doi: 10.7763/IJET.2010.V2.94
Performance and Exhaust Emissions Analysis of a Diesel Engine Using Methyl Esters of Fish Oil with Artificial Neural Network Aid
T. Hari Prasad
1
,
,
- 1 Sri Venkateswara College of Engineering, R.V.S nagar, tirupathi road, chittoor, Andhra Pradesh, India -517127
- 2 Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India
- 3 the swarnadra engineering college, Naraspuram, india
Abstract
This study deals with artificial neural network (ANN) modeling of a diesel engine to predict the exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a single cylinder, four-stroke test engine was fuelled with biodiesel blended with diesel and operated at different loads. Using some of the experimental data for training, an ANN model based on feed forward neural network for the engine was developed. Then, the performance of the ANN predictions were measured by comparing the predictions with the experimental results which were not used in the training process. It was observed that the ANN model can predict the engine exhaust emissions quite well with correlation coefficients, with very low root mean square errors. This study shows that, as an alternative to classical modeling techniques, the ANN approach can be used to accurately predict the performance and emissions of internal combustion engines.
Keywords
- Artificial
- neural
- network;
- diesel
- engine;
- biodiesel;
- methyl
- esters
- of
- fish
- oil;
- Exhaust
- emissions
How to Cite
T. Hari Prasad, K. Hema Chandra Reddy, and M. Muralidhara Rao, "Performance and Exhaust Emissions Analysis of a Diesel Engine Using Methyl Esters of Fish Oil with Artificial Neural Network Aid," International Journal of Engineering and Technology, vol. 2, no. 1, pp. 23-27, 2010. https://doi.org/ 10.7763/IJET.2010.V2.94
Copyright & License
Copyright © 2010 by the authors. 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 (CC BY 4.0).