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Real Time Prediction of Particle Sizing at the Exhaust of a Diesel Engine by Using a Neural Network Model
ISSN: 1946-3936, e-ISSN: 1946-3944
Published September 04, 2017 by SAE International in United States
Citation: Taglialatela, F., Lavorgna, M., Di Iorio, S., Mancaruso, E. et al., "Real Time Prediction of Particle Sizing at the Exhaust of a Diesel Engine by Using a Neural Network Model," SAE Int. J. Engines 10(4):2202-2208, 2017, https://doi.org/10.4271/2017-24-0051.
In order to meet the increasingly strict emission regulations, several solutions for NOx and PM emissions reduction have been studied. Exhaust gas recirculation (EGR) technology has become one of the more used methods to accomplish the NOx emissions reduction. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle size and density. These latter have a great importance both for the optimal functioning of after-treatment systems, but also for the adverse effects that small particles have on human health. Epidemiological studies, in fact, highlighted that the toxicity of particulate particles increases as the particle size decreases.
The aim of this paper is to present a Neural Network model able to provide real time information about the characteristics of exhaust particles emitted by a Diesel engine. In particular, the model acts as a virtual sensor able to estimate the concentration of particles with a specific aerodynamic diameter on the basis of some engine parameters such as engine speed, engine load and EGR ratio.