Screening oxygenates for favorable NOx/smoke trade-off in a DI diesel engine using multi response optimization

This study proposes the use of Grey-Taguchi based multi-response optimization to screen suitable diesel-oxygenate blends for achieving simultaneous reduction of smoke and NOx emissions with maximum performance in a DI diesel engine with minimum number of trials. The effects of factors such as oxygenate type, its blend proportion with diesel and retarded injection timing on emission and performance variables were considered. Three popular oxygenates viz., Diethyl ether (DEE), Dimethyl carbonate (DMC) and Diglyme (DGM) were screened. Response surface models (RSM) were developed using experimental data. Taguchi's signal-to-noise ratio approach was applied to predict optimal factor settings for all individual responses. RSM and predicted optimum factor levels were later validated by rigorous experimentation. It was found that DEE blends delivered best performance. Lowest smoke opacity was realized with DMC blends. NOx emissions were least for DEE blends. Higher DMC and DGM blends generated low HC emissions while lower DGM blends gave out low HC emissions at lower retarded injection timing. CO emissions were generally low for higher DMC blends. Smoke and NOx reducing capabilities of DGM are in between DEE and DMC. Finally it was experimentally validated that, Grey-Taguchi predicted combination of 10% DGM blend injected at 21 degrees CA, simultaneously reduced smoke opacity(del 29.17%) and NOx emissions(del 17.4%) with best performance(triangle 7%) when compared to baseline diesel operation. The results indicated that Grey-Taguchi method can be effectively used to screen oxygenates suitable to achieve the set objective with minimum number of trials saving cost and time. (C) 2017 Elsevier Ltd. All rights reserved.