Table of Contents
Advances in Artificial Intelligence
Volume 2012, Article ID 610487, 7 pages
Research Article

Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester

1Department of Mechanical Engineering, MIT, Manipal 576104, India
2Department of Mechanical Engineering, NMAMIT, Nitte 574110, India

Received 12 May 2012; Accepted 21 September 2012

Academic Editor: Jun He

Copyright © 2012 Shiva Kumar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Radial basis function neural networks (RBFNNs), which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO). The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), exhaust gas temperature ( ), and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters.