Grey-Taguchi and ANN optimization in CI Engines using acetylene & biodiesel blends for Low Emissions and Better Performance

Abstract

This study aimed to reduce smoke and NOx emissions in a diesel engine fuelled with a 20% blend of Calophyllum inophyllum and Prosopis juliflora biodiesel (B20) with neat diesel, supplemented with acetylene at flow rates of 1, 2, and 3 liters per minute (lpm) in dual-fuel mode. Using the Grey-Taguchi method and an L9 (3^3) orthogonal array, the effects of compression ratio, fuel type, and acetylene flow rate were examined. Regression models were developed to predict brake thermal efficiency, smoke, and NOx emissions based on these controllable factors. The study found that the optimal individual values for NOx, brake thermal efficiency, and smoke were 2353 ppm, 31.52%, and 48.7 ppm, respectively. The best-combined results were achieved with a compression ratio of 17.5 and an acetylene flow rate of 3 lpm using the CI20 blend. The findings demonstrated significant improvements in output response factors when the optimal combination was applied, as validated by experimental and artificial neural network (ANN) simulations. The Grey-Taguchi approach proved effective in reducing emissions while enhancing engine performance.