Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network
Article
Article Title | Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network |
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ERA Journal ID | 4005 |
Article Category | Article |
Authors | Najafi, G. (Author), Ghobadian, B. (Author), Tavakoli, T. (Author), Buttsworth, D. R. (Author), Yusaf, T. F. (Author) and Faizollahnejad, M. (Author) |
Journal Title | Applied Energy |
Journal Citation | 86 (5), pp. 630-639 |
Number of Pages | 10 |
Year | 2009 |
Publisher | Elsevier |
Place of Publication | United States |
ISSN | 0306-2619 |
1872-9118 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.apenergy.2008.09.017 |
Web Address (URL) | http://dx.doiorg/10.1016/j.apenergy.2008.09.017 |
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. |
Keywords | waste cooking biodiesel; biodiesel–diesel blends; artificial neural network; diesel engine |
ANZSRC Field of Research 2020 | 401703. Energy generation, conversion and storage (excl. chemical and electrical) |
400201. Automotive combustion and fuel engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Tarbiat Modares University, Iran |
University of Oxford, United Kingdom | |
Department of Mechanical and Mechatronic Engineering |
https://research.usq.edu.au/item/9yz44/performance-and-exhaust-emissions-of-a-gasoline-engine-with-ethanol-blended-gasoline-fuels-using-artificial-neural-network
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