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A computationally efficient particle submodel for CFD-simulations of fixed-bed conversion

Henrik Ström (Institutionen för energi och miljö, Energiteknik ; Institutionen för tillämpad mekanik, Strömningslära) ; Henrik Thunman (Institutionen för energi och miljö, Energiteknik)
Applied Energy (0306-2619). Vol. 112 (2013), SI, p. 808-817.
[Artikel, refereegranskad vetenskaplig]

Fixed-bed conversion is one of the standard methods for conversion of biofuels. However, in several cases the performance observed in applications of fixed-bed conversion of biomass and waste is far from optimal. Mathematical modeling using computational fluid dynamics (CFD) has a large potential to assist in the optimization of the fuel conversion processes, with regard to parameters such as burnout, emissions, fuel flexibility and material wear. To this end, computationally efficient models that can handle the most important features of the fuel conversion processes are needed.

In the present work, a model is derived for the first conversion stages of a woody biofuel: drying and devolatilization in an inert atmosphere. The model predictions are compared to experimental data and to the predictions of similar models of higher and lower degrees of computational complexity. It is shown that the proposed model is able to predict the correct drying and devolatilization behavior by using a small number of variables and a relatively coarse resolution of the particle interior. It is also shown that a simpler model cannot accurately describe the conversion processes as observed in experiments. Moreover, by using a shrinking-core model to describe the char combustion, the particle mass loss can be predicted correctly also during this phase. Finally, it is outlined how the current model can be extended to include effects on spatial scales significant to that of the fuel bed, such as bed collapses and channeling.

Nyckelord: Biomass; Fixed bed; Mathematical modeling; Solid fuels; Grate furnaces



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Denna post skapades 2012-12-27. Senast ändrad 2015-12-17.
CPL Pubid: 168696

 

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