CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network
Article
Article Title | CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network |
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ERA Journal ID | 4005 |
Article Category | Article |
Authors | Yusaf, Talal F. (Author), Buttsworth, D. R. (Author), Saleh, Khalid H. (Author) and Yousif, B. F. (Author) |
Journal Title | Applied Energy |
Journal Citation | 87 (5), pp. 1661-1669 |
Number of Pages | 9 |
Year | 2010 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0306-2619 |
1872-9118 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.apenergy.2009.10.009 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0306261909004371 |
Abstract | This study investigates the use of artificial neural network (ANN) modelling to predict brake power, torque, break specific fuel consumption (BSFC), and exhaust emissions of a diesel engine modified to operate with a combination of both compressed natural gas CNG and diesel fuels. A single cylinder, four-stroke diesel engine was modified for the present work and was operated at different engine loads and speeds. The experimental results reveal that the mixtures of CNG and diesel fuel provided better engine performance and improved the emission characteristics compared with the pure diesel fuel. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception network was used for non-linear mapping between the input and output parameters. It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.9884, 0.9838, 0.95707, and 0.9934 for the engine torque, BSFC, NOx and exhaust temperature, respectively. |
Keywords | CNG fuel; ANN; engine performance; engine emission |
ANZSRC Field of Research 2020 | 400205. Hybrid and electric vehicles and powertrains |
400202. Automotive engineering materials | |
400201. Automotive combustion and fuel engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Mechanical and Mechatronic Engineering |
University of Oxford, United Kingdom | |
Department of Agricultural, Civil and Environmental Engineering | |
University of Nottingham, United Kingdom |
https://research.usq.edu.au/item/9z493/cng-diesel-engine-performance-and-exhaust-emission-analysis-with-the-aid-of-artificial-neural-network
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