CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network

Yusaf, Talal F. and Buttsworth, D. R. and Saleh, Khalid H. and Yousif, B. F. (2010) CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network. Applied Energy, 87 (5). pp. 1661-1669. ISSN 0306-2619

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Author's version unavailable.
Depositing User: Dr Belal Yousif
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering
Date Deposited: 08 Feb 2010 05:57
Last Modified: 02 Jul 2013 23:28
Uncontrolled Keywords: CNG fuel; ANN; engine performance; engine emission
Fields of Research (FOR2008): 09 Engineering > 0902 Automotive Engineering > 090205 Hybrid Vehicles and Powertrains
09 Engineering > 0902 Automotive Engineering > 090202 Automotive Engineering Materials
09 Engineering > 0902 Automotive Engineering > 090201 Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: doi: 10.1016/j.apenergy.2009.10.009
URI: http://eprints.usq.edu.au/id/eprint/6064

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