The Application of Neural Networks for Prediction of Concentration of Harmful Components in the Exhaust Gases of Diesel Engines

• Published in: Tehnički vjesnik Vol. 27; no. 1; pp. 262 - 269
• Main Authors: Grujic, Ivan, Stojanovic, Nadica, Doric, Jovan, Miloradovic, Danijela, Glisovic, Jasna
• Format: Journal Article Paper
• Language: English
• Published: Slavonski Baod University of Osijek 01.02.2020; Josipa Jurja Strossmayer University of Osijek; Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek; Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
• Subjects: Artificial neural networks; diesel engine; Diesel engines; Diesel motor industry; Engines; environment; Environmental law; Exhaust emission; Exhaust gases; Fossil fuels; Health; Internal combustion engine industry; Internal combustion engines; Laws, regulations and rules; legal regulations; Medical law; Neural networks; Pollutants; Power (Philosophy); road traffic; Traffic engineering; transport; Transport vehicles; Vehicles; Serbia; Switzerland
• ISSN: 1330-3651; 1848-6339
• DOI: 10.17559/TV-20181126111859

Means of transport (vehicles) are a part of everyday life. The main purpose of the vehicles is to transport the goods and the passengers. The road traffic has the largest share in transport. Majority of the transport vehicles use the IC engine as a power unit. The application of engines driven by fossil fuels has caused the road traffic to exhibit its poor side in terms of harmful effect on environment and human health. The constant tightening of the legal regulations has led the engine manufacturers to continuously improve the IC engines. Determination of the exhaust emission parameters usually requires an experiment. The possibility of neural network application for pollutants concentration prediction in the exhaust gases from diesel engines based on experimental data is considered in this paper. Some inputs, not previously considered in the reviewed literature, are introduced into the developed model of the neural network. By comparison with experimental data, it was established that the developed neural network model can successfully predict concentration of pollutants and that it can be used for future research.