magnetic particle tracking in spouting and bubbling fluidized beds jack halow separation design...
TRANSCRIPT
Magnetic Particle Tracking in Spoutingand Bubbling Fluidized Beds
Jack Halow
Separation Design Group
Stuart Daw
Oak Ridge National Laboratory
Presented at the 2012 Fall National Meeting of the American Institute of Chemical Engineers
October 16-21, 2011
Pittsburgh, Pennsylvania
Objectives
Develop and demonstrate a unique experimental magnetic particle tracking system (MPTS) for studying solids mixing and dynamics in fluidized beds
Apply MPTS to develop statistically significant measures that characterize fluidized solids behavior
Develop correlations to develop fast running models of fluidized bed processes
Acquire data sets that can be used for validation of first principles and two phases and process models.
The magnetic particle tracking system
Tracer particles are constructed over a tiny neodymium magnet core
Magnet is Imbedded in a bead, foamed or coated
Tracer particles used are >1 mm diameter, 0.4-7 g/cc
•Single tracer particles are injected into bed
•Magnetic field signals recorded for analysis
•Special algorithms deconvolute signals to give 3D trajectory data
Four inch bed with current MPTS setup
Probes aligned North, South, East, West
Helmholtz coils modify earth’s magnetic field in bed
Non-metallic bed and supports
MTPS Current Capabilities
Beds up to 10 cm in diameter
Sampling rates up to 200 hertz
Runs times to 10 minutes
Sensitivity <0.5 milligauss equivalent to ~20 cm
Temperatures to 200-300 Centigrade possible
Applications to fluid beds, granular flow or fluid flow system
Tracer sizes > ~ 1 mm and densities > 0.4 g/cc
Trade off sometimes required between parameters
Information we get from MPTS 3D trajectory data
– Visualize tracer motion Position versus time graphs (i.e. Z or R vs time) Short time 3D vector plots: particular types of events 2D projections
– Visualize average location “Dot cloud” plots of data
– 3D clouds or 2D slices
– Quantify spatial information Frequency distributions Fit to statistical models for use in process model
– Quantify temporal information Autocorrelation analysis to yield characteristic times FFT for highly periodic processes
Experiments I’ll discuss
• Test conditions- 5.5 cm diameter bed- Porous plate distributor- 175 to 250 micron glass beads- 2.5 Umf (~15 cm/sec)- Slumped bed L/D =1- 0.76 g/cc 4 mm tracer- Sampling rate was 100 Hertz- Run time was 5 minutes- 5 replicate tests performed
• Data representations and analysis
Vertical position vs time – 30 sec
3D views of three tests “Dot clouds” give average temporal representations
But replicates not directly comparable
Top View
Side View
Statistical comparisons are better
Direct one to one comparisons not viable– Overall conditions same but detailed dynamics
depend on detailed local initial conditions which are never identical
Spatial statistical comparison– Compare frequency distributions - probability of
spatial location
Temporal statistical comparison– Compare autocorrelation curves at various time
lags - characteristic cycle times
Spatial comparison: frequency plots Divide vertical height into 40 bins
Place each measured z into a bin and count up each bin
Calculate normalized frequency for each bin
Length of test is important
• Compares full tests and segments of a test• Agreement deteriorates
with shorter times•Need adequate run times
to characterize bed
300 sec
25 sec50 sec
Temporal comparison Autocorrelation function
– Compares times series to itself as it’s shifted in time
– Periodicity shows up as peaks in the correlation coefficient
~0.8 sec cycle time
Radial position at various levels
• Dots in ring at higher bed levels• Concentrated in center lower in bed
Radial Frequency Distributions
• For each of the six levels:• Radial position sorted into 7 even radius bins• Points counted and normalized for each bin
Radial position autocorrelation
• Doesn’t show significant correlation• Radial motion essentially random• Bubble position radially random
Velocities show bubble events• 5 second segment• X & Y spikes indicate rapid lateral motion from bubbles•X & Y motion coupled with vertical motion
Total velocity frequency distribution
•Weibull distribution gives excellent fit (CC=0.98)
Summary Magnetic particle tracking can provide highly detailed
information about the motion of single particles (> 1mm) in fluidized beds.
Direct trajectory comparisons give qualitative information
Statistical comparisons are quantitative
Spatial frequency distributions give time averaged locations: Weibull distribution can represent vertical positions
Temporal analysis such as autocorrelation can reveal average circulation times and perhaps regime transitions
Model validations should use statistical data
Model calculations must run for minutes to be meaningful
Publications
E. Patterson, J. Halow, and S. Daw, “Innovative Method Using Magnetic Particle Tracking to Measure Solids Circulation in a Spouted Fluidized Bed,” Ind. Eng. Chem. Res. 2010, 49, 5037–5043.
J. Halow, K Holsopple, B. Crawshaw, S. Daw, “Observed Mixing Behavior of Single Particles in a Bubbling Fluidized Bed of Higher-Density Particles,” Ind. Eng. Chem. Res. on-line just accepted, October 10, 2012
Presentations J. Halow, E. Patterson, S. Daw, 2009 Annual AIChE Mtg. Nashville, TN
J. Halow, B Crawshaw, S Daw, C. Finney, 2011 Annual AIChE Mtg. Minneapolis, MN
E. Patterson, 237th ACS National Mtg, Salt Lake City, March, 2009.
Holsopple, 239th ACS National Mtg, San Francisco, March, 2010
Background Slides
MPTS – What is it? Tracer
– Small Neodynium magnet imbedded in particle– Tracer aligns with earth magnetic field orienting is magnetic field– Tracer density can be adjusted by choice of tracer material– Tracers diameters are 1 mm or larger– Tracer densities of from 0.4 to 7 g/cc
Sensors– Magneto-resistive type or Hall-effect– Externally mounted around bed– Orientation important for data analysis
Analysis– Special algorithms used to extract trajectory from field data– Data presentation by graphic and statistical techniques
Background and Motivation: Many processes utilize fluidized bed contactors and reactors
Turbulent multi-phase flow
High heat and mass transfer
Mixing of particles with gas, other particles key to performance
Different size and density of particles can lead to segregation
Dynamics of non-normal particles not well understood
Easy, safe inexpensive particle tracking system likely to have applications in many flow systems.
Vertical Position Probability Correlated with Weibull Distribution
kzk
ezk
zf
/
1
)(
for Z ≥ 0
k>0 is a shape factorλ>0 is a scale factor
Weibull Parameters Vary with Velocity and Tracer Density
•Weibul distribution represents data analytically•Useful for process modeling
Vertical position vs time – full test
Shows vertical motion
But Replicate tests not comparible
Radial position of tracer
•All points shown•Slight off center •Not much at walls
Radial Position Versus HeightSide view
Test Procedure
Bed and probes are leveled
Probes aligned probe NSEW
Bed material added and fluidized
Probe level adjusted to fluidized bed height
Probes zeroed
Data acquisition started.
Tracer dropped into bed
Temporal comparison - velocities• Tests with 0.76 g/cc traced• Velocity varied from just bubbling to turbulent• Characteristic circulation times evident
Autocorrelation of 1.2 g/cc tracer
Weibull Distribution with 0.76 g/cc Tracer
Weibull Distribution with 0.89 Tracer
Weibull Distribution with 1.20 Tracer