Understanding hydrodynamics in membrane bioreactor systems for wastewater treatment:two-phase empirical and numerical modelling and experimental validation
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The document discusses modeling the hydrodynamics of a membrane bioreactor system using computational fluid dynamics (CFD) and empirical modeling. Experiments were conducted to measure shear stress and gas slug velocity in a tube under two-phase slug flow conditions. A CFD model was developed and validated against experimental data. Empirical models were also developed to correlate shear stress measurements and predict bimodal shear stress histograms. The models were then used to analyze pressure drop, energy consumption, and opportunities for optimization in the membrane bioreactor system. Finally, the structure of a PhD thesis modeling the hydrodynamics of a specific Norit airlift membrane system is outlined.
![Validation SP and HSC with CFD
TB rising velocity (9.9 mm) uTB = 1.2 um + 0.345 [g d ]0.5
U TB um
=C +k
(g d ) 0.5
(g d ) 0.5 1.2
HSC
Sim y = 1.00x + 0.41
R2 = 0.99
• Theoretical values 1
- C = 1.2 0.8
- k = 0.35
y = 1.04x + 0.30
0.5 R2 = 0.99
UTB/(gd)
0.6
0.4
0.2
0
0 0.1 0.2 0.3 0.4 0.5 0.6
Um/(gd)0.5
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Understanding hydrodynamics in membrane bioreactor systems for wastewater treatment:two-phase empirical and numerical modelling and experimental validation
- 1. www.mbr-network.eu Understanding hydrodynamics in membrane bioreactor systems for wastewater treatment: two-phase empirical and numerical modelling and experimental validation Nicolás Ratkovich Faculty of Bioscience Engineering Ghent University May 3rd 2010, Ghent - Belgium
- 2. Introduction Waste water treatment processes • Goals - Produce clean effluent - Recover nutrients and energy from waste stream • Biological treatment - Conventional Activated Sludge (CAS) – Gravity-based separation Influent Effluent Air Bioreactor Settler 2
- 3. Introduction Waste water treatment processes (cont.) • Biological treatment (cont.) - Membrane Bioreactor (MBR) – Filtration-based separation Influent Effluent Side-stream Air Bioreactor Membrane Effluent Influent Immersed Air Bioreactor 3
- 4. Introduction Waste water treatment processes (cont.) • Comparison (pros & cons) CAS MBR Sludge production ↑ ↓ Effluent quality ↓ ↑ Disinfection ↓ ↑ Footprint ↑ ↓ Problem Settling Fouling Energy consumption ↓ ↑ Cost ↓ ↑ 4
- 6. Introduction MBR economics • Energy - MBR > CAS 0.15 O&M costs breakdown ($/m3) - MBR ≈ CAS + TT 0.10 • Total cost - MBR > CAS - MBR ≈ CAS + TT 0.05 • Effluent quality 0.00 - MBR > CAS CAS MBR CAS+TT - MBR = CAS + TT Cote et al. (2004) Energy optimization TT: Tertiary treatment (polishing) (air sparging) 6
- 7. Introduction Membrane fouling (drawback) • Caused by attachment of… - suspended solids and - soluble substances • Mechanisms of fouling: Resistances Operation Clean membrane Filtration Membrane Relaxation Pore blocking Backwash Cake build up 7
- 8. Introduction Gas slug Air sparging • Used as fouling control • Gas-liquid (two-phase) flow in vertical tubes • Slug flow • Advantages - Airlift (buoyancy) - Scouring effect (shear stress) Air flow 8
- 9. Motivation Influent • Membrane Fouling Effluent - Scouring effect (shear) Biology - Particle removal Particle size Hydrodynamics distribution • Energy consumption - Aeration (air sparging) - Viscosity Filtration TMP - Flux 9
- 10. Outline 10
- 11. Objectives To observe and measure… • Behaviour of developed gas slug • Shear stress using Shear Probes (SP) • Gas slug rising velocity using High Speed Camera (HSC) To develop… • CFD and empirical models To quantify… • Pressure drop and energy consumption of the system 11
- 12. Introduction Slug flow • 3 Zones • Large shear stress values Falling film zone • Dynamic shear stress Wake zone Liquid slug zone *Taha & Cui, 2006 12
- 13. Experimental set-up Tube diameter: • 9.9 mm Fluids used: • Water + electrolyte Flow rates: • Liquid: 0.1 - 0.5 l⋅min-1 • N2: 0.1 - 0.3 l⋅min-1 13
- 14. Experimental set-up at UBC (SP) Electrolyte solution Fe(CN )6 + e → Fe(CN )6 3− 4− • Cathode (probes): Fe(CN )6 → Fe(CN )6 + e 4− 3− • Anode (pipe fitting): Shear probe (magnitude) Shear stress 0.70 1 Probe 1 Probe 1 0.60 0.8 0.50 Shear stress (Pa) Voltage (V) 0.6 0.40 0.30 0.4 0.20 0.2 0.10 0.00 0 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Time (s) Time (s) 14
- 15. Experimental set-up at UBC (SP) 2 Shear probes (flow direction) Two voltage signals Shear stress Gas slug Gas slug 15
- 16. Experimental measurements Experimental measurements • i.e. 0.11 - 0.06 m s-1 (water-N2) 2 Shear probe 1.5 1 0.5 Shear stress (Pa) 0 -0.5 -1 -1.5 -2 -2.5 -3 4 4.5 5 5.5 6 6.5 7 Time (s) 16
- 17. Experimental measurements Shear stress Histogram (SSH) • i.e. 0.11 - 0.06 m s-1 (water-N2) 0.45 0.4 0.35 Gas slug 0.3 Frequency (-) 0.25 0.2 0.15 0.1 0.05 Liquid slug 0 -3 -2 -1 0 1 2 3 Shear stress (Pa) 17
- 18. Experimental measurements 0.5 Gas 0.1 l/min Gas 0.2 l/min Gas 0.3 l/min 0.4 0.3 Frequency 0.2 0.1 0 -3 -2 -1 0 1 2 3 Shear stress (Pa) Liquid 0.1 l·min-1 18
- 19. Computational Fluid Dynamics CFD model • Numerical methods and algorithms • Analyze problems that involve fluid flows • Interaction of liquid-gas 19
- 20. Computational Fluid Dynamics Slug flow CFD model • Fluids flow in a vertical tube - Superficial velocities (gas + liquid) - Volume fraction • Validated against… - Shear stress measurements (SP) - Gas slug rising velocity (HSC) 20
- 21. Validation SP and HSC with CFD 1.5 1 Phase, Velocity (m/s), Shear stress (Pa) 0.5 0 -0.5 -1 -1.5 -2 -2.5 wall Phase -3 Velocity Shear stress -3.5 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (s) 21
- 22. Validation SP and HSC with CFD Liquid slug: well predicted Gas slug: shifted to the left 0.5 Exp 0.1 l/min Exp 0.2 l/min Exp 0.3 l/min 0.4 Sim 0.1 l/min Sim 0.2 l/min Sim 0.3 l/min 0.3 Frequency 0.2 0.1 0 -3 -2 -1 0 1 2 3 Liquid 0.1 l·min-1 Shear stress (Pa) 22
- 23. Validation SP and HSC with CFD TB rising velocity (9.9 mm) uTB = 1.2 um + 0.345 [g d ]0.5 U TB um =C +k (g d ) 0.5 (g d ) 0.5 1.2 HSC Sim y = 1.00x + 0.41 R2 = 0.99 • Theoretical values 1 - C = 1.2 0.8 - k = 0.35 y = 1.04x + 0.30 0.5 R2 = 0.99 UTB/(gd) 0.6 0.4 0.2 0 0 0.1 0.2 0.3 0.4 0.5 0.6 Um/(gd)0.5 23
- 24. Shear Stress Histograms Empirical model • Correlate shear stress with… - Magnitude - Direction - Gas-liquid flow rates • Occurrence of both peaks (height + width) - Better fouling control (Ochoa et al. 2007) • Bimodal SSH based on Gaussian distribution 0.3 0.25 0.3 0.25 0.25 0.2 0.2 0.2 = + 0.15 Frequency Frequency Frequency 0.15 0.15 0.1 0.1 0.1 0.05 0.05 0.05 0 0 0 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 Shear stress (Pa) Shear stress (Pa) Shear stress (Pa) Gas slug Liquid slug 24
- 25. Bimodal distribution SSH • liquid-gas flow rates that equilibrates the peaks 0.3 0.1 - 0.43 l/min 0.2 - 0.49 l/min 0.3 - 0.54 l/min 0.25 0.4 - 0.58 l/min 0.5 - 0.63 l/min Liq - gas Relative frequency (-) 0.2 0.15 0.1 0.05 0 -3 -2 -1 0 1 2 3 Shear stress (Pa) 25
- 26. Pressure drop and energy consumption Pressure drop 22000 Gas flow 0.0 L/min Gas flow 0.1 L/min Gas flow 0.2 L/min 21000 Gas flow 0.3 L/min Total pressure drop (Pa) 20000 7% 19000 18000 17000 16000 0 0.1 0.2 0.3 0.4 0.5 0.6 Liquid flow rate (L/min) 26
- 27. Pressure drop and energy consumption Pump power 0.25 Gas flow 0.0 L/min Gas flow 0.1 L/min Gas flow 0.2 L/min Gas flow 0.3 L/min 7% 0.2 0.15 Epump (W) 0.1 0.05 0 0 0.1 0.2 0.3 0.4 0.5 0.6 Liquid flow rate (L/min) 27
- 28. Pressure drop and energy consumption Blower power 0.25 Gas flow 0.0 L/min Gas flow 0.1 L/min Gas flow 0.2 L/min 0.2 Gas flow 0.3 L/min 0.15 Eblower (W) 0.1 70 % 0.05 0 0 0.1 0.2 0.3 0.4 0.5 0.6 Liquid flow rate (L/min) 28
- 29. Pressure drop and energy consumption Total power 0.4 Gas flow 0.0 L/min Gas flow 0.1 L/min 0.35 Gas flow 0.2 L/min Gas flow 0.3 L/min 0.3 0.25 25 % Etotal (W) 0.2 0.15 0.1 0.05 0 0 0.1 0.2 0.3 0.4 0.5 0.6 Liquid flow rate (L/min) 29
- 30. Pressure drop and energy consumption Optimal bimodal SSH • Pressure drop: ↓4% • Blower power: ↑9% • Pump power: ↑2% • Total power: ↑2% 16000 0.6 Total pressure drop Epump Eblower Etotal 15500 0.5 Energy consumption (W) Total pressure drop (Pa) 15000 0.4 14500 0.3 14000 0.2 13500 0.1 13000 0 0 0.1 0.2 0.3 0.4 0.5 0.6 Liquid flow rate (L/min) ↑ gas flow ↓ fouling does not result in large increase in energy consumption 30
- 31. PhD thesis structure 31
- 32. Objectives CFD modelling of Norit Airlift system • Membrane module - Membrane resistance (1 tube) - Bundle of tubes (700 tubes) • Air diffuser - Ring aerator - Disk aerator • Modelling exercise - In-situ measurement is tough 32
- 33. Norit airlift system Membrane module • Length 3 m • 700 tubes • ID 5.2 mm 33
- 34. Membrane Module (1 tube) water Membrane tube outlet • 3D single UF tube - Hydrodynamics - Filtration - Single phase flow Permeate outlet • Membrane resistance - Viscous resistance (Darcy’s law) - Inertial resistance water inlet Membrane Outside tube volume 34
- 35. Membrane Module (700 tubes) Membrane module • Step-wise extrapolated to 700 tubes • Two resistances - Membrane resistance - Bundle of tubes resistance CFD model • Calibrated in single-phase flow - Mass balance to determine resistance values (TMP + Flux) 35
- 36. Air diffusers Two types • Ring aerator outlet Ring aerator • Disk aerator Water inlet Disk aerator 36
- 37. Air diffusers Inlet of membrane Ring aerator Red 0.05 – Blue 0 volume fraction of air module Module Air diffuser Disk aerator Module Air diffuser 37
- 38. Module + air diffuser Red 0.2 – Blue 0 volume fraction of air Module + air diffuser • Ring aerator: - Air near the wall • Disk aerator: - Air in the bulk Membrane Module 3m Ring aerator Water inlet Diffuser Disk 0.5 m aerator Disk Ring 38
- 39. Outline 39
- 40. Objectives Sludge rheology • Viscosity • Delft Filtration Characterization method (DFCm) unit • Activated sludge rheological model 40
- 41. Sludge Rheology Viscosity • It describes a fluid's internal resistance to flow • Why is it important… - To characterize hydraulic regime near membrane. - Design of equipment (e.g. mixing, pumping, aeration devices) k 41
- 42. Sludge Rheology Viscosity (cont.) • Relation between shear stress (τ ) and shear rate (γ): - Newtonian (e.g. water, oil) - Non-Newtonian (e.g. blood, toothpaste, ketchup & activated sludge) Activated sludge • Pseudoplastic (Power-law) τ = kγ n - or η = kγ n −1 k & n = f (TSS ) k Flow behaviour index (Pa·s) n Flow consistency index (-) 42
- 43. Sludge Rheology Rotational rheometers: Pout • Torque is correlated to viscosity • Drawback - Measurement ex-situ - Eddies formation Tubular (capillary) rheometers: • Pressure drop is correlated to viscosity • Drawback - Large sludge samples • Advantage: TMP Sample - Measurement on-site J • Can the DFCm unit be used as a tubular rheometer? Pin CFV CFV 43
- 44. Sludge Rheology Viscosity in a tube 0.016 Experimental data This work 0.014 Rosenberger et al. (2006) Pollice et al. (2007) 0.012 Water Apparent viscosity (Pa s) 0.01 0.008 0.006 0.004 0.002 0 0 2 4 6 8 10 12 14 16 18 20 TSS (g/l) 44
- 45. Conclusions Modelling of slug flow • SSH used to represent slug flow • First peak (liquid slug) is properly captured by the CFD model • Second peak (gas slug) is shifted to the left • SSH with two balanced peaks is desirable - To decrease/control fouling - However, more energy is required Modeling of airlift MBR (modelling exercise) • Step-wise extrapolation was made for the tube-bundle (700) and membrane resistance. • Two types of diffusers were modeled - Disk aerator provides a better dispersion of air within the module than the ring aerator. 45
- 46. Conclusions Sludge rheology • A new rheological model for MBR activated sludge is presented based on the data collected using the DFCm. • It was found that the previous models underestimate the data collected from different MBR plants. - Difference in sludge composition and apparatus used 46
- 47. Perspectives Sludge rheology • Model that includes floc structure, size, strength, etc. Two-phase flow • Varying thermo-physical properties to study coalescence effects. Shear stress for non-Newtonian liquids • Electrolyte solution mix with a non-Newtonian liquid (e.g. CMC) Air diffusers • To study the air distribution in non-Newtonian fluids. 47
- 48. Thank you for your attention 48