Response Surface Methodology based optimization of emission characteristics of SI engine fueled with blends of ethanol and Graphene oxide nanoparticles

Abstract

This study investigates the performance and emission characteristics of a spark-ignition (SI) engine operating on ethanol-gasoline blends, with the
addition of graphene oxide (GO) nanoparticles, to enhance engine performance and reduce emissions. The research examined the effects of varying ethanol blend
ratios and GO nanoparticle concentrations on key engine parameters, specifically brake thermal efficiency, brake specific fuel consumption, and emissions of
carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). Statistical models were developed using response surface methodology (RSM) to
accurately predict engine responses based on the input parameters. The optimization process identified specific combinations of ethanol blend ratios and GO
nanoparticle levels that achieved a favorable balance between improved efficiency and lower emissions. The results indicated that the lowest emissions were
achieved with 60 ppm GO concentration in E20 fuel, at a load of 10 kg and a speed of 2400 rpm, yielding a desirability value of 0.949, as determined by Design
Expert software, including ANOVA analysis. In conclusion, this research demonstrates the potential of RSM to optimize SI engine performance with ethanol-GO
blends, offering insights for more sustainable engine operation.