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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| Language: | English |
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Taylor & Francis
2009
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| Online Access: | http://irep.iium.edu.my/39662/ http://irep.iium.edu.my/39662/ http://irep.iium.edu.my/39662/ http://irep.iium.edu.my/39662/1/Application_of_Artificial_Neural_Networks_%28ANN%29_for.pdf |
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iium-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 PeerReviewed 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 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| 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/ http://irep.iium.edu.my/39662/ http://irep.iium.edu.my/39662/ http://irep.iium.edu.my/39662/1/Application_of_Artificial_Neural_Networks_%28ANN%29_for.pdf |
| first_indexed |
2023-09-18T20:57:00Z |
| last_indexed |
2023-09-18T20:57:00Z |
| _version_ |
1777410370604892160 |