Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel

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• 作者：K. Muralidharan ; D. Vasudevan
• 关键词：Biodiesel ; Variable compression ratio engine ; Performance ; Combustion ; Emission ; Artificial neural networks
• 刊名：Journal of the Brazilian Society of Mechanical Sciences and Engineering
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：37
• 期：3
• 页码：915-928
• 全文大小：618 KB
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• 作者单位：K. Muralidharan (1)
  D. Vasudevan (1)
  
  1. Department of Mechanical Engineering, PSNA College of Engineering and Technology, Dindigul, 624622, India
  
• 刊物主题：Mechanical Engineering;
• 出版者：Springer Berlin Heidelberg
• ISSN：1806-3691

文摘

The intention of this study is to predict the performance, emission and combustion characteristics of a single-cylinder, four-stroke variable compression ratio engine fuelled with waste cooking oil methyl ester and its blends—standard diesel with the aid of artificial neural network (ANN). The tests were performed with fuel blends of 20, 40, 60 and 80?% biodiesel with standard diesel, with an engine speed of 1,500?rpm and at compression ratios of 18:1, 19:1, 20:1, 21:1 and 22:1 under different loading conditions. Three different ANN models based on standard feed-forward back-propagation algorithm have been developed to predict the performance, emission and combustion characteristics of VCR engine. To train the network, compression ratio, blend percentage and percentage load were used as input parameters whereas engine parameters such as brake thermal efficiency, specific fuel consumption, brake power, indicated mean effective pressure, mechanical efficiency and exhaust gas temperature were used as output parameters for the performance model and exhaust emissions such as carbon dioxide, carbon monoxide, hydrocarbon and NO_x were used as output parameters for emission model. Separate model is developed for combustion characteristics in which compression ratio, blend percentage, load percentage and crank angle were used as the input parameters whereas combustion pressure, heat release rate, ignition delay, combustion duration and mass fraction burnt were used as the output parameters. This study shows that there is a good correlation between the ANN-predicted values and the experimental data for different engine performance, emission parameters and combustion characteristics.