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Post Mixing Optimization and Solutions Process Intensifier: Optimization Using CFD Part 1 Pete Csiszar, Black & Baird Ltd., North Vancouver, B.C. Keith.

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Presentation on theme: "Post Mixing Optimization and Solutions Process Intensifier: Optimization Using CFD Part 1 Pete Csiszar, Black & Baird Ltd., North Vancouver, B.C. Keith."— Presentation transcript:

1 Post Mixing Optimization 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

2 Post Mixing Optimization 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

3 Post Mixing Optimization and Solutions 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 Post Mixing Optimization 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

5 Post Mixing Optimization 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

6 Post Mixing Optimization and Solutions Experimental Design All units were studied in a nominal schedule 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)

7 Post Mixing Optimization and Solutions CFD Background

8 Post Mixing Optimization and Solutions CFD Background ACUSOLVE GLS-FE Rigorous stability and convergence proofs Local / Global Conservation operators High Performance Accuracy - Advective / Diffusive operators

9 Post Mixing Optimization and Solutions Galerkin / Least-Squares Minimize error of approximating functions Hyperbolic/Parabolic Automatic: Stability and Convergence Proven GLS Terms  M = O ( h / |V| ) Advective  M = O ( h 2 /  ) Diffusive

10 Post Mixing Optimization 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

11 Post Mixing Optimization 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

12 Post Mixing Optimization 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

13 Post Mixing Optimization and Solutions CFD Solid Shapes Lightnin Hayward Gordon Radials Axials

14 Post Mixing Optimization 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

15 Post Mixing Optimization 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

16 Post Mixing Optimization 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

17 Post Mixing Optimization and Solutions Results: Power Number Power numbers RP4, h/D=0.2 N=360 RPM P/V = 5 Hp/1000 gallons (1 kW/m 3 ) Z/T = 1, 4 standard, w b /T = 0.1 Np(CFD) = Np(Lightnin) = 3.4 Oldshue Proximity Factor = 0.87, Np = CFD Proximity Factor = Conclusion: Oldshue was right!

18 Post Mixing Optimization and Solutions Results: Power Number Power numbers 3PBT30, h/D=0.25 Np(CFD) = 0.55 OB/D = same as HGA Np(CFD) = 0.57 OB/D = same as LTA PF=1.044: Agrees with Oldshue, again! Np(4PBT45, h/D=0.2) = 1.27 Nagata: sin(angle) 1.2 Np(4PBT30, h/D=0.2) = 0.63 Shaw: Np(4PBT30, h/D=0.2)=0.58 Nagata: 77.5% of a 4-bladed impeller Np(3PBT30 h/D=0.2) = Nagata: h/D = 0.2 to 0.25 = an increase of 21% Np(3PBT30 h/D=0.25) = Conclusion: Nagata was right!

19 Post Mixing Optimization and Solutions Results: Power Number

20 Post Mixing Optimization and Solutions Results: Power These small units can agitate up to Million Gallons (6 Million Liters) per day (at 1100 GPM (250 m3/hr))

21 Post Mixing Optimization and Solutions Results: P/V 85  P/V  715 Hp/1000 Gallons 17  P/V  143 kW/m 3

22 Post Mixing Optimization 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.

23 Post Mixing Optimization and Solutions Results: Pressure Drop Default max-min pressure fields

24 Post Mixing Optimization and Solutions Results: Pressure Drop Normalized Common scale pressure fields

25 Post Mixing Optimization and Solutions Results: Velocity Vectors

26 Post Mixing Optimization and Solutions Results: Velocity Vectors

27 Post Mixing Optimization and Solutions Results: Velocity Vectors

28 Post Mixing Optimization and Solutions Results: Velocity Vectors

29 Post Mixing Optimization and Solutions Results: Velocity Distribution

30 Post Mixing Optimization and Solutions Results: Flow Visualization

31 Post Mixing Optimization and Solutions Results: Flow Visualization

32 Post Mixing Optimization and Solutions Results: Tracer Study LTA: 650 GPM

33 Post Mixing Optimization and Solutions Results: Tracer Study LTA: 1100 GPM

34 Post Mixing Optimization and Solutions Results: Tracer Study LTR: 1100 GPM HGA: 1100 GPM

35 Post Mixing Optimization and Solutions Results: Residence Time Distribution

36 Post Mixing Optimization and Solutions Results: Residence Time Distribution

37 Post Mixing Optimization and Solutions Results: Residence Time Distribution LTA: 1100 GPM Single Input, 1750 RPM Single Input, 0 RPM Multiple Inputs, 1750 RPM

38 Post Mixing Optimization and Solutions Results: Comparison to Non- Newtonian Fluid

39 Post Mixing Optimization 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.

40 Post Mixing Optimization and Solutions 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 Post Mixing Optimization 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 m 3 Upper Oversized RT D/T=0.30, w/D=0.333, h/D=0.29 Settling velocity = 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 = kW/m 3 = 0.18 Hp/1000 gallons


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