Wavelet Neural Networks for Predicting Engine Emissions

In this work, a wavelet neural network (WNN) model of internal combustion engine emissions is presented. We collect data of a 1.6L spark ignition gasoline engine. The engine was coupled to a hydraulic dynamometer to control the engine speed in real time. Setting four parameters specifies the set point, so the engine speed, the injection time, the injected fuel mass flow and the angle of the admission throttle valve are the input variables to the engine model. The output parameters that were measured at the exhaust tile pipe are hydrocarbons (HC), carbon monoxide (CO) and nitrogen oxides (NOx). Performances of the different predictor models were evaluated using standard statistical evaluation criteria. The results showed that the use of wavelets neural networks can describe the emission behavior of the studied gasoline engine. High correlation values R2 of 0.9714, 0.9626 and 0.9929 were observed between the measured and predicted HC, CO and NOx exhaust emissions respectively.