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

A. S. Silitonga, H. H. Masjuki, Hwai Chyuan Ong, H. G. How, F. Kusumo, Y. H. Teoh, T.m. Indra Mahlia

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalSAE Technical Papers
Volume2015-November
Issue numberNovember
Publication statusPublished - 17 Nov 2015

Fingerprint

Diesel engines
Rails
Engines
Neural networks
Backpropagation algorithms
Engine cylinders
Biodiesel
Internal combustion engines
MATLAB
Esters
Testing

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

Cite this

Silitonga, A. S., Masjuki, H. H., Ong, H. C., How, H. G., Kusumo, F., Teoh, Y. H., & Mahlia, T. M. I. (2015). Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network. SAE Technical Papers, 2015-November(November).
Silitonga, A. S. ; Masjuki, H. H. ; Ong, Hwai Chyuan ; How, H. G. ; Kusumo, F. ; Teoh, Y. H. ; Mahlia, T.m. Indra. / Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network. In: SAE Technical Papers. 2015 ; Vol. 2015-November, No. November.
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abstract = "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.",
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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, Hwai Chyuan; How, H. G.; Kusumo, F.; Teoh, Y. H.; Mahlia, T.m. Indra.

In: SAE Technical Papers, Vol. 2015-November, No. November, 17.11.2015.

Research output: Contribution to journalArticle

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AU - Silitonga, A. S.

AU - Masjuki, H. H.

AU - Ong, Hwai Chyuan

AU - How, H. G.

AU - Kusumo, F.

AU - Teoh, Y. H.

AU - Mahlia, T.m. Indra

PY - 2015/11/17

Y1 - 2015/11/17

N2 - 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.

AB - 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.

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Silitonga AS, Masjuki HH, Ong HC, How HG, Kusumo F, Teoh YH et al. Engine Performance, Emission and Combustion in Common Rail Turbocharged Diesel Engine from Jatropha Curcas Using Artificial Neural Network. SAE Technical Papers. 2015 Nov 17;2015-November(November).