Cashew nut shells (CNS) pyrolysis provides a sustainable pathway for producing valuable biochar, bio-oil, and gases while addressing CNS waste management. Optimizing the pyrolysis process requires an accurate knowledge of both composition, and degradation kinetics. This study introduces an integrated framework combining high-resolution thermogravimetric analysis (Hi-Res TGA) and a pseudo-component kinetic model (PKM) to simultaneously determine the composition and degradation kinetics of pyrolysis. Hi-Res TGA demonstrated superiority over conventional TGA (5 K min -1 ) as the optimal resolution reduced experimental time and provided derivative thermogravimetric ( ) curves with higher qualities. The developed PKM closely fits the curves, achieving a percentage error in area (PEIA) of 2.10% for the optimal Hi-Res TGA fitting. The model-derived CNS composition excellently aligned within a 5% margin of standard wet chemical analysis, significantly outperforming mathematical Gaussian deconvolution. Furthermore, the model yielded values distributed closely with reported values and only demonstrated 5% deviation. The model’s robustness was validated by the consistency of both composition and kinetic parameters across different heating profiles, establishing this integrated method as a rapid and reliable framework for CNS characterization. The method is readily transferable to other biomass types and can be optimized to reactor-level simulations. Extending the scheme with evolved gas analysis (e.g., Py-GC/MS) can enhances the model’s utility for industrial optimization. • Hi-Res TGA reduced experimental time by 69% with superior data quality • A PKM was developed using Hi-Res TGA data • The PKM can determine CNS’s composition and degradation kinetics simultaneously • The PKM-derived CNS composition matches analytical values within 5% • Activation energy values obtained via the PKM are close to reported values ( 5%)
Liu et al. (Thu,) studied this question.