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Modeling and estimation of moisture transport properties of drying of potential Amazon biomass for renewable energy: Application of the two-compartment approach and diffusive models with constant or moisture-dependent coefficient

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  • Claudio, Caio C.
  • Perazzini, MaisaT.B.
  • Perazzini, Hugo

Abstract

Mathematical modeling and estimation of moisture transport properties in drying of acai biomass were done in this work. The first approach consisted of applying the diffusive model for three different boundary conditions at the particle's surface and the effective diffusivity considered a constant or variable parameter. It was found that the model's accuracy depended on the drying level and the boundary conditions for the effective diffusivity assumed as a constant parameter. Accordingly, the results from the Arrhenius relationship depended on the predictions of the different approaches of the diffusive model, giving different values of the activation energy for the same experimental condition. The effective diffusivity ranged from 9 × 10−11 to 7 × 10−10 m2 s−1 and the activation energy from 38 to 55 kJ mol−1, indicating a high resistance to moisture evaporation. The predictions were not improved when considering the effective diffusivity as a moisture-dependent parameter, possibly due to the simplifications used for calculations. The two-compartment concept consisted of a set of differential equations that gave the best concordance with experimental data. The kinetics parameters ranged from 0.2 to 17 h−1 and presented a well-defined variation with the operating conditions, encouraging its use in drying optimization for efficient conversion processes.

Suggested Citation

  • Claudio, Caio C. & Perazzini, MaisaT.B. & Perazzini, Hugo, 2022. "Modeling and estimation of moisture transport properties of drying of potential Amazon biomass for renewable energy: Application of the two-compartment approach and diffusive models with constant or m," Renewable Energy, Elsevier, vol. 181(C), pages 304-316.
    • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:304-316
    DOI: 10.1016/j.renene.2021.09.054
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    References listed on IDEAS

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