Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features

Abstract

Accurate determination of kinetic and thermodynamic parameters is vital for understanding biomass pyrolysis and optimizing renewable thermochemical conversion. In this study, sapodilla leaves were analyzed as representative lignocellulosic feedstock using both experimental and machine learning (ML) approaches. Thermogravimetric experiments at multiple heating rates, interpreted via the Coats–Redfern method, revealed strong dependence of activation energy (E_a) and pre-exponential factor (A) on reaction mechanism and temperature regime. Low-temperature devolatilization followed diffusion and reaction-order models, while high-temperature degradation exhibited nucleation-controlled behavior. Thermodynamic analysis indicated that sapodilla leaves' pyrolysis is endothermic and non-spontaneous (ΔG ≈ 104–107 kJ mol⁻¹) with negative entropy change (ΔS ≈ −0.23 kJ mol⁻¹ K⁻¹), which is consistent with increased ordering in the solid residue during pyrolysis. To complement mechanistic fitting, a ML framework was developed to predict kinetic parameters (E_a, A, C²) using a descriptor set that included proximate and ultimate analyses together with heating rate and reaction order. Ensemble learning models showed moderate predictive capability within this dataset, yielding a relatively narrow E_a range (42–45 kJ mol⁻¹) and identifying volatile matter, carbon content, and O/C and H/C ratios as influential compositional descriptors. The combined use of mechanistic analysis and interpretable ML provides a proof-of-concept comparison between stage-specific fitting and descriptor-based prediction, while also highlighting the present limitations in predictive robustness and generalizability.