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Process Intensifier: Optimization Using CFD Part 1

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Presentation on theme: "Process Intensifier: Optimization Using CFD Part 1"— Presentation transcript:

1 Process Intensifier: Optimization Using CFD Part 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 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 Introduction

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

4 CFD confirmation using standard mixing configurations, T=12. 5” (317
CFD confirmation using standard mixing configurations, T=12.5” (317.5 mm) RP4 radial impeller PBT axial impeller 5” RP4 D/T= ” 3PBT30 D/T=0.4 Experimental Design

5 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” RP x 5” RP x 3.5” 3PBT x 5” 3PBT30 Experimental Design

6 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) Experimental Design

7 CFD Background

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

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 Galerkin / Least-Squares

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 Backward Facing Step Problem (Advection / Diffusion Example)

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 = 7±0.1 Backward Facing Step Problem (Advection / Diffusion Accuracy)

12 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 Results: CFD Mesh

13 Lightnin Hayward Gordon
Radials Axials CFD Solid Shapes

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 CFD Modeling Considerations

15 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 CFD Analysis Approach

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 CFD Analysis Approach (Cont)

17 Power numbers RP4, h/D=0.2 Conclusion: Oldshue was right! N=360 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! Results: Power Number

18 Power numbers 3PBT30, h/D=0.25 Conclusion: Nagata was right!
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! Results: Power Number

19 Results: Power Number

20 These small units can agitate up to 1
These small units can agitate up to Million Gallons (6 Million Liters) per day (at 1100 GPM (250 m3/hr)) Results: Power

21 85  P/V  715 Hp/1000 Gallons 17  P/V  143 kW/m3 Results: P/V

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. Results: Impeller Flow to Throughput

23 Results: Pressure Drop
Default max-min pressure fields Results: Pressure Drop

24 Results: Pressure Drop Normalized
Common scale pressure fields Results: Pressure Drop Normalized

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 LTA: 650 GPM Results: Tracer Study

33 LTA: 1100 GPM Results: Tracer Study

34 LTR: 1100 GPM HGA: Results: Tracer Study

35 Results: Residence Time Distribution

36 Results: Residence Time Distribution

37 Results: Residence Time Distribution
LTA: GPM Single Input, 1750 RPM Single Input, 0 RPM Multiple Inputs, 1750 RPM Results: Residence Time Distribution

38 Results: Comparison to Non-Newtonian Fluid

39 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. Conclusions

40 Four configurations studied, yielding insights for mixing improvements
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 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 = 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/m3 = 0.18 Hp/1000 gallons

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