Kinetic and thermodynamic study of faba bean hulls pyrolysis with machine learning-based bio-oil modeling

The growing global demand for sustainable, low-carbon energy has intensified research into biomass pyrolysis as a promising route for biofuel production. This study provides a comprehensive investigation of the kinetic and thermodynamic behavior of faba bean hulls, an underutilized agricultural byproduct, to evaluate their potential for bio-oil generation. Thermogravimetric analysis (TGA) was conducted at heating rates of 10, 15, and 20 °C/min to determine the kinetic triplet and thermodynamic parameters. A multi-model iso-conversional approach, including Friedman (FR), Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), Starink (STK), and Vyazovkin (VYZ) methods, was applied to enhance the reliability of kinetic evaluation. The activation energy ranged from 143.6 to 194.2 kJ/mol, reflecting the complex multi-step decomposition process, with the Šesták–Berggren model identified as the most suitable reaction mechanism. Thermodynamic analysis revealed average enthalpy (ΔH) values between 142.48 and 182.51 kJ/mol, Gibbs free energy (ΔG) from 127.18 to 128.70 kJ/mol, and entropy (ΔS) from 24.27 to 86.91 J/mol·K, providing detailed insight into the energy requirements, spontaneity, and disorder changes during pyrolysis. Additionally, a machine learning-based predictive framework using Random Forest was developed to model and optimize bio-oil yield. The Random Forest model achieved the highest predictive accuracy (R 2 = 0.92), identifying pyrolysis temperature and volatile matter content as the most influential factors. Optimal bio-oil yield was achieved at 500–550 °C. This study establishes an integrated thermo-kinetic and data-driven modeling framework, providing a promising basis for future validation throughout additional feedstocks and larger-scale pyrolysis systems.