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

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dc.citedby14
dc.contributor.authorNoor C.W.M.en_US
dc.contributor.authorMamat R.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid55956848000en_US
dc.contributor.authorid37057681900en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T06:52:01Z
dc.date.available2023-05-29T06:52:01Z
dc.date.issued2018
dc.descriptionBackpropagation algorithms; Brakes; Efficiency; Forecasting; Fuel consumption; Gas emissions; Load testing; Marine engines; Mathematical models; Neural networks; Nitrogen oxides; Artificial neural network models; Brake specific fuel consumption; Brake thermal efficiency; Coefficient of determination; Engine performance; Levenberg-Marquardt training algorithm; Marine Diesel Engines; Mean absolute prediction error; Diesel enginesen_US
dc.description.abstractThe 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. � 2018, ICIC International. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage969
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85046469566
dc.identifier.spage959
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85046469566&partnerID=40&md5=1a50abda8efb64031d1608f9089f42a1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23811
dc.identifier.volume14
dc.publisherICIC Internationalen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Innovative Computing, Information and Control
dc.titleComparative study of artificial neural network and mathematical model on marine diesel engine performance predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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