Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid

Najafi, Gholamhassan and Ghobadian, Barat and Yusaf, Talal and Rahimi, Hadi (2007) Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid. American Journal of Applied Sciences, 4 (10). pp. 756-764. ISSN 1546-9239

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Abstract

[Abstract]: A comprehensive combustion analysis has been conducted to evaluate the performance of a commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini diesel engine using waste vegetable cooking oil as an alternative fuel. In order to compare the brake power and the torques values of the engine, it has been tested under same operating conditions with diesel fuel and waste cooking biodiesel fuel blends. The results were found to be very comparable. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The total sulfur content of the produced biodiesel fuel was 18 ppm which is 28 times lesser than the existing diesel fuel sulfur content used in the diesel vehicles operating in Tehran city (500 ppm). The maximum power and torque produced using diesel fuel was 18.2 kW and 64.2 Nm at 3200 and 2400 rpm respectively. By adding 20% of waste vegetable oil methyl ester, it was noticed that the maximum power and torque increased by 2.7 and 2.9% respectively, also the concentration of the CO and HC emissions have significantly decreased when biodiesel was used. An artificial neural network (ANN) was developed based on the collected data of this work. Multi layer perceptron network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. The results showed that the training algorithm of Back Propagation was sufficient enough in predicting the engine torque, specific fuel consumption and exhaust gas components for different engine speeds and different fuel blends ratios. It was found that the R2 (R: the coefficient of determination) values are 0.99994, 1, 1 and 0.99998 for the engine torque, specific fuel consumption,CO and HC emissions, respectively.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited with blanket permission of publisher.
Depositing User: Dr Talal Yusaf
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering
Date Deposited: 11 Oct 2007 01:07
Last Modified: 02 Jul 2013 22:44
Uncontrolled Keywords: alternative fuel; biodiesel-diesel blends; artificial neural network
Fields of Research (FOR2008): 09 Engineering > 0904 Chemical Engineering > 090405 Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
09 Engineering > 0902 Automotive Engineering > 090201 Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
URI: http://eprints.usq.edu.au/id/eprint/2483

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