Search Results

A novel total hydrocarbon (THC) emission concentration estimation model is proposed for reduction of engine development cost and simplification of exhaust measurements. The proposed method uses the absorbance spectra of a Fourier transform infrared (FTIR) spectrometer, which contains the information on a wide variety of hydrocarbons, as input. The model is based on machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) regression and bagging techniques. To train the model, we created a dataset containing pairs of a spectrum of engine exhaust gas and the THC concentration. In addition, we incorporate absorbance spectra of individual hydrocarbon components and several inorganic components so that the model learns the contribution of each hydrocarbon to THC concentration and to ignore interferences of irrelevant gas components.