What flow visualisation can teach us about reactor...
Transcript of What flow visualisation can teach us about reactor...
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What flow visualisation can
teach us about reactor design What? Flow visualisation can
teach us about reactor design?
Hugh Stitt [1] & Peter Jackson [2]
[1] [2]
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Outline
• In research
– Laboratory experiments,
– Model development
• Scale up
– Role of flow visualisation
– Measurement density
• Flow visualisation in the field
– Reactors behaving badly
– Knowledge vs. information vs. data
– Implementation
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Stirred Tank Tomography in 4D
at Medium Scale • 3 m3 demonstration scale mixing tank
with 8 planes of electrical sensors
R Mann et al., Chem Eng Sci, 52, 2087-2097 (1997)
– Sensor readings reconstructred
to give resistivity map
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Stirred Tank Tomography in 4D
• Video frame and tomogram showing tracer
distribution after 3 secs
R Mann et al., Chem Eng Sci, 52, 2087-2097 (1997)
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Stirred Tank Tomography in 4D
• Video frame and tomogram showing tracer
distribution after 3 secs
R Mann et al., Chem Eng Sci, 52, 2087-2097 (1997)
This is great – good picture!!
– But gives little quantitative
information on mixing
UNLESS
we have a model to
compare it with
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Getting High Quality Information on
Stirred Tanks • Needs a Lagrangian experimental approach
– Velocimetry – or particle Tracking
Positron Emission Particle
Tracking (PEPT)
Computer Automated
Radioactive Particle
Tracking (CARPT )
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Lagrangian Measurements on a Stirred Tank
• Loop circulation patterns
are severely averaged
• Actual fluid motion is far
more random
– Direction & velocity
Velocity Trajectory
Fishwick, Winterbottom & Stitt
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Lagrangian Measurements on a Stirred Tank
Fishwick, Winterbottom & Stitt
• CARPT on 8" dia vessel • PEPT on 4" vessel
Rammohan, Kemoun & Dudukovic
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Radioactive Velocimetry on a
Rushton Turbine Agitated Baffled Vessel
• Time-averaged velocity
plots
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Radioactive Velocimetry on a
Rushton Turbine Agitated Baffled Vessel
• Time-averaged velocity
plots
Strength of these spatial velocity data
– they can be compared directly to simulations
Great pictures!!
– But they give little quantitative
information on mixing
UNLESS
we have a model to
compare it with the data
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Stirred Tank Experimental vs Simulation
Velocity Vectors
• Both give recirculation loop centres at
– Upper loop : 0.575, 0.575
– Lower loop : 0.225, 0.225
Rammohan, Dudukovic & Ranade: IECRes 42, 2589 (2003)
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Stirred Tank Experimental vs Simulation
Turbulent Kinetic Energy
Rammohan, Dudukovic, Ranade: IECRes. 42, 2589 (2003)
• Model quality reduced for derived value
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• Optical techniques not appropriate
– Need penetrative methods; eg. g-rays
– Flow visualisation in highly dispersed multiphase operation
• Understanding of instantaneous effects
• Valuable data for comparison to time averaged models
CREL
Velocimetry in Multiphase
Bubble Column Operation
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Gas Sparging in a Stirred Tank Radioactive Techniques allow interrogation
at high hold up of dispersed phases
• Effect of gas sparging on liquid velocities
– PEPT data
• Gas hold up patterns
in a sparged stirred
tank
– g-CT data
No gas
Gas sparged Fishwick, Winterbottom & Stitt Rammohan & Dudukovic
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Tomography & Velocimetry in
Multiphase Flow Reactors
• Modelling of multiphase reactors is subject to many uncertainties
– Multiphase flow regime: bubbly, unstable
– Coalescence - redispersion
• Population balance: bubble class models
– Momentum transfer
– CFD “Closures”
• Require validation of models against detailed experimental data
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Tomography on a Bubble Column
• Electrical Resistance
Tomography
• Computer Tomography
(g-ray)
Williams, Wang et al, Leeds Univ, UK APCI / CREL data
Temporal resolution – but
uncertain spatial precision
Time averaged – good
spatial resolution
Both have been done on columns 18" diameter
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MRI – TBR Trickle-Pulse Flow Transition
Trickle regime 1.4 mm/s
Pulsing regime L = 13.3 mm/s
Transition regime
4.6 mm/s
Gas flow: 112.4 mm/s
Resolution: 0.7×1.4 mm
Acquired at 50 f.p.s.
All presented on the
same intensity scale
Lim, Sederman, Gladden, Stitt, Chem Eng Sci, in press
Flow transition
is a local
phenomenon.
Specific information on pulsing, its origin and
the bed structures that promote it
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Flow Visualisation in the Laboratory
• Range of techniques available for use with
multiphase systems
– g-ray, X-ray, Electrical, MRI
• Varying cost, spatial and temporal resolution
• Important role in building models and
fundamental understanding
– Specific information on flow regimes
– Model discrimination and validation
• Next question
– How do we exploit these techniques in
scale up and design ?
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“The bench scale results were so good
that we by-passed the pilot plant”
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Design and Scale up
Role of Flow Visualisation
• Experimental tomography and velocimetry
have a clear role in reactor design and
development
– Quantitative information for model validation
– Qualitative role in understanding flow
behaviour and phase interactions
– Quantitative evaluation of changes in mixing /
hydrodynamics behaviour with changes in
scale
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Low Cost Radial Flow Packed Bed
Proof of Concept
• High pressure processes • Ammonia synthesis
– Low DP at a premium
• Radial flow benefits
– High cost engineering retrofits available
– But a very cost sensitive industry
• Can radial flow be induced by directed packing?
Header Space
Feed Feed
distributor
Large dia.
inert packing
Smaller dia.
catalyst
Exit collector
(porous wall)
Exit flow
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Low Cost Radial Flow Packed Bed
Flow Modelling
• Radial flow patterns
predicted using CFD
• Process gas
conditions and flow
– Based on assumptions
of global packed bed
permeabilities
• But are these
predictions correct
and realistic ?
– Use Electrical Resistance Tomography
Bolton, Hooper, Mann & Stitt:, Chem Eng Sci, 59, 1989-1997 (2004)
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Low Cost Radial Flow Packed Bed
Experimental Validation with ERT • Electrical Resistance tomography
– 4D resolution
• Low spatial resolution
• Use 36" diameter vessel
– Packed aspect ratio 1:1
– Annular configuration,
• 2 particle diameters
• Central collector
– 8 planes of 32 electrodes
• Injection of concentrated brine tracer and monitor conductivity
– Reconstruct conductivity maps
Bolton, Hooper, Mann & Stitt, Chem Eng Sci, 54, 1989 (2004)
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Radial Flow Packed Bed ERT Flow Pattern
ERT provides demonstration of
overall axial / radial flow profile Bolton, Hooper, Mann & Stitt, Chem Eng Sci, 54, 1989 (2004)
• Reconstructed conductivity maps at single
horizontal plane for 8 different times
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Low Cost Radial Flow Packed Bed
Quantitative Validation • Velocity mapping from
ER tomography
• CFD simulation
of experiment
Bolton, Hooper, Mann & Stitt, Chem Eng Sci, 54, 1989 (2004)
• Qualitatively reproduces main features
– Quantitation is less conclusive
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What? Flow visualisation can teach us
about larger scale operation? • Scale up
– Use measurement system and measurement
density appropriate to validation of design
concept and models
• Does not need same precision as lab scale.
• Objective different
– Justification of scale up protocol
– Testing of models at increased scale
– NOT fundamental understanding and
derivation of models per se
• But what about manufacturing scale ?
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• Tracking of fluid movement
– within and between oil and gas
reservoir wells
• during drilling and production.
• Examination of transfer pipelines
to and from processing facilities
– for slugging effects, phase flow
rates, solids build-up or blockage,
pigging operation monitoring.
It’s only one dimensional and single
pass but ……….. it is an invaluable technique
Priority list : 1) Is there a blockage?
2) If yes, then where is it?
3) Then characterise the blockage
Tomography & Velocimetry in the Field Large Scale Particle Tracking : An old technology
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Reactors Behaving Badly
Stirred Tank Reactors
Liquid level below
top impeller Impeller damage makes
good mixing impossible
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• Pelleted catalysts
– Shallow bed (4")
– Large dia (8´)
• Reactor operating at
reduced conversion
• Observation (through
spy glass) indicates
“dark patches”
Reactor Behaving Badly
Catalytic Oxidation Reactor
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“Field” Particle Tracking Technology?
• What are the objectives ?
– Detailed diagnosis of flow patterns with high
spatial resolution ?
• But how high a spatial resolution is required?
• Customer requirement
– Measuring the degree of mixing with sufficient resolution to establish:
• overall quality of mixing and
• any severe maloperation
• at minimum cost
– Do mixing and flow patterns adversely affect production and profits?
– —————————————————————
—————————
• ——————————————————————
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Modality for “Field” Operation
• Key requirements for field and research
use are not the same
Research Priorities Field Priorities
Resolution Tomography Technique
Spatial Temporal
Transp- ortable
Sees through
Metal
g-ray Good None Yes Yes
e+ emission Good Moderate No Yes
X-ray Good Some Moderate Moderate
Electrical Moderate Excellent Yes No
Optical Good Good Yes No
MRI Good Good No No
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Modality for “Field” Operation
• Currently - only g-ray systems meet all the
requirements for field use
Research Priorities Field Priorities
Resolution Tomography Technique
Spatial Temporal
Transp- ortable
Sees through
Metal
g-ray Good None Yes Yes
e+ emission Good Moderate No Yes
X-ray Good Some Moderate Moderate
Electrical Moderate Excellent Yes No
Optical Good Good Yes No
MRI Good Good No No
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10
100
1000
80 90 100
Information Obtained (%)
Co
st
(Arb
itra
ry)
BUT :
Cost vs. Information
is exponential
The 80 : 20 Rule
• 80% of the information is only 20% of the cost
– And that 80% is normally sufficient to make an
educated decision or diagnosis
• Corollary : the remaining 20% of information
requires an additional 80% of the total effort
• Cost vs number of data points may be linear
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“Field” Tomography Technology?
• What information are we trying to obtain ?
– And at what level ?
• High levels of information cost money & time
• Diagnosis of good, adequate or poor operation can often be done with little measurement and information
– Provided you know what information or data to measure ……. & how to interpret it
• Detailed measurement will only be done in the field where it is essential
– Where it adds value
• Hence - if an operator can get enough information to understand what he critically needs to know by a 1D, 1m measurement
– Then he won’t pay for more!!!
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Reactors Behaving Badly
Steam Reformer
• Not too good
• Not good at all
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Reactors Behaving Badly
Steam Reformer
• Tube wall temperature surveys can be used
routinely to identify zones of misbehaviour
– Use Gold Cup Pyrometry
• Zone of hot tubes
– Operator needs
to trim burners to
avoid premature
tube failure
• And the resulting
cost penalty
But here we’re lucky. We have observation
windows to look through
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Dignostics and Tomography at Scale
A Case Study • Pilot plant slurry bubble column reactor,
– 18” diameter, heat exchange tube internals,
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Base line scan - Densitometry
1.0E+03
1.0E+04
1.0E+05
1.0E+06
0 10 20 30 40 50 60
Pin Number
Co
un
ts
Two successive sets of scans - Data are nearly
identical showing good reproducibility
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Field Measurements on a
Slurry Bubble Column Reactor
– 18” diameter, heat exchange tube internals
• High number of detectors / scans required to
achieve spatial resolution
– Very long time (thus high cost) to collect
statistically significant data set
• Internals effect “lines of sight”
• Very complex reconstruction
• Calibration during operation?
• Questionable value proposition
– Consider an alternative approach
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Gas Inlet
Slurry
outlet
Gas Outlet Detector 2
Detector 1
Tracer Study - Application Example 1
Slurry Bubble Column
• Open Tracer Studies
– For axial mixing and entrainment measurements
• Inject gas tracer at gas inlet.
– Responses from detectors 1 & 2 gives mean residence time,
• Axial mixing information
– Use third detector at
slurry outlet to measure
gas carryover
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Tracer Study - Application Example 2
Slurry Bubble Column
• Open tracer studies with ring detectors
– Investigate phase distribution and mixing
– Tracers
• Catalyst particles
– doped with Mn562O3
• “Liquid follower” :
– powdered Mn562O3
• Open gas tracer : Ar41
Gas Inlet
Slurry
outlet
Gas Outlet
– Use of more than one ring allows
measurement of rise velocities
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Particle Tracer Studies on a SBCR
• Install several rings of
collimated detectors
• Use pulse injection of
active particle tracers
– “Liquid”
– Catalyst
- Pilot plant operated by Air Products
- Tracking particles prepared by JM
- Data measurement by JM-Tracerco
- Data interpretation by CREL,
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Particle Tracer Studies on a SBCR • Catalyst and “liquid follower” particles show
almost identical behaviour
– Assumption of pseudo-homogeneous
slurry phase is valid
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Particle Tracer Studies on a SBCR
• Pulse injection of multiple particles and ring
detectors used in lieu of single Lagrangian trace
or tomography
– Simpler to install, calibrate and use
• Ring detector responses compared to model
predictions
– In general - good comparability
– Demonstrates model validity
OR....If we have a model that predicts behaviour
then we can assess any deviation from that
ideal using simpler (tracing) techniques
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• Pelleted catalysts
– Shallow bed (4“)
– Large dia (8´)
• Reactor operating at reduced conversion
• Observation through (spy glass) indicates “dark patches”
• Modelling
– Local extinction of catalyst and stable “cold channels” with steep thermal gradients
• With very high mass flow
Reactor Behaving Badly
Catalytic Oxidation Reactor
Hot (active) catalyst)
Dark
patches
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Catalytic Oxidation Reactor
• CFD modelling of gas
distribution system
and head space
indicated no problem
• If modelling is correct
(catalyst extinction and
cold flow channels) …..
– Would expect massive
mal-distribution of gas flow
• Significantly higher flow
though cold zones
Hot (active) catalyst)
Dark
patches
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Evaluation of Flow (mal)Distribution
Through a Packed Bed Reactor
• Flow distribution study using
– Open 85Kr tracer
– Ring of detectors just above catalyst bed
Detectors
were not
colliimated
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Reactor Flow Distribution using Tracer
• Typical test trace
Inlet
detector
response
Ring detector responses
– showing significant
differences
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Reactor Flow Distribution Using Tracer
• Flow distribution by Segment
High response
at locations of
persistent
dark patches
- Consistent
with model
Unexpected
area of low flow
• Repeat runs, and detectors at bottom of
catalyst bed all gave similar results
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Flow Visualisation in the field
• High measurement density not appropriate
– Financial considerations
• Information rich data, with few
measurements feasible based on
– Selecting appropriate measurements
• Not necessarily the same as in the lab
– Open tracers, chordal scans, ………
– A priori knowledge of what results represent
poor / bad behaviour
– Availability of models to interpret data and
relate to lab-based understanding
• Validation of model scalability
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But sometimes we need a “map”
Development of Tomography for Field Use
• A portable g-ray tomographic toolkit • For process diagnostic application on steel vessels
– Robust & portable. Accurate, repeatable & quick to analyse, Non-intrusive and non-invasive, Easy to install & remove. Economic
• Experimental & Methods
– Steel vessel, thin walled, 40 cm diameter
• Source : 137Cs : 662 keV
– Use of Phantoms
• Steel bar, tube and plate, Hollow polystyrene block
– Ab initio reconstructions
• From calculated line densities
Darwood et al., WCIPT3, Sept 2003
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Densitometry : Results for Dual Phantoms
Experimental
20 x 8 grid
Theoretical
40 x 4 grid
• Ghost images on both experimental and
theoretical reconstructions
– Grid scanning not able to discriminate multiple
features at low numbers of scans
Steel
Pipe
Steel
Plate
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Fan-beam Tomograms of Phantoms
Drilled polystyrene block 32 nodes x 6 scans
Pipe & plate dual phantom 32 nodes x 6 scans
• Tomograms show good representation
– Note absence of ghost images on
tomogram of dual phantom
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g-ray Computed Tomography Scanning
Imaging of Process Vessels & Reactors
“Fan beam”
arrangement
of sensors
Use multiple
source
positions
• 6.2 m dia. packed column
– 32 source locations
– 6 scans per position
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Tomography of Commercial Units
• Tomography can be done on commercial units with reduced number of scans
– Scale limited by g-ray attenuation
• Particle tracking also feasible but issues on tracer retrieval
6.2 m dia fractionation column 1m dia FCC Riser
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What? Flow Visualisation Can Teach us
about Reactor Design and Operation ? • Research
– Building fundamental understanding
• Model building, discrimination and validation
• Requires high density of measurements
• Scale up and Design
– Objective to test the model at the larger scale
• Lower measurement density probably adequate
• Manufacturing scale
– Objective is diagnostic
• Good operation or not: is it a financial burden?
• Even lower (single point?)
measurement may suffice
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What?
Flow visualisation can
teach us about reactor
design and operation