An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine
Venkata Narayana Beeravelli 1 * , Ratnam Chanamala 2, Uma Maheswara Rao Rayavarapu 3, Prasada Rao Kancherla 4
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1 Gate Engineering College, Nalgonda, Kodad, INDIA
2 Andhra University, Andhra Pradesh, INDIA
3 Sasi Institute of Technology and Engineering, Tadepalligudem, Andhra Pradesh, INDIA
4 NRI Institue, Vijayawada, JNTUK, Andhra Pradesh, INDIA
* Corresponding Author

Abstract

Prediction of operating parameters as a function of brake thermal efficiency (BTHE), brake specific fuel consumption (BSFC), carbon monoxide (CO), Hydrocarbons (HC), nitrogen oxide (NOX) and Smoke opacity is very important in performance and emission characteristics of the engine. In this study, the effect of operating parameters such as load, blend, compression ratio (CR), injection pressure (IP) and injection timing (IT) on the output responses above mentioned were investigated by using ANN (Artificial neural networks) and trained the signal- to- noise ratio (S/N) results obtained from Taguchi L16 orthogonal design. These results are compared with the artificial neural network and confirmation test was conducted and the results obtained were well supported.

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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.

Article Type: Research Article

https://doi.org/10.20897/ejosdr/85412

, 2018 - Volume 2 Issue 2, Article No: 16

Publication date: 08 Mar 2018

Article Views: 2095

Article Downloads: 1944

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