Performance And Emission Analysis Of Ci Engine Fuelled With Waste Cooking Oil Biodiesel Doped With Copper Oxide Nano Additives

Abstract

newline The present study is engaged to power diesel engines fuelled with Waste Cooking biodiesel-diesel blend. The work also displays the application of nano fuel additives in biodiesel-diesel blends for enhancing performance and lowering emissions. Waste Cooking Oil bioesters were prepared using the transesterification process and blends were prepared with diesel to form a designated mixture called WB20. Later, synthesized Copper oxide nano additives were added in the blend (WB20) at 10 mg/L (ppm), 20 mg/L (ppm), 30 mg/L (ppm), and 40 mg/L (ppm). So a total of 04 nano additive waste cooking oil biodiesel blends and base WB20 were compared with diesel for thermophysical properties. Further, these fuels (06 No s) were subjected to experimental studies on the abovementioned engine for variations in fuel injection pressure (180 - 240 bar, increments of 30 bar), fuel injection timing (21 - 25° bTDC, increment of 2°), and a compression ratio of engine (17 to 19, increments of 1). The data obtained from the experimentation was noted when only 1 engine parameter/factor was varied whose effect is to be determined and keeping the other two engine parameters at standard. The experimental data observed were the engine performance features such as BTHE, BSFC, EGT, and exhaust emittents after combustion which were CO2, CO, unburnt HC, NOX, and smoke intensity. The information noted was colossal and the analysis is complex as the responses found were conflicting in nature. For eg. High values of BTHE is advantageous but collaterally this leads to better combustion shoots the levels of NOx. So statistical analysis like the Response surface technique was used for multi-objective optimization. An L-9 DOE was formed based on Box-Behnken design. Mathematical regression equations of good coefficient of correlation were derived for various engine performance and emission responses. The polynomial equations of noted engine responses were correlated with the engine predictors and levels of nanoparticle addition. The response s