(280e) Gasification Fluidized Bed Reactor Modeling | AIChE

(280e) Gasification Fluidized Bed Reactor Modeling

Authors 

Ege, P. E. - Presenter, Reactech Process Development Inc.
Abbasi, A. - Presenter, University of Toronto
deLasa, H. I. - Presenter, University of Western Ontario
Kawaji, M. - Presenter, University of Toronto


Gasification reactions in fluidized beds depend strongly on the flow conditions. Significant mixing may reduce the reaction rates of equilibrium-limited reactions or change selectivity of reactions in series. A fluidized bed can be designed to enhance the desired product by changing the flow conditions within fluidized beds through vessel and distributor shapes and locations plus flow and particle properties. It is also common to introduce internals that form the flow as preferred.

This presentation compares two different methods to model a catalytic gasification reactor. The advantages and disadvantages of both are discussed. The first method uses time average flow patterns from a two-dimensional CFD simulation to estimate a steady-state combination of Plug Flow and CSTR regions with mass transfer and advanced kinetics. The second method models the dynamic system using 3D CFD with simplified kinetics. Time averaged effluent results provide pseudo-steady state results to compare with the first method.

The results show quite reasonable outcomes with the first simple method. The simplified flow model allows a more in-depth study of kinetics and a variety of process conditions. It also is helpful to highlight design or process changes to optimize the production. A CFD combined with reaction is much more time consuming so does not allow the same flexibility of conditions. However it is superior to the first approach in highlighting the dynamics and spatial effects. It also allows the direct visual illustration and evaluation of proposed design changes.

In conclusion the two methods are complementary. The first method allows the engineers to get a better understanding of a variety of process conditions. The second method can then be targeted and thus very useful to investigate large-scale systems as well as scale-up and design changes prior to implementing them in practice.

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