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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Main Authors: B.R. Hosamani, Syed Abbas Ali, Vadiraj Katti
Format: Article
Language:English
Published: Elsevier 2021-02-01
Series:Alexandria Engineering Journal
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016820305263
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spelling 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
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