Abstract
Abstract
This study investigates the effects of magnesium carbonate (MgCO3) nanoparticles in addition to cottonseed biodiesel/diesel blends on diesel engine performance and emission characteristics. Experiments were conducted under various engine loads, and Response Surface Methodology (RSM) was employed for modeling and multi-objective optimization of operating parameters. Results showed that biodiesel blends increased brake-specific fuel consumption (BSFC) by up to 16.11% and reduced brake thermal efficiency (BTE) by up to 13.53% compared to diesel fuel, mainly due to lower calorific value and higher viscosity. However, the addition of MgCO3 nanoparticles improved combustion performance, reducing BSFC by up to 5.25% and increasing BTE by up to 5.87% under optimal conditions. Emission analysis revealed that nitrogen oxide (NOx) emissions increased by up to 49.06%, while hydrocarbon (HC) and carbon monoxide (CO) emissions decreased by up to 42.44% and 51.93%, respectively, indicating enhanced combustion efficiency. Carbon dioxide (CO2) emissions increased by up to 17.67% due to improved oxidation reactions. RSM analysis confirmed the statistical significance of the developed models with high coefficients of determination (R2 = 0.9178–0.9921). The optimal operating condition was determined to be 52.30 ppm MgCO3 and 1.51 kW engine load. Validation experiments showed good agreement between predicted and experimental results, with errors ranging from 0.71% to 8.83%, all within acceptable limits. Overall, the study demonstrates that MgCO3 nanoparticles can partially mitigate the performance drawbacks of biodiesel while improving combustion quality, and RSM is an effective tool for optimizing engine operating conditions.
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@article{Sava2026Catalytic,
title = {Catalytic Combustion Enhancement of Cottonseed Biodiesel-Based Nanofuel Containing MgCO3 Nanoparticles in a Diesel Engine: Experimental Investigation and RSM Optimization},
author = {Arif Savaş and Samet Uslu and Oğuzhan Der and Ramazan Şener},
journal = {Fluids},
year = {2026},
doi = {10.3390/fluids11080193},
url = {https://doi.org/10.3390/fluids11080193}
}
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