Determination of composition and degradation kinetics of cashew nut shells pyrolysis using high-resolution TGA and pseudo-component kinetic modelling

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%)

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Publication Details

Journal
Biomass Futures
Published
2026-02-05
DOI
https://doi.org/10.1016/j.bmf.2026.100026
Primary Topic
Thermochemical Biomass Conversion Processes
Type
article
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article

Determination of composition and degradation kinetics of cashew nut shells pyrolysis using high-resolution TGA and pseudo-component kinetic modelling

M. van Sint Annaland, Yang Liu, Nathan Barros de Souza, Esther Tiemersma et al.
Biomass Futures
Thermochemical Biomass Conversion Processes
article

Determination of composition and degradation kinetics of cashew nut shells pyrolysis using high-resolution TGA and pseudo-component kinetic modelling

M. van Sint Annaland, Yang Liu, Nathan Barros de Souza, Esther Tiemersma, Qian Zhou
article en

Abstract

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%)

Biomass FuturesVol. 1
Avans University of Applied Sciences (NL), Eindhoven University of Technology (NL)
Responsible consumption and production
Openalex Percentile: Top 65%
Thermochemical Biomass Conversion Processes
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