Simultaneous optimization of smoke and NOx emissions in a stationary diesel engine fuelled with diesel–oxygenate blends using the grey relational analysis in the Taguchi method

Abstract

The objective of the present study is to optimize the smoke and NO_x emissions simultaneously in a diesel engine fuelled with stationary diesel–oxygenate blends using the grey based Taguchi method. Experiments were carried out by adopting the Design of Experiments (DOE) method and tests were conducted based on Taguchi's L9 orthogonal array. The effects of three parameters, namely oxygen content of the additives, oxygenate proportion with diesel and varying injection timing, were investigated. Experimental trials were conducted by blending the various chosen oxygenates in different proportions with diesel and different injection timings. Taguchi's signal-to-noise (S/N) ratio was determined based on performance characteristics. The grey relational grade was obtained from the S/N ratio using the grey relational analysis (GRA). Based on this grade, the optimum level of factors was identified using response tables and response graphs. The individual effects of factors are estimated using analysis of variances (ANOVA). The results of the experiments reveal that diglyme blended with 10% diesel and injected at −21° crank angle is the optimum combination for the simultaneous reduction of smoke and NO_x with a less significant impact on performance. This combination shows a smoke reduction of 28.33% with a 17.4% reduction in NO_x emissions simultaneously with the best possible performance increase of 6.7% when compared to diesel. The combination of GRA and Taguchi parametric design can be effectively used to obtain the optimal combination of the chosen parameters. Experimental results also show that the response variables can be improved effectively through this approach.