Impact of synthesizing surfactant-modified catalytic ceria nanoparticles on the performance and environmental behaviors of coconut oil/diesel-fueled CI engine: An optimization attempt

Abstract

The present study deals with the characterization as well as synthesis of surfactant-modified catalytic ceria nanoparticles and their application as fuel additives in a 4-stroke diesel engine fueled by biodiesel obtained from coconut oil. The synthesis of coconut oil biodiesel is made possible by the transesterification process and its chemical tests such as Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), and Fourier-transform infrared spectroscopy (FTIR), and their physiochemical properties are carried out to verify and confirm its suitability as fuel. Cerium oxide (CeO2) is selected as a catalytic nanoparticle and is synthesized by the precipitation method. The characterization technique which includes Scanning electron microscopy (SEM), high-resolutions transmission electron microscope (HR-TEM), X-ray diffraction spectroscopy (XRD), energy dispersive spectroscopy (EDS), FTIR, and zeta potential (ZP) analyses were used to examine the chemical and physical states of distinct nanoparticles. Nanoparticles are modified by a surfactant of oleic acid for the evaluation of dispersion stabilities as compared to their bare forms. It is found to be economical and has shown superior characteristics in stability and morphology. The engine emission characteristics and performance are performed in a DI-CI engine using different concentrations of CeO2 nanoparticles. The emissions are mitigated with the addition of nanoparticles and 35 ppm is the optimum level of dosing of the nanoparticles in terms of engine performance, and emission behaviors. A blend of 20% biodiesel with diesel is found to be optimal for the preparation of nano-fuel. For the load test the efficiency increased up to 5% and at higher loads, the hydrocarbon (HC) emissions decreased by 45%, and Nitrogen Oxide (NOx) emissions were reduced by 30%. Response Surface Methodology (RSM) models correctly fit the experimental data, producing R2 values that ranged from 91 to 94.5%, respectively.