(30b) Combining High and Low-Order Computational Models to Simulate Biomass Fast Pyrolysis Reactors | AIChE

(30b) Combining High and Low-Order Computational Models to Simulate Biomass Fast Pyrolysis Reactors


Wiggins, G. - Presenter, Oak Ridge National Laboratory
Ramirez, E., Oak Ridge National Laboratory
Sutton, J. E., Auburn University
Finney, C. E. A., Oak Ridge National Laboratory
Daw, C. S., Oak Ridge National Laboratory
Fast pyrolysis is a leading candidate technology for the thermochemical conversion of solid biomass into liquid bio-oils which can be used as feedstocks for producing biofuels and high-value chemicals. Bio-oils are commonly generated in fluidized, circulating, or entrained flow reactors in which biomass particles rapidly devolatilize in the absence of oxygen into mixtures of light gases, condensable bio-oil vapors, and char. To maximize bio-oil yields, the reactors typically operate at temperatures near 500°C and must maintain particle residence times in the range of 2-5 seconds and gas residence times less than 1 second. Deviations from these conditions can result in significant production and quality penalties, therefore optimal reactor design and control become crucial to achieving commercially viable bio-oil production.

In this presentation, we describe preliminary results produced with a hybrid modeling approach for simulating laboratory-scale biomass pyrolysis reactors. By exploiting the fact that the biomass solids loadings and heats of reaction are both relatively low, we can separate the complex gas-particle hydrodynamics from particle-scale and homogeneous gas-phase chemical reaction kinetics and thereby greatly accelerate the simulation process. Our approach is based on initially modeling the detailed particle-gas hydrodynamics in MFiX using the discrete element method (DEM) and the two-fluid model (TFM) to determine the residence time distributions (RTDs) of the different size biomass particles in the reaction zone. We then use the resulting particle RTDs in low-order reactor models written in Python to account for particle-scale and gas-phase reaction kinetics to estimate the trends in net bio-oil pyrolysis yield for a range of reactor conditions.