<p>This study presents the derivation of a kinetic model for the devolatilization of post-consumption food waste using thermogravimetric analysis (TGA). The derived model was implemented in an Eulerian-Eulerian Computational Fluid Dynamics (CFD) framework to simulate pyrolysis in a fluidized bed reactor operating at 550 °C. Due to the heterogeneity and complexity of food waste composition, the reaction function and devolatilization kinetics were identified using a staged decomposition approach. As determined by TGA, these stages correspond to the decomposition of protein, cellulose, and hemicellulose approximately between 270 and 300 °C, and the decomposition of lipids approximately between 350 and 400 °C. The activation energy, obtained using three different model-free iso-conversional methods, was consistent, with an average value of <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12155_2025_10835_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="22" /> </InlineMediaObject> <EquationSource Format="TEX">\({E}_{a}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>E</mi> <mi>a</mi> </msub> </math></EquationSource> </InlineEquation> = 219.23&#xa0;kJ/mol. The pyrolysis reaction was found to follow an order-based model, with the master plot method yielding an average reaction order of <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12155_2025_10835_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(n\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>n</mi> </math></EquationSource> </InlineEquation> = 10.3 and an Arrhenius frequency factor of <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12155_2025_10835_Article_IEq3.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(A\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>A</mi> </math></EquationSource> </InlineEquation> = 1.16 × 10<sup>19</sup> [(kmol/m<sup>3</sup>)<sup>1−<i>n</i></sup>/s]. The predicted distribution of pyrolysis products, validated against experimental data, highlights the robustness of the proposed analysis and its computational implementation. This methodology provides a strong foundation for further development and adaptation to simulate the pyrolysis of food waste and other diverse feedstocks, broadening its applicability to various types of biomass.</p>

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Food Waste Devolatilization Kinetics with Demonstration of Its Implementation in Computational Modeling of a Fluidized Bed Pyrolysis Reactor

  • Nihal Yasir,
  • Yassir Makkawi,
  • Baraa Ahmed,
  • Ondrej Masek

摘要

This study presents the derivation of a kinetic model for the devolatilization of post-consumption food waste using thermogravimetric analysis (TGA). The derived model was implemented in an Eulerian-Eulerian Computational Fluid Dynamics (CFD) framework to simulate pyrolysis in a fluidized bed reactor operating at 550 °C. Due to the heterogeneity and complexity of food waste composition, the reaction function and devolatilization kinetics were identified using a staged decomposition approach. As determined by TGA, these stages correspond to the decomposition of protein, cellulose, and hemicellulose approximately between 270 and 300 °C, and the decomposition of lipids approximately between 350 and 400 °C. The activation energy, obtained using three different model-free iso-conversional methods, was consistent, with an average value of \({E}_{a}\) E a = 219.23 kJ/mol. The pyrolysis reaction was found to follow an order-based model, with the master plot method yielding an average reaction order of \(n\) n = 10.3 and an Arrhenius frequency factor of \(A\) A = 1.16 × 1019 [(kmol/m3)1−n/s]. The predicted distribution of pyrolysis products, validated against experimental data, highlights the robustness of the proposed analysis and its computational implementation. This methodology provides a strong foundation for further development and adaptation to simulate the pyrolysis of food waste and other diverse feedstocks, broadening its applicability to various types of biomass.