Influence of varying concentrations of TiO(2) nanoparticles and engine speed on the performance and emissions of diesel engine operated on waste cooking oil biodiesel blends using response surface methodology

For a few decades now fast depleting fossil fuels has been a major challenge. Fast expanding population and increased rate of urbanization has increased energy demand. This makes the current scenario worse. Fossil fuels' emissions are another challenge. Apart from fossil fuel emissions, the untreated disposal of waste cooking oil presents another environment’s sustainability challenge. The treatment of waste cooking oil as fuel presents a tangible solution to challenge. In this research article, impact of the engine speed and the concentration of titanium dioxide (TiO(2)) nanoparticles (NPs) in diesel-biodiesel blended fuels on the engine’s performance. The emission characteristics of a single-cylinder four-stroke diesel engine has also been examined. TiO(2) NPs were produced by a sol-gel methodology. The diesel-biodiesel combination was fortified with TiO(2) NPs at 40, 80 and 120 ppm. These mixtures were used to power the diesel engine, which was then run at 1150, 1400, 1650, 1900 and 2150 RPM. Interaction between engine speeds and nanoparticle concentrations and investigation of their combined effect on engine performance and emissions was done using response surface methodology. The minimum BSFC of 0.33994 kg/kWh and maximum BTE of 25.90% were found for B30 + 120 ppm biodiesel blend at 2150 rpm as compared to all other tested fuels. The emissions including CO and HC emissions were recorded as 25.61486 kg/kWh and 0.05289kg/kWh respectively at 2150 rpm for B30 + 120 ppm biodiesel blend while NO(x) on the contrary side exhibits a slight escalation with increasing engine speed and nanoparticles concentration. The findings of the experiments demonstrated that adding TiO(2) nanoparticles to diesel–biodiesel blends is an effective way to enhance the performance of diesel engines while simultaneously reducing the emissions. It was also discovered that the mathematical model that was built can efficiently estimate the performance of the engine and the emission levels.