Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine

A neural networks (NN) model has been trained to predict the performance characteristics of a dual fuel internal combustion engine (ICE). In the network, back propagation (BP) neural network with Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) algorithms, single hidden-layer and logi...

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Main Authors: M I, JAHIRUL, Rashid, Muhammad Mahbubur, R , SAIDUR, H H, MASJUKI
Format: Article
Language:English
Published: Taylor & Francis 2009
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Online Access:http://irep.iium.edu.my/39662/1/Application_of_Artificial_Neural_Networks_%28ANN%29_for.pdf
http://irep.iium.edu.my/39662/
http://www.tandfonline.com/loi/thie20
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spelling my.iium.irep.396622015-01-03T01:52:52Z http://irep.iium.edu.my/39662/ Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine M I, JAHIRUL Rashid, Muhammad Mahbubur R , SAIDUR H H, MASJUKI TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering A neural networks (NN) model has been trained to predict the performance characteristics of a dual fuel internal combustion engine (ICE). In the network, back propagation (BP) neural network with Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) algorithms, single hidden-layer and logistic sigmoid transfer function has been used to optimise prediction model performance. The Neural Networks Toolbox of MATLAB 7 was used to train and test the NN model on a personal computer. In this investigation, a multi cylinder diesel engine was modified for duel fuel system to compare the experimental data with the prediction results obtained from NN model. Engine load, speed (rpm) and Diesel-NG ratio have been used as the input layers, while engine thermal efficiency, break specific fuel consumption (BSFC), exhaust temperature and air-fuel ratio have been used at the output layers. It is found that the RMS error values are smaller than 0.015, R2 values are about 0.999 and mean error smaller then 0.01% which indicate the NN model well matches with experimental results. The results of this investigation will be used to optimise the performance of future NG fueled engine. Taylor & Francis 2009-04-09 Article REM application/pdf en http://irep.iium.edu.my/39662/1/Application_of_Artificial_Neural_Networks_%28ANN%29_for.pdf M I, JAHIRUL and Rashid, Muhammad Mahbubur and R , SAIDUR and H H, MASJUKI (2009) Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine. The Hong Kong Institution of Engineers Transactions, 16 (1). pp. 14-20. ISSN 1023-697X (Print), 2326-3733 (Online) http://www.tandfonline.com/loi/thie20 10.1080/1023697X.2009.10668146
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
M I, JAHIRUL
Rashid, Muhammad Mahbubur
R , SAIDUR
H H, MASJUKI
Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine
description A neural networks (NN) model has been trained to predict the performance characteristics of a dual fuel internal combustion engine (ICE). In the network, back propagation (BP) neural network with Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) algorithms, single hidden-layer and logistic sigmoid transfer function has been used to optimise prediction model performance. The Neural Networks Toolbox of MATLAB 7 was used to train and test the NN model on a personal computer. In this investigation, a multi cylinder diesel engine was modified for duel fuel system to compare the experimental data with the prediction results obtained from NN model. Engine load, speed (rpm) and Diesel-NG ratio have been used as the input layers, while engine thermal efficiency, break specific fuel consumption (BSFC), exhaust temperature and air-fuel ratio have been used at the output layers. It is found that the RMS error values are smaller than 0.015, R2 values are about 0.999 and mean error smaller then 0.01% which indicate the NN model well matches with experimental results. The results of this investigation will be used to optimise the performance of future NG fueled engine.
format Article
author M I, JAHIRUL
Rashid, Muhammad Mahbubur
R , SAIDUR
H H, MASJUKI
author_facet M I, JAHIRUL
Rashid, Muhammad Mahbubur
R , SAIDUR
H H, MASJUKI
author_sort M I, JAHIRUL
title Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine
title_short Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine
title_full Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine
title_fullStr Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine
title_full_unstemmed Application of Artificial Neural Networks (ANN) for prediction the performance of a dual fuel internal combustion engine
title_sort application of artificial neural networks (ann) for prediction the performance of a dual fuel internal combustion engine
publisher Taylor & Francis
publishDate 2009
url http://irep.iium.edu.my/39662/1/Application_of_Artificial_Neural_Networks_%28ANN%29_for.pdf
http://irep.iium.edu.my/39662/
http://www.tandfonline.com/loi/thie20
_version_ 1643611679051743232
score 13.159267