Analysis of the Possibility of Using Neural Networks to Monitor the Technical Efficiency of Diesel Engines During Operation

• Autorzy: Tucki Karol , Orynycz Olga , Świć Antoni , Wasiak Andrzej , Mruk Remigiusz , Gola Arkadiusz

Identyfikatory

DOI

10.12913/22998624/172003

• Języki publikacji: EN

Abstrakty

EN

The aim of the research was to analyse the possibility of using neural networks to determine the parameters of the chemical composition of exhaust gases as a function of engine performance parameters obtained from the on-board diagnostics system such as crankshaft speed and engine load index. The subject of the study was a Fiat Panda car equipped with a 1.3 Multijet diesel engine and powered by pure diesel. The tests used the MAHA MET 6.3 exhaust gas analyser and the on-board diagnostics system OBD II. The obtained values of NOx,O2,CO2 and PM measured behind the DPF were analysed. For the purpose of building a neural network model, preliminary studies were carried out in non-urban traffic (high-speed route). Based on the data obtained, processes of learning neural network structures with approximate properties with backward propagation of errors were carried out. Subsequently, tests were carried out on the operational parameters of the vehicle and the chemical composition of exhaust gases in urban traffic. Analysis of the obtained values of the average parameters obtained during the measurement and obtained on the basis of the prepared neural models allows to determine the relative differences at the level of not more than 10 percent.

Słowa kluczowe

EN

DPF; engine; vehicle; biofuel; fuel

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 6
• Strony: 1--15
• Opis fizyczny: Bibliogr. 56 poz., fig., tab.

Twórcy

autor

Tucki Karol

• Department of Production Engineering, Institute of Mechanical Engineering, Warsaw University of Life Sciences, ul. Nowoursynowska 164, 02-787 Warsaw, Poland

• https://orcid.org/0000-0001-7340-4100

autor

Orynycz Olga

• Department of Production Management, Faculty of Engineering Management, Bialystok University of Technology, Wiejska Street 45A, 15-351 Bialystok, Poland

autor

Świć Antoni

• Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland

• https://orcid.org/0000-0003-0405-4009

autor

Wasiak Andrzej

• Advisors Panel Production Engineering, Alternative Energy Sources Bialystok, 15-351 Bialystok, Poland

• https://orcid.org/0000-0002-0991-8665

autor

Mruk Remigiusz

• Department of Production Engineering, Institute of Mechanical Engineering, Warsaw University of Life Sciences, ul. Nowoursynowska 164, 02-787 Warsaw, Poland

• https://orcid.org/0000-0002-5026-6769

autor

Gola Arkadiusz

• Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-2935-5003

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).