Process Intensifier: Optimization Using CFD Part 1

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


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


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


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


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


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)


CFD Background


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


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


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


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


CFD Solid Shapes

Lightnin Hayward Gordon

Radials

Axials


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


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!



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!


Results: Power

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


Results: P/V

85 P/V 715 Hp/1000 Gallons17 P/V 143 kW/m3


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.


Results: Pressure Drop

Default max-min pressure fields


Results: Pressure Drop Normalized

Common scale pressure fields


Results: Velocity Vectors


Results: Velocity Distribution


Results: Flow Visualization


Results: Tracer Study

LTA:

650

GPM



LTA:

1100

GPM



LTR:

1100

GPM

HGA:

1100

GPM


Results: Residence Time Distribution


Results: Residence Time Distribution

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


Results: Comparison to Non-Newtonian Fluid


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.


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.


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

Process Intensifier: Optimization Using CFD Part 1

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