Akademik Veri Yönetim Sistemi
SCIENTIFIC REPORTS, cilt.15, ss.5911, 2025 (SCI-Expanded)
The current work focuses on utilization of ANN (artificial neural network) for the predictionof performance and tailpipe emissions of Garcinia gummigutta methyl ester (GGME) enrichedwith H2 and TiO2 nano additives. For experimentation, H 2 gas was introduced to the mixescontaining TiO2 nanoparticles. Diesel, B10 blend (10% GGME biofuel + 90% Diesel), B20 (20%GGME biofuel + 80% Diesel), Diesel-TiO2 (Mineral Diesel with 100 ppm TiO 2 nano additives),B10-H2-TiO2 (B10 blend with 100 ppm nano additives + 5 L/min of H2) and B20-H2-TiO2 (B20 blendwith 100 ppm nanoparticles + 5 L/min of H2) were considered for experimentation. A constantmass flow rate of 10 L/min was used for the hydrogen flow throughout the test procedures. Testresults were carefully analyzed to determine the performance and emission measures. Differentspeeds between 1800 and 2800 rpm were used for each test. When combined with pure Dieseland mixtures of biodiesel, these nanoparticles and hydrogen enhanced the performance data. Forinstance, the brake-specific fuel consumption was reduced but the power, torque, and thermalefficiency were increased. Although there was a modest rise in NO emissions, the primary goalof lowering CO, CO2, and other UHC emissions was met. The ANN models confirm and agreedthe Diesel engine experimental work possesses minimal root mean square error (RMSE) andcorrelation coefficient values were estimated. This ideal model predicts and optimizes the engineoutput at a higher accuracy level, which gives better results compared with other empirical andtheoretical models.