An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine
Journal Title: European Journal of Sustainable Development Research - Year 2018, Vol 2, Issue 2
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.
Authors and Affiliations
Venkata Narayana Beeravelli, Ratnam Chanamala, Uma Maheswara Rao Rayavarapu, Prasada Rao Kancherla
Keywords
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- EP ID EP323854
- DOI 10.20897/ejosdr/85412
- Views 118
- Downloads 0
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An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine
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 importa...