Development of THC estimation model using FTIR spectrum

Abstract

A novel total hydrocarbon (THC) emission concentration estimation model is proposed for reduction of engine development cost as well as simplification of measurement system. The model is based on machine learning algorithm including the least absolute shrinkage and selection operator (LASSO) regression and bagging techniques. Major features of the proposal model are taking the absorbance spectrum of Fourier transform infrared (FTIR) spectrometer as input and incorporating not only spectra of the engine exhaust gas but also those of individual hydrocarbon and inorganic gas components as training data set. This method was validated on the exhaust gas before the catalyst of a gasoline engine. The results show an error of less than 5% in both steady and transient operating conditions, outperforming the 20 % error of conventional regression model using only the reference hydrocarbon concentrations. We also evaluate the contribution to performance improvements in THC estimation of employing FTIR spectrum and incorporating spectrum information of gas components, respectively.

Keywords

• thc
• ftir
• estimation model
• machine learning
• lasso
• bagging