Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction

The investigation of marine diesel engines is still limited and considered new in both: physical testing and prediction. Therefore, this study deals with an artificial neural network (ANN) modeling for a marine diesel engine performance prediction such as the brake power (BP), brake specific fuel co...

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Main Authors:	C. W., Mohd Noor, R., Mamat, Ahmed, Ali Najah
Format:	Article
Language:	English
Published:	ICIC International 2018
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/21172/ http://umpir.ump.edu.my/id/eprint/21172/ http://umpir.ump.edu.my/id/eprint/21172/ http://umpir.ump.edu.my/id/eprint/21172/1/Publication%204_IJICIC.pdf

id	ump-21172
recordtype	eprints
spelling	ump-211722018-05-21T02:33:17Z http://umpir.ump.edu.my/id/eprint/21172/ Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction C. W., Mohd Noor R., Mamat Ahmed, Ali Najah TJ Mechanical engineering and machinery The investigation of marine diesel engines is still limited and considered new in both: physical testing and prediction. Therefore, this study deals with an artificial neural network (ANN) modeling for a marine diesel engine performance prediction such as the brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), volumetric efficiency (VE), exhaust gas temperature (EGT) and nitrogen oxide (NOX) emissions. Input data for network training was gathered from laboratory engine testing operated at various speed, load and fuel blends. ANN prediction model was developed based on standard back-propagation with Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the experimental data and the output from the mathematical model. Results showed that the ANN model provided a good agreement to the experimental data with the coefficient of determinations (R2) of 0.99. Mean absolute prediction error (MAPE) of ANN and the mathematical model is between 1.57-9.32% and 4.06-28.35% respectively. These values indicate that the developed ANN model is more reliable and accurate than the mathematical model. The present study reveals that the ANN approach can be used to predict the performance of marine diesel engine with high accuracy. ICIC International 2018 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/21172/1/Publication%204_IJICIC.pdf C. W., Mohd Noor and R., Mamat and Ahmed, Ali Najah (2018) Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction. International Journal of Innovative Computing, Information and Control, 14 (3). pp. 959-969. ISSN 1349-4198 http://www.ijicic.org/ijicic-140313.pdf DOI: 10.24507/ijicic.14.03.959
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery C. W., Mohd Noor R., Mamat Ahmed, Ali Najah Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction
description	The investigation of marine diesel engines is still limited and considered new in both: physical testing and prediction. Therefore, this study deals with an artificial neural network (ANN) modeling for a marine diesel engine performance prediction such as the brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), volumetric efficiency (VE), exhaust gas temperature (EGT) and nitrogen oxide (NOX) emissions. Input data for network training was gathered from laboratory engine testing operated at various speed, load and fuel blends. ANN prediction model was developed based on standard back-propagation with Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the experimental data and the output from the mathematical model. Results showed that the ANN model provided a good agreement to the experimental data with the coefficient of determinations (R2) of 0.99. Mean absolute prediction error (MAPE) of ANN and the mathematical model is between 1.57-9.32% and 4.06-28.35% respectively. These values indicate that the developed ANN model is more reliable and accurate than the mathematical model. The present study reveals that the ANN approach can be used to predict the performance of marine diesel engine with high accuracy.
format	Article
author	C. W., Mohd Noor R., Mamat Ahmed, Ali Najah
author_facet	C. W., Mohd Noor R., Mamat Ahmed, Ali Najah
author_sort	C. W., Mohd Noor
title	Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction
title_short	Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction
title_full	Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction
title_fullStr	Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction
title_full_unstemmed	Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction
title_sort	comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction
publisher	ICIC International
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21172/ http://umpir.ump.edu.my/id/eprint/21172/ http://umpir.ump.edu.my/id/eprint/21172/ http://umpir.ump.edu.my/id/eprint/21172/1/Publication%204_IJICIC.pdf
first_indexed	2023-09-18T22:30:58Z
last_indexed	2023-09-18T22:30:58Z
_version_	1777416282330628096

Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction

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