Experimental Investigation of Biodiesel Fuels Obtained by Enriching the Content of Vegetable and Waste Oils with Nanoparticles and Modeling of Data Obtained from the Produced Fuel Samples Using Artificial Intelligence - Demirpolat, Ahmet Beyzade; Uyar, Muhammed Mustafa; Citlak, Aydin (Mdpi, 2025)
The objective of this study is to investigate the effects of Mn2O3 nanoparticle additives on the performance and emission characteristics of biodiesel fuels produced from vegetable- and waste-based oils. Biodiesel fuels were synthesized via the transesterification process, after which Mn2O3 nanoparticles were blended in different concentrations (50, 75, and 100 ppm). The prepared fuels were tested in a single-cylinder diesel engine operating under constant speed and variable load conditions. Engine performance parameters such as specific fuel consumption (SFC) and thermal efficiency, along with emission indicators including CO, HC, NOx, smoke opacity, and exhaust gas temperature, were systematically analyzed. Additionally, the experimental findings were modeled and validated using the machine learning-based linear regression method. The addition of Mn2O3 nanoparticles significantly improved combustion and emission performance. Among all samples, the COB10+ 100 ppm Mn2O3 fuel exhibited the best overall performance, achieving a 37.50% reduction in CO, 38.8% reduction in HC, and 33.84% reduction in smoke (soot) emissions compared to conventional diesel. This fuel also demonstrated an increase in thermal efficiency comparable to that of diesel. The improvement in thermal efficiency was attributed to enhanced the in-cylinder temperature, reduced ignition delay, and shorter combustion duration. Furthermore, the use of waste-derived vegetable oils contributed to lower production costs and a reduction in environmental impact. The linear regression model yielded an optimum prediction accuracy with a mean squared error of 5.86 x 10(-6) for CO emission data. These findings indicate that Mn2O3 nanoparticles can effectively enhance the performance and sustainability of biodiesel fuels while maintaining economic and ecological advantages.
Keywords: Mn2O3 nanoparticle; diesel fuel; biodiesel; artificial intelligence modeling