Radial basis function neural networks in prediction and modeling of diesel engine emissions operated for biodiesel blends under varying operating conditions

R  Manjunatha; P  Badari Narayana; K  Hemachandra Reddy  andK  Vijaya Kumar Reddy

doi:10.17485/ijst/2012/v5i3.14

Article

Radial basis function neural networks in prediction and modeling of diesel engine emissions operated for biodiesel blends under varying operating conditions

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2012/v5i3.14

Year: 2012, Volume: 5, Issue: 3, Pages: 1-6

Review Article

Radial basis function neural networks in prediction and modeling of diesel engine emissions operated for biodiesel blends under varying operating conditions

R. Manjunatha^1*, P. Badari Narayana² , K. Hemachandra Reddy³ andK. Vijaya Kumar Reddy⁴

¹ Irrigation Department,
² Cybermotion Technologies Pvt. Ltd., Hyderabad-500034 AP, India.
³ Director-Academic and Planning, JNT University-Anantapur-515001 AP, India.
⁴ Deparment of Mechanical Engineering, [email protected]*
[email protected]
[email protected]
[email protected]
*Author For Correspondence
R. Manjunatha
Deparment of Mechanical Engineering,
Email: [email protected]*

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Biofuels are environmental friendly and their utilization addresses global concerns about containment of carbon emissions. Biodiesel certification involves analysing these emissions for various biodiesel blends in order to certify & recommend the new fuel to transport sector. Conducting the experiments for emission analysis is tedious and time consuming. Modeling the emissions operated for various biodiesel blends will help biodiesel manufacturers and certification authorities in analysing the possible pollutant levels. Artificial neural networks (ANN) can be used in modelling and prediction of biodiesel emissions operated under varying operating conditions. The objective of this research work is to design a neuro computing model to analyze the complex process of diesel engine emissions formation and estimate exhaust emissions operated with biodiesels under variable operating conditions. Experimental data of a single cylinder four stroke diesel engine run with various biodiesel blends has been used for training the network. During testing phase, emissions are predicted for new biodiesel & its blends. ANN developed is based on radial basis function neural networks (RBFNN). Predictive ability of this neural network is analysed using statistical analysis. The developed model has shown improved coefficient of determination (CoD) values of 0.99, 0.99, 0.96, 0.98 and 0.95 for NOx, HC, CO, CO2 and smoke emissions respectively. These results indicate that radial basis function neural networks are superior to the traditional back propagation algorithm based multi layer neural networks in terms of accuracy and efficiency performance. In this work, prediction & emission modelling of diesel engine operated with different biodiesel blends under varying operating conditions is successfully demonstrated. Hence, RBFNN can be used as a powerful virtual sensing technology tool for prediction & modelling of biodiesel emissions.
Keywords:ANN, biodiesel, radial basis function, coefficient of determination, MAPE.