Investigation into the Ideal Concoction for Performance and Emissions Enhancement of Jatropha Biodiesel-Diesel with CuO Nanoparticles Using Response Surface Methodology

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Abstract

The present work covers the preparation of biodiesel from jatropha oil through the transesterification process followed by its characterization, and furthermore, performance and emission analyses were done in terms of blending biodiesel with fossil diesel and CuO nanoparticles. Jatropha biodiesel blends (B10, B20, and B30) were chosen for this preliminary investigation based on the observation that B20 outperformed other blends. Next stage B20 with copper oxide (CuO) nanoparticle concentrations of 25, 50, 75, and 50 ppm are used to examine the performance and emission characteristics of a constant speed single cylinder, 4-stroke, 3.5 kW compression ignition (CI) engine. Finally, The response surface methodology (RSM) was utilized to determine the optimal nanoparticle concentration for B20. The results revealed that the blend of B20 with 80 ppm nanoparticles had the highest desirability (0.9732), and the developed RSM model was able to predict engine responses with a mean absolute percentage error (MAPE) of 3.113%. A confirmation test with an error in prediction of less than 5% verified the model’s adequacy. When comparing optimized B20CuO80 to diesel, brake specific energy consumption (BSEC) increased by 8.49% and brake thermal efficiency (BTE) was lowered by 3.34%. Hydrocarbon (HC), carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxide (NOx), and smoke emissions were reduced by 3.66% and 2.88%, 4.78%, 22.9%, and 20.54%, respectively, at 80% load. As a result, the B20 blend with nanoparticle concentrations of 80 ppm may be used in current diesel engines without engine modification.

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Ramachandran, E., Krishnaiah, R., Venkatesan, E. P., Shaik, S., Saleel, C. A., & Hussain, F. (2023). Investigation into the Ideal Concoction for Performance and Emissions Enhancement of Jatropha Biodiesel-Diesel with CuO Nanoparticles Using Response Surface Methodology. ACS Omega, 8(42), 39067–39079. https://doi.org/10.1021/acsomega.3c03890

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