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

The current work applies L9 (3(3)) 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 NOx 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.