(231e) Low-Order Modeling of the Vapor Phase Upgrading of Fast Pyrolysis Bio-Oil in a Bubbling Fluidized Bed Reactor

Sutton, J. E., Auburn University
Wiggins, G., Oak Ridge National Laboratory
Daw, C. S., Oak Ridge National Laboratory
Recently, the conversion of raw biomass into valorized fuels and chemicals has received a tremendous amount of attention, with a number of approaches being considered. One approach is fast pyrolysis of raw biomass followed by catalytic vapor phase upgrading of the raw bio-oil. A bench scale system for evaluating this strategy and guiding future scale-up efforts has been built at the National Renewable Energy Laboratory (NREL).[1, 2] Although initial results are promising, the complex coupling between the reactor hydrodynamics and the kinetics of pyrolysis and upgrading inhibits efforts to first identify optimal operating conditions and subsequently transfer this knowledge to larger-scale reactors. A predictive low-order model of these reactors would facilitate a search for more effective operating conditions and provide insights into some potential challenges in scaling-up the system.

We have developed a model of the catalytic vapor phase upgrader at NREL. This model employs a network of ideal stirred tank reactors to represent the different zones (bubbling bed, freeboard above the bed, solids inlet/outlet in the middle of the freeboard, and freeboard above the solids inlet) in the real reactor. Aside from a few adjustable parameters (the fraction of solids in the freeboard regions and the solids inlet/outlet region), all necessary parameters are specified with correlations drawn from the fluidized bed literature. We also incorporate an existing kinetic scheme for upgrading bio-oil on ZSM 5.[3, 4]

We employ uncertainty quantification to estimate the reliability of our predictions and global sensitivity analysis to determine the parameters with the most influence on reactor operation. Based on these results, we offer suggestions to improve reactor performance and highlight areas requiring more fundamental research such as the need for improved kinetic schemes and better fluidization correlations.

1. Iisa, K., et al., Energy & Fuels 30, 2144 (2016).
2. Yung, M.M., et al., Energy & Fuels 30, 9471 (2016).
3. Adjaye, J.D. and N.N. Bakhshi, Fuel Processing Technology 45, 161 (1995).
4. Adjaye, J.D. and N.N. Bakhshi, Fuel Processing Technology 45, 185 (1995).