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Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network

This paper investigates the performance, emission and combustion of a four cylinder common-rail turbocharged diesel engine using jatropha curcas biodiesel blends (JCB). The test was performed with various ratios of jatropha curcas methyl ester (JCME) in the blends (JCB10, JCB20, JCB30, and JCB50). A...

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Main Authors: Silitonga, A.S., Masjuki, H.H., Ong, H.C., How, H.G., Kusumo, F., Teoh, Y.H., Mahlia, T.M.I.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-61032018-03-19T06:19:21Z Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network Silitonga, A.S. Masjuki, H.H. Ong, H.C. How, H.G. Kusumo, F. Teoh, Y.H. Mahlia, T.M.I. This paper investigates the performance, emission and combustion of a four cylinder common-rail turbocharged diesel engine using jatropha curcas biodiesel blends (JCB). The test was performed with various ratios of jatropha curcas methyl ester (JCME) in the blends (JCB10, JCB20, JCB30, and JCB50). An artificial neural networks (ANN) model based on standard back-propagation algorithm was used to predict combustion, performance and emissions characteristics of the engine using MATLAB. To acquire data for training and testing of the proposed ANN, the different engine speeds (1500-3500 rpm) was selected as the input parameter, whereas combustion, performance and emissions were chosen as the output parameters for ANN modeling of a common-rail turbocharged diesel engine. The performance, emissions and combustion of the ANN were validated by comparing the prediction dataset with the experimental results. The results show that the correlation coefficient was successfully controlled within the range 0.9798-0.9999 for the ANN model and test data. The value of MAPE (Mean Absolute Percentage Error) was within the range 1.2373-6.4217 and the Root Mean Square (RSME) value was below 0.05 by the model, which is acceptable. This study shows that modeling techniques as an approach in alternative energy can give improvement advantage of reliability in the prediction of performance and emission of internal combustion engines. Copyright © 2015 SAE Japan and Copyright © 2015 SAE International. 2017-12-08T09:11:24Z 2017-12-08T09:11:24Z 2015 Article https://pure.uniten.edu.my/en/publications/engine-performance-emission-and-combustion-in-common-rail-turboch en_US Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network. SAE Technical Papers, 2015-November(November)
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
description This paper investigates the performance, emission and combustion of a four cylinder common-rail turbocharged diesel engine using jatropha curcas biodiesel blends (JCB). The test was performed with various ratios of jatropha curcas methyl ester (JCME) in the blends (JCB10, JCB20, JCB30, and JCB50). An artificial neural networks (ANN) model based on standard back-propagation algorithm was used to predict combustion, performance and emissions characteristics of the engine using MATLAB. To acquire data for training and testing of the proposed ANN, the different engine speeds (1500-3500 rpm) was selected as the input parameter, whereas combustion, performance and emissions were chosen as the output parameters for ANN modeling of a common-rail turbocharged diesel engine. The performance, emissions and combustion of the ANN were validated by comparing the prediction dataset with the experimental results. The results show that the correlation coefficient was successfully controlled within the range 0.9798-0.9999 for the ANN model and test data. The value of MAPE (Mean Absolute Percentage Error) was within the range 1.2373-6.4217 and the Root Mean Square (RSME) value was below 0.05 by the model, which is acceptable. This study shows that modeling techniques as an approach in alternative energy can give improvement advantage of reliability in the prediction of performance and emission of internal combustion engines. Copyright © 2015 SAE Japan and Copyright © 2015 SAE International.
format Article
author Silitonga, A.S.
Masjuki, H.H.
Ong, H.C.
How, H.G.
Kusumo, F.
Teoh, Y.H.
Mahlia, T.M.I.
spellingShingle Silitonga, A.S.
Masjuki, H.H.
Ong, H.C.
How, H.G.
Kusumo, F.
Teoh, Y.H.
Mahlia, T.M.I.
Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network
author_facet Silitonga, A.S.
Masjuki, H.H.
Ong, H.C.
How, H.G.
Kusumo, F.
Teoh, Y.H.
Mahlia, T.M.I.
author_sort Silitonga, A.S.
title Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network
title_short Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network
title_full Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network
title_fullStr Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network
title_full_unstemmed Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network
title_sort engine performance, emission and combustion in common rail turbocharged diesel engine from jatropha curcas using artificial neural network
publishDate 2017
_version_ 1644493842960875520
score 13.222552

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