(487ai) Modeling of a Multiple Tube Aerosol Flow Reactor for High Temperature Solar-Thermal Processes | AIChE

(487ai) Modeling of a Multiple Tube Aerosol Flow Reactor for High Temperature Solar-Thermal Processes

Authors 

Martinek, J. - Presenter, University of Colorado
Weimer, A. W. - Presenter, University of Colorado at Boulder


Various solar thermal processes, including metal oxide water splitting cycles and high temperature gasification of cellulosic biomass to syngas, have been proposed as renewable routes to hydrogen or liquid fuels. These processes use concentrated solar energy to reach high temperatures and drive strongly endothermic chemical reactions. A multiple-tube solar aerosol flow reactor consisting of a reflective outer cavity with a windowed aperture enclosing five reaction tubes has been designed and a three dimensional computational fluid dynamics model of the reactor has been developed. The solid particles are modeled using a discrete phase Eulerian-Lagrangian approach and radiation heat transfer is included in the models via a discrete ordinates method. A gray band model is employed to account for spectral behavior of the optical properties and to separate the shorter-wavelength solar radiation from the longer-wavelength emission from the heated tubes. Absorption coefficients, scattering coefficients, and the scattering phase function for a cloud of particles are calculated from Mie theory. Simulations of inert alumina particles entrained in nitrogen gas with a total solar input of 9kW, an average solar concentration of 1860kW/m2, and an imposed external cavity wall temperature of 700K indicate that temperatures of up to 2000K can be reached within the flow tubes. Model validation experiments have been carried out on-sun at the High Flux Solar Furnace (HFSF) at the National Renewable Energy Laboratory (NREL). Both inert gases and particles were fed, and measurements of the temperature profiles and energy losses in the multi-tube reactor were obtained. These experimental results will be compared with the predictions of the computational model.