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Modelling of Unburned Hydrocarbon Emission in a DI Diesel Engine Using Neural Networks
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on September 15, 2020 by SAE International in United States
The reduce in the toxic pollutant emissions in the exhaust gas of diesel engines is one of the main tasks of their designers. Toxic substances that are emitted in diesel exhaust, among others, are hydrocarbons. Reducing their emissions can be achieved by affecting the exhaust gases or reducing their formation in the combustion chamber. One of the methods is to change the control parameters of the fuel injection process. In present study, the direct injection diesel engine with a displacement of 1890 cm3 was tested. The diesel engine was equipped with a prototype common rail injection system, allowing injection of a fuel quantity divided into three parts during one engine working cycle. Each part can be injected at a certain time of injector opening at certain injection advance angle. As a result, this gives six different control parameters. Such a number of parameters results in a large number of combinations. Therefore, in order to cover the entire operating range of the engine, a very large number of measurements should be carried out. The application of the PS / DS-P: λ test plan has significantly reduced the number of necessary tests. Based on the test plan, learning data for the neural network was obtained. The next step was to develop and teach the structure of the neural network. The obtained neural network allows to generate output data for the input data of the network that is outside the training set. The model verification carried out on a test engine resulted in satisfactory results. The relative uncertainty did not exceed 8%, while the character of changes in the network output data was also kept when changing the input parameters. The developed model can be used in simulation tests of a diesel engine. In order to facilitate the identification of the obtained data, the results of simulation tests were presented on three-dimensional graphs.
- Krzysztof Balawender - Rzeszow University of Technology
- Adam Ustrzycki - Rzeszow University of Technology
- Kazimierz Lejda - Rzeszow University of Technology
- Mirosław Jakubowski - Rzeszow University of Technology
- Artur Jaworski - Rzeszow University of Technology
- Hubert Kuszewski - Rzeszow University of Technology
- Sylwia Siedlecka - Rzeszow University of Technology
- Edyta Zielińska - Rzeszow University of Technology