The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa

This paper describes an experimental and computer simulation studies used to develop a suitable algorithm to predict and control the oxides of nitrogen (NO ) emitted from the Yanmar L60AE-D single cylinder direct injection diesel engine, fitted in a Cusson's Engine Test Bed Model P8160. NOx con...

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Main Authors: Mohammud, Mohd Mahadzir, Mustafa, Khairil Faizi
Format: Article
Language:en
Published: Universiti Teknologi MARA Cawangan Pulau Pinang 2003
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Online Access:https://ir.uitm.edu.my/id/eprint/4003/1/01_Esteem_Academic_Journal_Vol_1_2003.pdf
https://ir.uitm.edu.my/id/eprint/4003/
https://uppp.uitm.edu.my
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author Mohammud, Mohd Mahadzir
Mustafa, Khairil Faizi
author_facet Mohammud, Mohd Mahadzir
Mustafa, Khairil Faizi
author_sort Mohammud, Mohd Mahadzir
building Tun Abdul Razak Library
collection Institutional Repository
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
continent Asia
country Malaysia
description This paper describes an experimental and computer simulation studies used to develop a suitable algorithm to predict and control the oxides of nitrogen (NO ) emitted from the Yanmar L60AE-D single cylinder direct injection diesel engine, fitted in a Cusson's Engine Test Bed Model P8160. NOx contained in the exhaust gases of diesel engines have been identified as elements responsible for polluting our atmosphere. In order to reduce or to control diesel engine polluting emissions, the formation mechanism of NOx can be predicted. A neural network model is developed to obtain the NOx emission concentration under various operating condition. The neural network, well suited for non-linear phenomena modelization, is able to deal with high uncertainly input level and able to operate outside of their range of training experience. A feedforward neural network structure has been selected with a backpropagation training procedure. Four operating parameters (engine speed, engine load, exhaust temperature and air fuel ratio) have been used as an input data in the modelling process. The modelling algorithm implemented, takes a large set of measurements to learn how to predict the NOx emission from four operating parameters. The predicted values obtained using neural network model are compared with the experimental values. The studies show that the predicted results are in good agreement with experimental values, within less 9 % relative error.
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institution Universiti Teknologi Mara
language en
publishDate 2003
publisher Universiti Teknologi MARA Cawangan Pulau Pinang
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spelling my.uitm.ir-40032022-05-24T00:12:42Z https://ir.uitm.edu.my/id/eprint/4003/ The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa esteem Mohammud, Mohd Mahadzir Mustafa, Khairil Faizi TA Engineering. Civil engineering This paper describes an experimental and computer simulation studies used to develop a suitable algorithm to predict and control the oxides of nitrogen (NO ) emitted from the Yanmar L60AE-D single cylinder direct injection diesel engine, fitted in a Cusson's Engine Test Bed Model P8160. NOx contained in the exhaust gases of diesel engines have been identified as elements responsible for polluting our atmosphere. In order to reduce or to control diesel engine polluting emissions, the formation mechanism of NOx can be predicted. A neural network model is developed to obtain the NOx emission concentration under various operating condition. The neural network, well suited for non-linear phenomena modelization, is able to deal with high uncertainly input level and able to operate outside of their range of training experience. A feedforward neural network structure has been selected with a backpropagation training procedure. Four operating parameters (engine speed, engine load, exhaust temperature and air fuel ratio) have been used as an input data in the modelling process. The modelling algorithm implemented, takes a large set of measurements to learn how to predict the NOx emission from four operating parameters. The predicted values obtained using neural network model are compared with the experimental values. The studies show that the predicted results are in good agreement with experimental values, within less 9 % relative error. Universiti Teknologi MARA Cawangan Pulau Pinang 2003 Article NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/4003/1/01_Esteem_Academic_Journal_Vol_1_2003.pdf The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa. (2003) ESTEEM Academic Journal <https://ir.uitm.edu.my/view/publication/ESTEEM_Academic_Journal/>, 1: 1. ISSN 2289-4934 https://uppp.uitm.edu.my
spellingShingle TA Engineering. Civil engineering
Mohammud, Mohd Mahadzir
Mustafa, Khairil Faizi
The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa
title The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa
title_full The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa
title_fullStr The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa
title_full_unstemmed The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa
title_short The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa
title_sort prediction of diesel engine nox emissions using artificial neural network / mohd mahadzir mohammud and khairil faizi mustafa
topic TA Engineering. Civil engineering
url https://ir.uitm.edu.my/id/eprint/4003/1/01_Esteem_Academic_Journal_Vol_1_2003.pdf
https://ir.uitm.edu.my/id/eprint/4003/
https://uppp.uitm.edu.my
url_provider http://ir.uitm.edu.my/