(684f) Dynamic 2D Heterogeneous Model for Chemical-Looping Combustion in Fixed Bed Reactors

Dynamic 2D heterogeneous model for
chemical-looping combustion in fixed bed reactors

Lu Han,
Zhiquan Zhou, and George M. Bollas, Department of Chemical, Materials &
Biomolecular Engineering, University of Connecticut, Storrs, CT

Abstract:

The objective of this work is to present a dynamic,
heterogeneous two-dimensional model for fixed bed chemical-looping reactors.
This model can be applied to chemical-looping reforming processes and is suitable
for use with large catalyst pellets in industrial-scale applications as well as
small catalyst pellets in small-scale units. This model is applied to modeling
the reduction step in chemical-looping combustion to examine the dynamic
behavior and process sensitivity in the fixed bed reactors.

The reactions involved in the chemical-looping
reduction reaction step are:

The kinetics for all reactions used follows the results
by Zhou, et al. who developed a kinetics network capable of modeling various
published experimental data.

A mathematical model is developed with the
assumptions of an axially dispersed plug flow with negligible mass or heat
variations in the radial direction of the reactor, external and internal transport
effects to the catalyst particles, and reaction occurring inside the pores of
the oxygen carrier. The model equations used (shown below) consider the mass,
energy, and momentum balances of the gas along the reactor, and the mass and
energy balances inside the catalyst pellet. The gas phase momentum balance is
assumed to be in pseudo-steady state in which the Ergun equation is applied.

In this presentation, model predictions will be compared
against experimental data from various laboratories utilizing fixed bed
reactors for chemical-looping combustion and reforming. Process sensitivities
to pressure changes, catalyst characteristics, thermal gradients, and
diffusional limitations will be illustrated and discussed.

Acknowledgement:
This material is based upon work supported by the National Science Foundation
under Grant No. 1054718.