Predictive modelling through RSM for diesel engine using Al2O3 nanoparticles fuel blends

Metal oxide nanoparticles have becoming increasingly popular as an additive to diesel blend due to their numerous advantages. The current study aims to optimize and analyse the diesel engine characteristics by applying RSM optimization tool operating with Al2O3 nanoparticles incorporated fuel blends at various compression ratio and loads. The diesel engine’s input parameters were selected are compression-ratio, load, and Al2O3 nanoparticle concentration. The diesel engine compression-ratio was differed by 16.5–18.5, load varied by 25–100% and concentration of nanoparticles varied by 1–100 ppm. With RSM, multi-objective optimization has been done. The output responses were predicted by the mathematical models. The expected co-efficient of determinations for selected responses like BTE, BSFC, CO, HC, NOx, and CO2 are 98.94%, 97.65%, 96.27%, 95.38%, 99.76%, and 99.38%, respectively. Experimental evaluation of the engine output responses is established to fall within the acceptable range of error (< 5%). So, On the basis of study, it was concluded that the mathematically created model could be successfully used to forecast the aforesaid engine performance characteristics and found statistically fit.