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

European Journal of Sustainable Development Research, Volume 2, Issue 2, Article No: 16.

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

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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.

Keywords

artificial neural networks, Taguchi, Karanja methyl ester, signal to noise ratio, absolute prediction error

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Citation

Beeravelli VN, Chanamala R, Rayavarapu UMR, Kancherla PR. An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine. European Journal of Sustainable Development Research. 2018;2(2):16. https://doi.org/10.20897/ejosdr/85412

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