A machine learning model to predict NOx emission for turbofan engines of commercial aircraft during idle condition

Abstract

A machine learning modelling approach to predict nitrogen oxides (NOx) emission was accomplished for turbofan engines of commercial aircraft during the idle condition utilizing International Civil Aviation Organization (ICAO) emission database. Being the first non-conventional emission modelling for the idle phase in the literature, the derived model relates the input parameters consisting of bypass ratio, engine pressure ratio, maximum rated thrust, and fuel flow rate of the considered turbofan engine with the output parameter, NOx emission index during the idle. Multi-layer perceptron neural networks (NNs) with one- and two-hidden-layer architectures were trained by various learning algorithms; namely, conjugate gradient, Levenberg-Marquardt, delta-bar-delta, back-propagation with momentum, and Quickprop algorithms, so as to obtain the optimal model. The estimated NOx emission index values provided a good fitting with the actual emission index values found in ICAO database while the most accurate model was achieved by the Quickprop algorithm trained two-hidden-layer NN