Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach
In this work, the thermal performance characteristics and emissions of VCR, one cylinder compression ignition (CI) engine fuelled with a mixture of two biodiesel in a blend with diesel has been assessed by using artificial neural network (ANN). Further, the two biodiesel considered are Pongamia and...
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doaj-b97ae051aec84a23948079d6d28e837f2021-06-02T19:59:46ZengElsevierAlexandria Engineering Journal1110-01682021-02-01601837844Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approachB.R. Hosamani0Syed Abbas Ali1Vadiraj Katti2HKE’s SLN College of Engineering and Technology, Raichur, IndiaSECAB Institute of Engineering and Technology, Vijaypur, India; Corresponding author.KLS’s VD Rural Institute of Technology, Haliyal, IndiaIn this work, the thermal performance characteristics and emissions of VCR, one cylinder compression ignition (CI) engine fuelled with a mixture of two biodiesel in a blend with diesel has been assessed by using artificial neural network (ANN). Further, the two biodiesel considered are Pongamia and Jatropha mixed in different volume ratio, i.e. 25:75, 50:50, 75:25 these mixtures are called M1, M2, and M3. The mixtures are used to prepare the various blends operated with diesel fuel, which are utilized in the experimentation. The engine experimental data required for the training and validation of ANN model are obtained through the VCR engine operated with pure diesel and blends of two biodiesel mixture as a fuel at different load and compression ratio. To train the ANN model, mixture ratio, blend ratio, load and compression ratio (CR), are selected as the inputs and output variables are brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), smoke density. Different architectures of ANN are trained by varying the number of hidden neurons in the hidden layer and corresponds to the minimum mean square error (MSE) for validation data for selecting the optimum architecture to estimate the parameters. The thermal performance and emissions of VCR engine estimated by using proposed ANN model are found to be quite close to experimental values with reasonable accuracy as the correlation coefficient is ranging from 0.97 to 0.99.http://www.sciencedirect.com/science/article/pii/S1110016820305263Artificial neural networkTwo biodiesel mixtureVariable compression ratioThermal performanceEmissions |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
B.R. Hosamani Syed Abbas Ali Vadiraj Katti |
spellingShingle |
B.R. Hosamani Syed Abbas Ali Vadiraj Katti Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach Alexandria Engineering Journal Artificial neural network Two biodiesel mixture Variable compression ratio Thermal performance Emissions |
author_facet |
B.R. Hosamani Syed Abbas Ali Vadiraj Katti |
author_sort |
B.R. Hosamani |
title |
Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach |
title_short |
Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach |
title_full |
Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach |
title_fullStr |
Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach |
title_full_unstemmed |
Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach |
title_sort |
assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: artificial neural network approach |
publisher |
Elsevier |
series |
Alexandria Engineering Journal |
issn |
1110-0168 |
publishDate |
2021-02-01 |
description |
In this work, the thermal performance characteristics and emissions of VCR, one cylinder compression ignition (CI) engine fuelled with a mixture of two biodiesel in a blend with diesel has been assessed by using artificial neural network (ANN). Further, the two biodiesel considered are Pongamia and Jatropha mixed in different volume ratio, i.e. 25:75, 50:50, 75:25 these mixtures are called M1, M2, and M3. The mixtures are used to prepare the various blends operated with diesel fuel, which are utilized in the experimentation. The engine experimental data required for the training and validation of ANN model are obtained through the VCR engine operated with pure diesel and blends of two biodiesel mixture as a fuel at different load and compression ratio. To train the ANN model, mixture ratio, blend ratio, load and compression ratio (CR), are selected as the inputs and output variables are brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), smoke density. Different architectures of ANN are trained by varying the number of hidden neurons in the hidden layer and corresponds to the minimum mean square error (MSE) for validation data for selecting the optimum architecture to estimate the parameters. The thermal performance and emissions of VCR engine estimated by using proposed ANN model are found to be quite close to experimental values with reasonable accuracy as the correlation coefficient is ranging from 0.97 to 0.99. |
topic |
Artificial neural network Two biodiesel mixture Variable compression ratio Thermal performance Emissions |
url |
http://www.sciencedirect.com/science/article/pii/S1110016820305263 |
work_keys_str_mv |
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