Effects of artificial neural networks characterization on prediction of diesel engine emissions.

摘要

More than a century after its invention, diesel remains the fuel of choice for buses and freight trucks. Diesel exhaust contains three gases that are regulated by the United States Environmental Protection Agency (EPA), as well as particulate matter (PM). There is a societal need both to lower emissions and to predict or model emissions more accurately for inventory purposes. Engine modeling, and real time control are the most indispensable steps towards lowering engine emissions, and it is argued that this modeling can be achieved by implementation of Artificial Neural Networks (ANN). Effects of ANN design, architecture, and learning parameters on the accuracy of emissions predictions were studied along with the variation of embedded activation functions. An optimization strategy was followed to attain the most suitable network in the defined framework for five emissions of NOx, PM, HC, CO, and CO2. The emissions data were obtained from five engine transient test schedules, namely the E-CSHVR, ETC, FTP, E-Highway and E-WVU-5 Peak schedules. These were performed on a 550 hp General Electric DC engine dynamometer-testing unit at the West Virginia University Alternative Fuels, Engine and Emissions Research Center. The 3-Layer and Jump Connection networks were the most promising architectures and it was found that the radial basis functions such as the Gaussian and Gaussian Complement functions outperform the sigmoidal functions in all of the examined architectures. The accuracy of an excellent typical instance of CO2 prediction was as good as 0.009% error of accumulated value over the course of a FTP cycle.