Process Intensifier: Optimization Using CFD Part 1

1
Process IntensifierOptimization Using CFDPart 1
Paper 362c

• 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

2
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

3
Introduction

• Internet Search
• Lightnin Line-Blender
• Radial and Axial impeller designs
• Hayward Gordon In-line Mixer
• Radial and Axial impeller designs
• No systematic study reported on them
• Use CFD to understand and optimize these pipe
  mixers

4
Experimental Design

• CFD confirmation using standard mixing
  configurations, T12.5 (317.5 mm)

RP4 radial impeller PBT axial impeller
5 RP4 D/T0.4 5 3PBT30 D/T0.4
5
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
6
Experimental Design

• All units were studied in a nominal schedule 40
  10-inch pipe (254 mm)
• DO5 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)

7
CFD Background
8
CFD Background

• ACUSOLVE GLS-FE
• Rigorous stability and convergence proofs
• Local / Global Conservation operators
• High Performance
• Accuracy - Advective / Diffusive operators

9
Galerkin / Least-Squares
GLS Terms
Minimize error of approximating
functions Hyperbolic/Parabolic Automatic
Stability and Convergence Proven
?M O ( h / V ) Advective ?M O ( h2 / ? )
Diffusive
10
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

11
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 70.1

12
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

13
CFD Solid Shapes
Lightnin Hayward Gordon
Radials Axials
14
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

15
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

16
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

17
Results Power Number

• Power numbers
• RP4, h/D0.2
• N360 RPM
• P/V 5 Hp/1000 gallons (1 kW/m3)
• Z/T 1, 4 standard, wb/T 0.1
• Np(CFD) 2.985
• Np(Lightnin) 3.4
• Oldshue Proximity Factor 0.87, Np 2.958
• CFD Proximity Factor 0.878
• Conclusion Oldshue was right!

18
Results Power Number

• Power numbers
• 3PBT30, h/D0.25
• Np(CFD) 0.55 OB/D same as HGA
• Np(CFD) 0.57 OB/D same as LTA
• PF1.044 Agrees with Oldshue, again!
• Np(4PBT45, h/D0.2) 1.27
• Nagata sin(angle)1.2 Np(4PBT30, h/D0.2)
  0.63
• Shaw Np(4PBT30, h/D0.2)0.58
• Nagata 77.5 of a 4-bladed impeller
• Np(3PBT30 h/D0.2) 0.45-0.48
• Nagata h/D 0.2 to 0.25 an increase of 21
• Np(3PBT30 h/D0.25) 0.54-0.58
• Conclusion Nagata was right!

19
Results Power Number
20
Results Power

• These small units can agitate up to 1.584 Million
  Gallons (6 Million Liters) per day (at 1100 GPM
  (250 m3/hr))

21
Results P/V

• 85 ? P/V ? 715 Hp/1000 Gallons
• 17 ? P/V ? 143 kW/m3

22
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.

23
Results Pressure Drop

• Default max-min pressure fields

24
Results Pressure Drop Normalized

• Common scale pressure fields

25
Results Velocity Vectors
26
Results Velocity Vectors
27
Results Velocity Vectors
28
Results Velocity Vectors
29
Results Velocity Distribution
30
Results Flow Visualization
31
Results Flow Visualization
32
Results Tracer Study

• LTA
• 650
• GPM

33
Results Tracer Study

• LTA
• 1100
• GPM

34
Results Tracer Study

• LTR
• 1100
• GPM
• HGA
• 1100
• GPM

35
Results Residence Time Distribution
36
Results Residence Time Distribution
37
Results Residence Time Distribution

• LTA 1100 GPM
• Single Input, 1750 RPM
• Single Input, 0 RPM
• Multiple Inputs, 1750 RPM

38
Results Comparison to Non-Newtonian Fluid
39
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.

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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.

41

• Evidence of the speed and accuracy of Acusolve
  CFD
• Paper given last night from 527 PM to 600 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/T0.30, w/D0.333, h/D0.29
• 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