Upload
nyah-wingett
View
225
Download
6
Tags:
Embed Size (px)
Citation preview
Post MixingOptimization and Solutions
Process Intensifier:Optimization Using CFD
Part 1
Pete Csiszar, Black & Baird Ltd., North Vancouver, B.C.Keith Johnson, Independent Consultant, North Canton, Oh
Post Mixing Optimization and Solutions, Pittsford, NY
’03 AIChE Annual Meeting
Nov 16-21, San Francisco
Paper 362c
Post MixingOptimization and Solutions
Introduction
Process Intensification High P/V, high shear, small volume, small residence time Applications
High Speed Dispersion of Bentonite Ex-situ Bioremediation of Organics Rapid Mixing of Water Treatment Polymers Preparation of Coatings Beverage Industry Flotation Chemical Extraction Series-parallel Reactions Oxidation Processes Emulsification Applications Dry Material Wetting Chemical Neutralization Mixing of High Viscosity Shear Thinning Fluids
High P/V, high shear, small volume, small residence time
Post MixingOptimization and Solutions
Introduction
Internet SearchLightnin Line-Blender
Radial and Axial impeller designsHayward Gordon In-line Mixer
Radial and Axial impeller designsNo systematic study reported on themUse CFD to understand and optimize these
pipe mixers
Post MixingOptimization and Solutions
Experimental Design
CFD confirmation using standard mixing configurations, T=12.5” (317.5 mm)
RP4 radial impeller PBT axial impeller 5” RP4 D/T=0.4 5” 3PBT30 D/T=0.4
Post MixingOptimization and Solutions
Experimental Design
Studied 4 Dynamic Pipe Mixers Did not consult with the vendors. Data is taken
directly from their respective web sites
LTR HGR LTA HGA 2x 5” RP4 2x 5” RP4 2x 3.5” 3PBT30 2x 5” 3PBT30
Post MixingOptimization and Solutions
Experimental Design
All units were studied in a nominal schedule 40 10-inch pipe (254 mm)DO=5 1/8” (130 mm) for LTR and HGR
Q = 1100 GPM (250 m3/hr) – 10” pipe
Q = 650 GPM (148 m3/hr) – 8” pipe
N = 1760 RPM (motor speed)
Post MixingOptimization and Solutions
CFD Background
Post MixingOptimization and Solutions
CFD Background
ACUSOLVE GLS-FERigorous stability and convergence proofsLocal / Global Conservation operatorsHigh PerformanceAccuracy - Advective / Diffusive operators
Post MixingOptimization and Solutions Galerkin / Least-Squares
dnuqnpnw
duw
dbpuuuqwuw
duq
dwpwbuuuw
jjijjiii
e
ejjcii
e
eikkiikiktmijijt
ii
ijjiiiijijti
e
e
,,
,,,,,,
,
,,,,
Minimize error of approximating functionsHyperbolic/Parabolic Automatic: Stability and Convergence Proven
GLS Terms
M = O ( h / |V| ) Advective
M = O ( h2 / ) Diffusive
Post MixingOptimization and Solutions
Backward Facing Step Problem(Advection / Diffusion Example)
Reynolds number of 40,000
7,200 brick elements; 14,822 nodes
Spalart-Allmaras turbulence model
Advection / Diffusion “continuously” varying
Post MixingOptimization and Solutions
Backward Facing Step Problem(Advection / Diffusion Accuracy)
Even for this coarse mesh Able to predict the two
smaller eddies near the recirculation corner
Smallest eddy captured within a radius of 3-elements
Predicted reattachment length = 7.05 (step height) Experimental results =
7±0.1
Post MixingOptimization and Solutions
Results: CFD Mesh
These models tended to converge in the range of 20 to 30 nonlinear iterations, to a normalized residual tolerance of less than 1.0 E-3.
Runs on a 1.8 GHz laptop computer with 512 MB of memory in roughly 2 hours.
Runs on a parallel configuration of two 2.0 GHz PCs with 2.0 GB memory each, and the solutions required only about 30 minutes each
Post MixingOptimization and Solutions
CFD Solid Shapes
Lightnin Hayward Gordon
Radials
Axials
Post MixingOptimization and Solutions
CFD Modeling Considerations
Reduce Assumptions / Approximations Eliminate local entry flow assumptions for mixer inlet /
outlet - used long entry exit Model size (DOF) not a major issue Accurately solves forward / backward facing step
problems
Geometry Idealized Sufficient Fluid Mechanics Performance Equivalency Eliminates Vendor Conflict / Propriety
ICEM/CFD autohexa extensions for geometry/mesh
Post MixingOptimization and Solutions CFD Analysis Approach
Validation / Confirmation Approach Defined Standard tank configurations run to assess power and
flow characteristics independently with respect to Industry Data
Discretization sensitivity considered
General Flow Solution - Defined - (No Turbulence) Discretization dependent Captures flow separations / eddys May produce stable macro / mezzo flow oscillations Lower bound power / torque
Post MixingOptimization and Solutions CFD Analysis Approach (Cont)
Turbulence Considerations / Concepts Considered Philosophy - “unresolved” eddy diffusion / dissipation /
production Intended for “micro” scale turbulence Turbulence introduced becomes upper bound to power /
torque
Discrete particle tracking - Turbulent Residence Time Statistics Mixing Assessments Proprietary algorithms based on Eddy Viscosity
Post MixingOptimization and Solutions
Results: Power Number
Power numbersRP4, h/D=0.2
N=360 RPMP/V = 5 Hp/1000 gallons (1 kW/m3)Z/T = 1, 4 standard, wb/T = 0.1Np(CFD) = 2.985Np(Lightnin) = 3.4Oldshue Proximity Factor = 0.87, Np = 2.958CFD Proximity Factor = 0.878
Conclusion: Oldshue was right!
Post MixingOptimization and Solutions
Results: Power Number
Power numbers3PBT30, h/D=0.25
Np(CFD) = 0.55 OB/D = same as HGANp(CFD) = 0.57 OB/D = same as LTAPF=1.044: Agrees with Oldshue, again!Np(4PBT45, h/D=0.2) = 1.27Nagata: sin(angle)1.2 Np(4PBT30, h/D=0.2) = 0.63Shaw: Np(4PBT30, h/D=0.2)=0.58Nagata: 77.5% of a 4-bladed impeller
Np(3PBT30 h/D=0.2) = 0.45-0.48Nagata: h/D = 0.2 to 0.25 = an increase of 21%
Np(3PBT30 h/D=0.25) = 0.54-0.58Conclusion: Nagata was right!
Post MixingOptimization and Solutions
Results: Power Number
Post MixingOptimization and Solutions
Results: Power
These small units can agitate up to 1.584 Million Gallons (6 Million Liters) per day (at 1100 GPM (250 m3/hr))
Post MixingOptimization and Solutions
Results: P/V
85 P/V 715 Hp/1000 Gallons17 P/V 143 kW/m3
Post MixingOptimization and Solutions
Results: Impeller Flow to Throughput
Rule-of-thumb: Impeller generated flow should be at least 3 times the pipe throughput. Not one of these devices complies. Even the LTA appears to be doing some mixing at 650 GPM, which has R = 28% or about 1/4th the pipe flow rate. LTA seems to have lost its mixing ability at 1100 GPM.Perhaps the rule-of-thumb for Process Intensifiers is that impeller generated flow should be at least 1/4th the pipe throughput.
Post MixingOptimization and Solutions
Results: Pressure Drop
Default max-min pressure fields
Post MixingOptimization and Solutions
Results: Pressure Drop Normalized
Common scale pressure fields
Post MixingOptimization and Solutions
Results: Velocity Vectors
Post MixingOptimization and Solutions
Results: Velocity Vectors
Post MixingOptimization and Solutions
Results: Velocity Vectors
Post MixingOptimization and Solutions
Results: Velocity Vectors
Post MixingOptimization and Solutions
Results: Velocity Distribution
Post MixingOptimization and Solutions
Results: Flow Visualization
Post MixingOptimization and Solutions
Results: Flow Visualization
Post MixingOptimization and Solutions
Results: Tracer Study
LTA:
650
GPM
Post MixingOptimization and Solutions
Results: Tracer Study
LTA:
1100
GPM
Post MixingOptimization and Solutions
Results: Tracer Study
LTR:
1100
GPM
HGA:
1100
GPM
Post MixingOptimization and Solutions
Results: Residence Time Distribution
Post MixingOptimization and Solutions
Results: Residence Time Distribution
Post MixingOptimization and Solutions
Results: Residence Time Distribution
LTA: 1100 GPM Single Input, 1750 RPM Single Input, 0 RPM Multiple Inputs, 1750 RPM
Post MixingOptimization and Solutions
Results: Comparison to Non-Newtonian Fluid
Post MixingOptimization and Solutions
Conclusions
This report demonstrates the versatility of using CFD to model and understand a complex mixing device such as the Process Intensifier.Previous use of CFD often meant very long computing time and it was often quicker to do the experiment. Not any more.ACUSOLVE was successfully able to determine the power number of the impellers within 1% of reported values without the use of fudge factors on a repeatable basis. Must be right if it says that Oldshue and Nagata were right!
This demonstrates that the ACUSOLVE CFD code formulation and its adherence to fundamental physics are extensible to handle the arbitrary geometric structures and flow conditions of inline mixers.Solutions consistent with general fundamental understandings of these mixer classes. However, past conventional wisdom concerning assumed internal details, clearly challenged by detailed CFD results.
Post MixingOptimization and Solutions
www.postmixing.com
Four configurations studied, yielding insights for mixing improvements. For example, tracer inlet location sensitivity, impeller locations, pumping direction, size, speed.
All examples demonstrated under sized impeller capacity for specified flow. Part 2 will talk about impeller optimization for Process Intensifiers.
Specific optimizations are clearly a function of application, fluid rheology, and mixing needs.
Provides a substantial platform for further wide ranging parameter study for specific application optimization.
Post MixingOptimization and Solutions
Evidence of the speed and accuracy of Acusolve CFD Paper given last night from 5:27 PM to 6:00
PM Computational time = 90 minutes (Laptop) A Novel Mixing Technology Provides Benefits
in Alumina Precipitation, Ian C. Shepherd*, Clive Grainger, CSIRO Australia
T = 14 m, Z = 40 m, conical bottom, V 6158 m3
Upper Oversized RT D/T=0.30, w/D=0.333, h/D=0.29 Settling velocity = 0.126 m/s Upward (red) flow = 0.3 m/s Downward (blue) flow = 0.15 m/s Resulting Np = 4.7 (fully baffled 7.5) Resulting Power = 230 kW Resulting P/V = 0.037 kW/m3 = 0.18 Hp/1000
gallons