Application of an enhanced Taguchi method for simultaneous reduction of smoke and NOx emissions using oxygenated additives and retarded injection timing in a stationary diesel engine

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作者
Saravanan Subramani
Rajesh Kumar Babu
机构
[1] Sri Venkateswara College of Engineering,Department Of Automobile Engineering
[2] Jeppiaar Institute of Technology,Department Of Mechanical Engineering
关键词
Diesel engine; Emission control; Oxygenated additives; Taguchi’s design; Optimisation; ANOVA;
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摘要
The present work employs an enhanced Weighted Signal-to-Noise (WSN) ratio method in conventional Taguchi design to determine an optimum combination of a diesel-oxygenate blend and injection timing in a stationary diesel engine with an objective to reduce NOx and smoke emissions simultaneously with best possible performance. An experimental design matrix based on Taguchi’s L9 orthogonal array was planned and experiments were conducted accordingly. Three parameters, namely oxygenate type, its blend composition with diesel and fuel injection timing with three levels, were chosen. NOx emissions, Smoke opacity, Brake thermal efficiency and Brake specific fuel consumption were taken as response variables. Diethyl ether (DEE), Dimethyl carbonate (DMC) and Diglyme (DGM) were chosen as oxygenates. The individual effects of each parameter in emissions and performance were investigated using analysis of variance. Confirmation experiments were conducted with the suggested optimum combination levels and the results were compared with baseline diesel engine. It was found that diesel with 10 % DGM blend injected at 21° crank angle (CA) is optimum for reducing smoke and NOx emissions simultaneously with best performance. Further, optimum parameter combination for lowest smoke opacity was predicted and confirmed to be 10 % DMC with diesel, injected at 21° CA. The lowest NOx emissions were obtained from 15 % DEE with diesel, injected at 17° CA and best performance from 10 % DEE with diesel, injected at 21° CA. The proposed WSN-based Taguchi method was experimentally verified and found to be effective in predicting an optimum blend for a chosen objective with 67 % reduction in experimentation time.
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页码:1893 / 1906
页数:13
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