Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network

Najafi, G. and Ghobadian, B. and Tavakoli, T. and Buttsworth, D. R. and Yusaf, T. F. and Faizollahnejad, M. (2009) Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy, 86 (5). pp. 630-639. ISSN 0306-2619

Abstract

This study deals with artificial neural network (ANN) modeling of a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results revealed that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model was developed based on standard Back-Propagation algorithm for the engine. Multi layer perception network (MLP) was used for non-linear mapping between the input and output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0004 by the model.


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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 Talal Yusaf
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering
Date Deposited: 12 Apr 2009 02:29
Last Modified: 02 Jul 2013 23:15
Uncontrolled Keywords: waste cooking biodiesel; biodiesel–diesel blends; artificial neural network; diesel engine
Fields of Research (FOR2008): 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
09 Engineering > 0902 Automotive Engineering > 090201 Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
Socio-Economic Objective (SEO2008): B Economic Development > 85 Energy > 8505 Renewable Energy > 850501 Biofuel (Biomass) Energy
Identification Number or DOI: doi: 10.1016/j.apenergy.2008.09.017
URI: http://eprints.usq.edu.au/id/eprint/5000

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