Parametric optimization of variable compression ratio diesel engine run on waste cooking biodiesel with CeO2 nanoparticles using response surface methodology (RSM) technique

Due to the growing demand for viable and reliable energy sources, there has been a considerable focus on biodiesel as a potential alternative to traditional fossil fuels. This study optimizes the CI engine characteristics when utilizing nanoparticle-blended biodiesel, employing the Response Surface Methodology (RSM) with the Box-Behnken Design (BBD). The goal of adding CeO₂ nanoparticles to waste cooking biodiesel-diesel blends is to identify an optimal nanoparticle dosage which improve its characteristics also results in less emissions, and better combustion efficiency. The evaluation process assessed the key performance indicators and emission characteristics. The RSM-BBD methodology was employed to methodically examine the synergistic impacts of many input variables, such as nanoparticle concentration (50–100 ppm), engine load (25–100 %), and compression ratio (16:1–18:1) on key performance measures. The optimization findings demonstrated substantial enhancements in engine performance, combustion, and emissions decrease at CeO₂ concentration 75 ppm, engine load 100 %, and CR 18:1. The proposed model posses a desirability of 0.951 and the responses were found to be statistically fit having a difference of Adj-R² and Pre-R² less than 0.2. A confirmatory test is performed at the optimized conditions to which a forecasting error of 7 % is obtained. In the comparative study, the CO, HC and smoke emissions were observed to be reduced by 11.5 %, 7.9 % and 19.9 % than diesel.The combustion analysis showed that an increment of 34 % and 13 % was observed for B20 + 75CeO₂ blend than B20 incase of ICP and HRR. This study introduces a strong framework for improving biodiesel formulations, contributing to the progress of environmentally friendly and effective fuels.