article · Industrial Crops and Products
This research evaluates the thermal breakdown and pyrolysis behaviour of Agave americana flower stalk biowaste fibers under several heating rates ranging from 5 to 30 degrees Celsius per minute. Thermogravimetric and derivative thermogravimetric analyses revealed that increasing the heating rate shifts the two primary devolatilization peaks from 320 to 350 degrees Celsius and from 410 to 445 degrees Celsius, respectively, driven by decreased heat transfer efficiency. To improve upon traditional kinetic models that struggle with nonlinear thermal factors, an artificial neural network with a 5 by 17 by 1 architecture was trained on the data. The neural network accurately mapped the process with a correlation coefficient above 0.98. While it slightly overestimated activation energies at an average of 135 kilojoules per mole compared to 125 to 130 kilojoules per mole from standard kinetic methods, it successfully predicted thermodynamic properties such as enthalpy, Gibbs free energy, and entropy change.
Understanding how agricultural biowaste breaks down under heat is vital for turning plant residues into energy or useful materials. Conventional mathematical models often fail to capture complex thermal interactions. By demonstrating that machine learning can accurately model thermal breakdown, this work provides a reliable computational method to predict how agricultural residues behave during high-temperature processing.
This work is early-stage research focused on laboratory thermal characterisation and computational modeling. It could enable engineers and operators designing biomass pyrolysis reactors to better forecast processing parameters and energy requirements for Agave americana waste. However, the abstract does not indicate that the method has been applied in operational reactors or tested beyond laboratory-scale data.
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Traditional kinetic models are commonly employed to analyze the pyrolysis behavior of biomass fibers. However, their accuracy is often limited because they struggle to capture the complex, nonlinear interactions among thermal factors. This study uses thermogravimetric analysis and derivative thermogravimetric analysis to examine the pyrolysis properties and thermal degradation behavior of flower stalk fibers from Agave americana waste under different heating rates ( β = 5, 10, 15, 20, 25, and 30 °C/min). The results show a clear shift of both devolatilization zones to higher temperatures as β increases, with the first peak moving from 320°C at 5 °C/min to 350°C at 30 °C/min, and the second peak shifting from 410°C to 445°C over the same range. This shift is attributed to a decrease in heat transfer efficiency at higher β , which affects the thermal degradation kinetics. Artificial neural network (ANN) models, especially the architecture (5 × 17 × 1), were developed to model the pyrolysis process. The ANN model achieved a mean bias error of less than 2 %, a mean absolute error of less than 0.03, and a correlation coefficient of over 0.98, demonstrating strong agreement with experimental data. Comparisons with kinetic parameters obtained from Flynn-Wall-Ozawa, Kissinger-Akahira-Sunose, and Starink methods indicated that the ANN slightly overestimated activation energies, with average predicted values of 135 kJ/mol compared to 125–130 kJ/mol from kinetic methods. The ANN also effectively predicted thermodynamic parameters, with enthalpy change values between 120–140 kJ/mol, Gibbs free energy around 110 kJ/mol, and entropy change values that varied slightly with minor deviations from experimental results.
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DOI: 10.1016/j.indcrop.2025.122001
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