Modeling and Grey Relational Multi-response Optimization of Chemical Additives and Engine Parameters on Performance Efficiency of Diesel Engine

Singular optimization of engine conditions for better performance have been studied extensively. However, in the practical sense, more than one characteristics are essential conditions. The current study investigates effect, optimization, and modeling on multi-characteristics a single cylinder-dual direct injection-water cooled diesel with help Taguchi-grey relational regression analyses. employed load, hydrogen, multi-walled carbon nanotubes (MWCNTs), ignition pressure, timing, at four different levels. analyzed were brake thermal efficiency (BTE), specific fuel consumption (BSFC), hydrocarbons (HC), nitrogen oxide (NOx), monoxide (CO), dioxide (CO2). results showed that there was similar behavioral pattern effect performance, except timing. optimal settings obtained 25% 20% 50 ppm MWCNTs, 220 bar 21 obTDC Interestingly, discovered did not fall within considered experimental runs, however, predicted 95% confidence bounds. It is recommended work based should be conducted to elucidate efficacy confirmation analysis. analysis variance load most significant factor overall having contribution 71.47%, followed by hydrogen MWCNTs. Also, pressure timing which need place attention factors performance. mathematical graphical design analysis, while interaction plots broader detailed