CFD-based Modeling of Inflight Mercury Capture
Transcript of CFD-based Modeling of Inflight Mercury Capture
CFD-based Modeling of Inflight Mercury Capture
Hg
HgCl2
Jens Madsen Thomas O’BrienAnsys / Fluent Inc. NETL / U.S. Dept. of Energy
Morgantown, West Virginia Morgantown, West Virginia
10th Electric Utilities Environmental Conference Tucson, January 22-24. 2007
EUEC-10, Tucson, January 2007
Background and Motivation
• 1,100+ coal-fired units in the US− 40% of man-made mercury emissions− Annual sorbent cost for typical 300 MW
plant estimated to $1-2 million − DOE’s programmatic goal is to reduce
cost by 25-50% over baseline estimates• Project Goal
− “Use CFD-based tools to simulate and improve the understanding of sorbent-based mercury control processes”
− Flow modeling support for DOE/NETL field test sites over the past three years
• CFD benefits− Enables parametric study and
optimization of capture processes − This may substantially reduce the cost of
CAMR compliance
Monroe (Detroit-Edison) Brayton Point (PG&E Natl.Energy)
Meramec (Ameren-UE) Yates (Southern Co.)
Presque Isle (Wepco)
EUEC-10, Tucson, January 2007
Background and Motivation
• Practical questions answered−Optimize injection grids−Predict necessary sorbent feed rates− Inexpensive what-if studies
• Detailed information provided −Flue gas conditions
• Velocity• Temperature• Mercury (elemental/oxidized)
concentrations [µg/m3] • ….
−Sorbent conditions• Dispersion and residence time• Where the capture takes place
Data for Model Calibration & Validation
Support of DOE/NETL field test activities
EUEC-10, Tucson, January 2007
Background and Motivation
• Few existing models of mercury capture− Typical simplifications include:
• Plug gas flow (1D models) • Uniform sorbent dispersion• No velocity slip between particles and flue gas
• CFD-based model without such simplifications− Based on first principles (conservation laws)− Considers adsorption of Hg(o) and HgCl2− Known partitioning between these species (oxidation fraction) used as model input
Hg
HgCl2
EUEC-10, Tucson, January 2007
Presentation Outline
• Consider distinct mass transfer processes− Occur on multiple scales − Any single process may limit the overall mercury capture
1. Injection and dispersion of sorbent• Discrete Particle Modeling (DPM)• Injection Lance design (single- or multi-nozzle lances?)• Injection Grid design (impact on sorbent dispersion and Hg
capture) 2. Duct-scale transport of gaseous mercury species
• Transport by convection and turbulent diffusion• Mercury sink term determined from DPM simulations
3. Film mass transport• Transfer from bulk gas phase to sorbent exterior
4. Pore diffusion through sorbent’s interior• Molecular (laminar) or Knudsen diffusion transport
5. Surface adsorption on internal sites• Adsorption rates calculated using Langmuir theory
DUCT SCALE
SORBENT SCALE
EUEC-10, Tucson, January 2007
Discrete Phase Model (DPM)
• Trajectories of particles are computed in a Lagrangian frame− Each trajectory represents a group of particles with same initial properties− Particle-Particle interaction is neglected (dilute solid-gas flows)− Particle force balance determines trajectory (Newton’s 2nd Law)− Effect of turbulence modeled by stochastic tracking
•Particles behave differently based on size
•Particle size distribution (PSD) represented by discrete bins
•Typically ten size bins (dp= 1 … 100µm)•Trajectory flow rates weighted by PSD
PSD for DARCO-Hg
Considered size range
EUEC-10, Tucson, January 2007
Injection Lance Design
0.30
0.39
0.19
0.080.03 0.01
0.61
0.84
0.94 0.96 0.97 0.98
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0.2
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1 20 40 60 80 100Diameter [micron]
Particle Size Fraction Lower Nozzle Fraction
• Determine sorbent split for multi-nozzle injection lances • Multi-nozzle lances offer a false sense of security
− Sorbent split can be very uneven (here 81% exits lower set of nozzles)− Sorbent coverage very similar to that of a much simpler single-nozzle lance− Staggered lance arrangements is a preferable approach to achieve good coverage
from top-to-bottom of duct− Note: Smaller size fraction does most the capture
Four-nozzle lanceUsed at DE-Monroe
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Sorbent Dispersion – DTE Energy’s Monroe Plant
• Monroe plant has a very wide rectangular duct (51.5ft)
• Major stratification problems (temperature/sorbent/capture)
• Five multi-nozzle injection lances provide only partial coverage
• Stratification causes packages of gas to pass untreated by ACI
• Overall CFD predictions agree with outlet mercury sampling and analysis of hopper ash mercury content
InletAC Injection
Outlets (~50% flow each)
Ladder vanes
Splitter plate
Perforated plate
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Navigation Slide
1. Injection and dispersion of sorbent• Discrete Particle Modeling (DPM)• Injection Lance design (single- or multi-nozzle lances?)• Injection Grid design (impact on sorbent dispersion and
Hg capture)
2. Duct-scale transport of gaseous mercury species • Transport by convection and turbulent diffusion• Mercury sink term determined from DPM simulations
3. Film mass transport• Transfer from bulk gas phase to sorbent exterior
4. Pore diffusion through sorbent’s interior• Molecular (laminar) or Knudsen diffusion transport
5. Surface adsorption on internal sites• Adsorption rates calculated using Langmuir theory
Compute sorbent trajectories using DPM
Solve scalar equation(s) for Hg transport
Compute Hg adsorption rates
Convergence?No
Solve gas phase momentum equations
START
Yes
STOP
EUEC-10, Tucson, January 2007
Duct-Scale Mercury Transport
• Mercury transport equations• Determines distribution of gas-phase
mercury in duct
• Turbulent diffusion dominates
• µt >> µmol
• Similar diffusivity for Hg(o) and HgCl2
Hgi
Hg
t
tmolgi
i
SxSc
µµcρux
=
∂
∂
+−
∂∂ c
sink term
Inlet Hg level
Carrier Gas(no Hg)
Hg level tapers off
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Navigation Slide
1. Injection and dispersion of sorbent• Discrete Particle Modeling (DPM)• Injection Lance design (single- or multi-nozzle lances?)• Injection Grid design (impact on sorbent dispersion and
Hg capture) 2. Duct-scale transport of gaseous mercury species
• Transport by convection and turbulent diffusion• Mercury sink term determined from DPM simulations
3. Film mass transport• Transfer from bulk gas phase to sorbent exterior
4. Pore diffusion through sorbent’s interior• Molecular (laminar) or Knudsen diffusion transport
5. Surface adsorption on internal sites• Adsorption rates calculated using Langmuir theory
Compute sorbent trajectories using DPM
Solve scalar equation(s) for Hg transport
Compute Hg adsorption rates
Convergence?No
Solve gas phase momentum equations
START
Yes
STOP
EUEC-10, Tucson, January 2007
Capture Modeling – Film Mass Transfer
c∞
Dp
position
concentration
co
δ
r
• Film layer effect: drop in Hg concentration from bulk phase to sorbent surface − Concentration drop across film determined from flux conservation
• Mass Transfer Coefficient kf from empirical relation for Sherwood number
( )off cckJ −⋅= ∞Film mass flux:
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Capture Modeling – Porous Diffusion
Molecular Diffusion – intermolecular collisions • Binary system: air+mercury (Hgo or HgCl2)• Chapman-Enskog (molecular) theory
Ω
+⋅= 2
12 p2/11/13/2T3-10 1.86 molD
σ
MWMW
Knudsen Diffusion – molecule/walls collisions
• Does not depend on gas composition nor pressure
MWT
2pored
9700 KnD =
• Two modes of porous diffusion• Less diffusive mode limiting (Molecular or Knudsen Diffusion) Effective diffusivity Deff
• Added resistance from tortuosity of the porous media • In both diffusive modes: DHgCl2< DHg(o) (by about 35…40%)
1-
KnD1
molD1
p
p effD
+=
τ
ε
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Capture Modeling – Porous Interior
• Closed-form solution for concentration profile within perfect sphere
• Introduction of sorbent effectiveness η
• Allows use of surface values co throughout volume when calculating adsorption rates
Concentration Profile in spherical particle First order reactions
0
0.2
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1
1.00 0.75 0.50 0.25 0.00 r/rp
cp/co
Thiele - Low Thiele - Medium Thiele - High
η=0.939
η=0.48
η=0.115
Reaction limited
Diffusion limitedc∞
Dp
position
concentration
co
δ
drr
Thiele Modulus:
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Capture Modeling – Surface Adsorption
• Mercury adsorption rates computed using Langmuir isotherms• Separate isotherm parameters for each mercury species• Capture by UBC may be accounted for by separate particle stream with own rates
• Langmuir: net adsorption rate = forward rate (k1) minus desorption rate (k2)
• Here θ is the sorbent utilization (ω / ωmax ), ie. fraction of occupied sites• ωmax is the maximum number of available sites (sorbent capacity)
• Isotherm parameters (ωmax, k1, and b = k1/k2) are temperature-dependent• Getting proper isotherm data for a sorbent is challenging• When determined from packed bed breakthrough curves, adsorption process is essentially
lumped with film transfer and pore diffusion
[ ] θθ max2omax1 ωω kc1k −−=ℜ
EUEC-10, Tucson, January 2007
Capture Modeling – Summary
Sorbent trajectories
HgCl2 sink terms
HgCl2 concentration (no ACI)
HgCl2 concentration (with ACI)
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Capture Modeling Results – Brayton Point
• Comparison: model predictions vs. field test measurements− Quantitatively, capture is under-predicted− Qualitatively, trends are well represented (as illustrated in normalized plot)
Normalized Capture vs. Injection Rate
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lb/Macf
Nor
mal
ized
cap
ture
Field Test CFD
Capture Efficiency
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Field Test CFD
EUEC-10, Tucson, January 2007
Parametric Study – Size Effects
• Capture efficiency for different uniform particle sizes− Maintain constant injection rate− No appreciable capture for size
fractions dp>10µm
• Conclusion: Size does matter! − Focus on smallest size fraction− Darco-Hg: ~1/3 smaller than 10µm − Modeling-wise, this means sensitivity
towards discrete representation of size distribution (number of bins)
Normalized Mercury Capture
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Particle Size [micron]
Normalized Capture
EUEC-10, Tucson, January 2007
Concluding Remarks
• CFD-based mercury capture model − Enables cost-effective optimization of injection systems
− Directly addresses major cost component of ACI control technology
− Detailed modeling framework exist
− More work pending on determining proper set of adsorption rates
EUEC-10, Tucson, January 2007
Extra slides
EUEC-10, Tucson, January 2007
Validation of Discrete Particle Model
• Can these predictions of sorbent dispersion be trusted?− Dispersion data not available for real power plants− Circumstantial evidence exist in the form of dispersion results that match capture
stratification patterns at Monroe field test site− A more thorough model validation required
• Model validation based on well-documented experiments *− Dispersion of particle jet in isotropic turbulence− Turbulence is generated in experiment using a screen− CFD model closely match experimental conditions (such as inlet turbulence parameters
y
* W.H. Snyder and J.L. Lumley : “Some measurements of particle velocity autocorrelation functionsin a turbulent flow”, Journal of Fluid Mechanics, 1971, vol. 48 (No.1), pp 41-71.
EUEC-10, Tucson, January 2007
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Time (msec)
Dis
pers
ion
(cm
2)Stochastic ModelExperimentalCloud Model
Validation of Discrete Particle Model (2)
NavgYY
Dispersion2)( −
=
• Stochastic Tracking under-predicts the particle dispersion by 5 … 30%