Grey-Taguchi and ANN based optimization of a better performing low-emission diesel engine fueled with biodiesel

ABSTRACT

The current work applies L9 (3³) Orthogonal Array (OA) of Taguchi design to obtain an optimum combination of nature of fuel, speed of engine, and load for DI- CI diesel engine that is alternatively fueled with waste cooking oil (WCO) based pure biodiesel (B100) & 20% blend of biodiesel with neat diesel (B20) with an objective to achieve maximum reduction in smoke and NO_x emissions and to enhance the output parameters like engine’s heat release, cylinder pressure and brake power with the conceivable dwindling of brake specific fuel consumption of the engine at various load conditions. The optimal input parameters (nature of fuel as B100, speed as 2300 rpm and load as 100%) were specified by the grey relational analysis in such a way that the preferred output results are successfully accomplished. Furthermore, analysis of variance (ANOVA) technique showed that the nature of fuel is the major leading factor with 44.28% impact on the output parameters. The experimental and ANN simulated results at the predicted optimal combination ensured the significant improvements of output response factors, thus validating the use of Grey-Taguchi method in reducing emissions and improving combustion and performance simultaneously.

KEYWORDS:

• Diesel engine
• waste cooking oil (WCO) biodiesels
• Grey-Taguchi Method (GTM)
• optimization
• Artificial Neural Network (ANN)

Acknowledgments

Authors are indebted National Natural Science Foundation (NNSF) of China for financial support under project No. 50576063 to implement Chinese indigenous emission standards in Beijing. All the experimental work was performed in the Laboratory of Auto Performance and Emission Test. School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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Funding

This work was supported by the National Natural Science Foundation (NNSF) of China; [50576063].