Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
