(53h) Tracking of Simulated Biomass Particles in Bubbling Fluidized Beds

Tracking of Simulated Biomass Particles in Bubbling Fluidized Beds

 Stuart Daw* (Oak Ridge National Laboratory, Knoxville, TN 37932)

J.S. Halow (Separation Design Group, Waynesburg, PA 15370),

*dawcs@ornl.gov Introduction

Fluidized beds are extensively used for implementing fluid-solid reactions, and, in many cases, more than one type of solid particle is present in the reactor. Interest in the behavior of multi-particle bubbling bed reactors is specifically of interest in the context of biomass pyrolysis for liquid fuels production. Bench-scale bubbling beds are often used in laboratory experiments to study the effects of pyrolysis reactor operation and to characterize the conversion behavior of different biomass feedstocks. However, it is frequently very difficult to separate the effects of biomass particle mixing and segregation in the reactor from the intrinsic biomass particle kinetics.   In this presentation we summarize results from experimental measurements of simulated biomass particle trajectories in an ambient temperature bench-scale bubbling bed of sand for ranges of fluidization intensity, bed height, and particle properties that we expect to be relevant to laboratory bubbling bed pyrolysis reactors.  Our measurement approach is based on a previously published magnetic tracking technique that allows very rapid, real-time recording of 3D position and velocity for simulated biomass particles of different size and density as they mix and/or segregate in the laboratory bubbling bed. Based on our observations, it appears that we are able to estimate the parameters needed to replicate the observed particle trajectory statistics with a Langevin-type model. The observed trends in the estimated parameters with fluidization intensity and particle properties indicate potentially useful directions for constructing general correlations and models for biomass particle mixing and segregation in bubbling bed reactors.

Experimental Materials and Methods

In the present study we utilized a previously reported (1,2) particle tracking method for fluidized beds.  Simulated biomass particles of varying size and density were tagged with inexpensive neodymium magnets and multi-probe magnetic detectors were used to track and record the spatial locations of the tagged particles at 0.01s intervals as they migrated through the beds for periods of several minutes under different fluidization conditions. Based on these measurements, we are able to reconstruct the detailed long-time-scale trajectory of each different simulated biomass particle, including its position, velocity, and acceleration. From this information, we derived statistical distributions for each of these quantities as well as developed a preliminary dynamic model to match the observed behavior.

For the results reported here, the experimental bubbling fluidized bed was a 55 mm in diameter glass tube fluidized with air at ambient conditions. The bed solids consisted of 200 micron glass beads and the simulated biomass particles ranged in size from about 2 mm to 12 mm with densities ranging from 0.5 g/cc to 1.0 g/cc. The fluidization air flows investigated ranged from 1 to 6 times the minimum fluidization and the bed height varied from 1.0 to 2.0 times the bed diameter.  Improvements have been made in the sensitivity of the magnetic tracking system and the algorithm used to calculate position from magnetic readings from the previously reported studies.  These will be discussed in the presentation.

Results and Discussion

We compare the time-average axial segregation patterns for the tracked particles in these experiments with our previously reported observations of Weibull-type axial distributions for a similar laboratory bed. We also summarize the spatially-averaged and spatially localized particle velocity distributions as functions of particle properties and bed operating conditions.  Preliminary results appear to indicate that a Langevin-type model may be able to replicate the observed tracked particle velocity distributions with appropriate values for the key parameters and distribution of the stochastic perturbations. If the accuracy of the Langevin-type model can be confirmed, it should provide a useful benchmark for validating and interpreting the results from computational fluid dynamics simulations. Likewise, we conjecture that a Langevin approximation of biomass particle mixing  may provide a rapid method to account for particle mixing effects in lower-order pyrolysis reactor simulations, provided that the variation in particle properties with conversion are adequately accounted for.  This could provide a low-computational-overhead approach for evaluating general trends in pyrolysis reactor performance with variations operating conditions and feedstock properties.

1. Patterson et.al., An Innovative Method Using Magnetic Particle Tracking to measure Solids Circulation in a Fluidized Bed, Ind. Eng. Chem. Res. 2010, 49, 5037-5043.

2. Halow et. Al. Observed Mixing Behavior of Single Particles in a Bubbling Fluidized bed,  Ind. Eng. Chem. Res. 2012, 51, 14566-14576.

Abstract submitted for presentation at the 2013 Annual A.I.Ch.E. meeting in San Francisco,CA