Performance optimization of an engine for canola oil blended diesel with Al2O3 nanoparticles through single and multi-objective optimization techniques

In the present work, a four-stroke single-cylinder diesel engine's combustion and emission performance are improved through optimization techniques. The diesel engine is fueled with canola oil blended diesel with aluminium oxide nanoparticles. Taguchi's signal to noise (S/N) ratio method, grey relational method, and response surface method are employed to maximize the brake thermal efficiency (BTE) and minimize the nitrogen oxide (NOx) emissions and fuel cost. Canola oil blend percentage, nanoparticles' concentration, fuel injection pressure, and fuel injection timing are the considered influencing parameters. The predicted optimum values are compared for validity and found a fair agreement. Finally, the best value is identified as Canola oil blend percentage = 18.8%, concentration of nanoparticles = 30 ppm, fuel injection pressure = 220 bar and fuel injection timing = 21 degrees before top dead center (bTDC) and this combination improve BTE by 16% and reduce the NOx by 3% with reference to diesel fuel.

Automated summary

By using canola oil blended fuel and aluminum oxide nanoparticles, optimization techniques effectively improve the combustion and emission performance of a diesel engine. Parameters such as canola oil blend percentage, nanoparticles concentration, fuel injection pressure, and fuel injection timing were considered in the optimization process, leading to the identification of the best value combination.