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

Elumalai Ramachandran; Ravi Krishnaiah; Elumalai Perumal Venkatesan; Saboor Shaik; Chanduveetil Ahamed Saleel; Fayaz Hussain

doi:10.1021/acsomega.3c03890

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

ACS Omega. 2023 Oct 10;8(42):39067-39079. doi: 10.1021/acsomega.3c03890. eCollection 2023 Oct 24.

Authors

Elumalai Ramachandran¹, Ravi Krishnaiah¹, Elumalai Perumal Venkatesan², Saboor Shaik¹, Chanduveetil Ahamed Saleel³, Fayaz Hussain⁴

Affiliations

¹ School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India.
² Department of Mechaical Engineering, Aditya Engineering College, Surampalem 533437, India.
³ Department of Mechanical Engineering, College of Engineering, King Khalid University, PO Box 394, Abha 61421, Saudi Arabia.
⁴ Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam.

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.