Journal of
Mechanical Engineering Research

  • Abbreviation: J. Mech. Eng. Res.
  • Language: English
  • ISSN: 2141-2383
  • DOI: 10.5897/JMER
  • Start Year: 2009
  • Published Articles: 118

Full Length Research Paper

Influence of injection timings on performance and emissions of a biodiesel engine operated on blends of Honge methyl ester and prediction using artificial neural network

Shiva Kumar
  • Shiva Kumar
  • Department of Mechanical Engineering MIT, Manipal, Karnataka, India.
  • Google Scholar
Srinivas Pai P
  • Srinivas Pai P
  • Department of Mechanical Engineering, NMAMIT, Nitte, Karnataka, India.
  • Google Scholar
Shrinivasa Rao B. R
  • Shrinivasa Rao B. R
  • Department of Mechanical Engineering, NMAMIT, Nitte, Karnataka, India.
  • Google Scholar


  •  Accepted: 07 January 2013
  •  Published: 31 January 2013

Abstract

 

In the present work, biodiesel prepared from Honge oil (Pongamia) was used as a fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine connected to an eddy current dynamometer. Experiments were conducted for different percentage of blends of Honge methyl ester with diesel at various compression ratios and at different injection timings.  Experimental investigation on the Performance parameters and Exhaust emissions from the engine were done. Artificial neural networks (ANNs) were used to predict the engine performance and emission characteristics of the engine. Separate models were developed for performance parameters as well as emission characteristics. To train the network compression ratio, blend percentage, percentage load and injection timings were used as the input variables whereas engine performance parameters and engine exhaust emissions were used as the output variables. Experimental results were used to train ANN. Results showed good correlation between the ANN predicted values and the desired values for various engine performance values and the exhaust emissions. Mean relative error values were less than 10 percent which is acceptable.

 

Key words: Honge methyl ester, transesterification, emissions, epochs, artificial neural network.