Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

The changes in the performance, emission and combustion characteristics of bioethanol-safflower biodiesel and diesel fuel blends used in a common rail diesel engine were investigated in this experimental study. E20B20D60 (20% bioethanol, 20% biodiesel, 60% diesel fuel by volume), E30B20D50, E50B20D3...

Full description

Bibliographic Details
Main Author: Hasan Aydogan
Format: Article
Language:English
Published: University of Cape Town 2017-04-01
Series:Journal of Energy in Southern Africa
Online Access:https://journals.assaf.org.za/jesa/article/view/2198
id doaj-912385936c0f48bebe071b3a35ca2813
record_format Article
spelling doaj-912385936c0f48bebe071b3a35ca28132020-11-24T21:33:58ZengUniversity of Cape TownJournal of Energy in Southern Africa1021-447X2413-30512017-04-01262748310.17159/2413-3051/2015/v26i2a21982198Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural networkHasan Aydogan0University of Cape TownThe changes in the performance, emission and combustion characteristics of bioethanol-safflower biodiesel and diesel fuel blends used in a common rail diesel engine were investigated in this experimental study. E20B20D60 (20% bioethanol, 20% biodiesel, 60% diesel fuel by volume), E30B20D50, E50B20D30 and diesel fuel (D) were used as fuel. Engine power, torque, brake specific fuel consumption, NOx and cylinder inner pressure values were measured during the experiment. With the help of the obtained experimental data, an artificial neural network was created in MATLAB 2013a software by using back-propagation algorithm. Using the experimental data, predictions were made in the created artificial neural network. As a result of the study, the correlation coefficient was found as 0.98. In conclusion, it was seen that artificial neural networks approach could be used for predicting performance and emission values in internal combustion engines.https://journals.assaf.org.za/jesa/article/view/2198
collection DOAJ
language English
format Article
sources DOAJ
author Hasan Aydogan
spellingShingle Hasan Aydogan
Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
Journal of Energy in Southern Africa
author_facet Hasan Aydogan
author_sort Hasan Aydogan
title Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
title_short Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
title_full Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
title_fullStr Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
title_full_unstemmed Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
title_sort prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
publisher University of Cape Town
series Journal of Energy in Southern Africa
issn 1021-447X
2413-3051
publishDate 2017-04-01
description The changes in the performance, emission and combustion characteristics of bioethanol-safflower biodiesel and diesel fuel blends used in a common rail diesel engine were investigated in this experimental study. E20B20D60 (20% bioethanol, 20% biodiesel, 60% diesel fuel by volume), E30B20D50, E50B20D30 and diesel fuel (D) were used as fuel. Engine power, torque, brake specific fuel consumption, NOx and cylinder inner pressure values were measured during the experiment. With the help of the obtained experimental data, an artificial neural network was created in MATLAB 2013a software by using back-propagation algorithm. Using the experimental data, predictions were made in the created artificial neural network. As a result of the study, the correlation coefficient was found as 0.98. In conclusion, it was seen that artificial neural networks approach could be used for predicting performance and emission values in internal combustion engines.
url https://journals.assaf.org.za/jesa/article/view/2198
work_keys_str_mv AT hasanaydogan predictionofdieselengineperformanceemissionsandcylinderpressureobtainedusingbioethanolbiodieseldieselfuelblendsthroughanartificialneuralnetwork
_version_ 1725950979938975744

Similar Items