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

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