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Forecasting the thermal degradation depending on the kinetics of dracaena Draco lignocellulosic fibers using an artificial neural network
(Journal of Natural Fibers;Taylor &Francis, 2025-07-17) Hadou, Abdelwaheb; Belaadi, Ahmed; Ghernaout, Djamel; Mukalazi, Herbert
In order to forecast the thermal degradation of Dracaena draco plant fibers (DDFs) using thermogravimetric analysis (TGA) at heating rates ranging from 5 to 30°C/min, this study employed artificial neural networks (ANNs). Hemicellulose, cellulose, and lignin break-down were represented by the three different degradation stages that were seen. The enhanced ANN27 model successfully captured pyrolysis behavior and degradation patterns, achieving a high prediction accuracy (R2 = 0.99966). The model performed well at lower heating rates (5 and 10°C/min), but because of bias and heteroscedasticity, adjustments are required at higher rates (15–30°C/min). In contrast to the experimental averages of 131.244 kJ/mol, 109.269 kJ/mol, and 131.694 kJ/mol, respectively, kinetic analysis showed that the ANN27-predicted activation energies (Ea) were 133.420 kJ/mol (KAS), 53.692 kJ/mol (FWO), and 133.784 kJ/mol (STR). Without requiring a lot of testing, our ANN method provides insights into DDF thermal behavior and optimizes processing settings by properly forecasting degradation curves