esters, alcohols, etc. Results show that the method exhibits a high accuracy for predicting the ON of not only single component fuels but also fuel mixtures with a mean absolute error of less than 2, and that parameter sharing allows the neural network to use few parameters while extracting some high-level semantic features. Furthermore, since the input information is some common small species, the method can make predictions for almost any fuel, especially for fuel blends whose information on physical parameters and molecular structure is not available.