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Modelli matematici applicati ai processi di filtrazione a membrana
— Mathematical modelling of MBR system
Biomath, Ghent University, Belgium06-06-2006
Tao Jiang
3
Overview of the presentation
• Modelling the biological performance of MBR
• Modelling of MBR fouling
4
Biological difference of MBR and TAS
• Complete retention of solids and partial retention of colloidal/macromolecular fraction
• Operational parameters
• Long SRT
• Short HRT
5
Colloidal fraction in MBR
• Colloidal: 0.001 µm - 1µm
• MBR membrane pore size: 0.03-0.4 µm
• non-settable flocs in TAS: < 5-10 µm
• Additional removal of solids by MBR
• Small flocs (0.45-10 µm)
• Partial retention of colloids (pore size - 0.45µm)
6
Colloidal concentration in MBR sludge
• TAS Effluent: 30-60 mg/L
• MBR sludge (<0.45µm): 50-200 mg/L
• MBR effluent (<pore size): 5-20 mg/L
• Membrane retention: 70-95%
7
Colloidal fraction is S or X?
• By size:
• Colloidal fraction < 0.45 µm S
• By retention:
• 70-90% retention X
• By biological degradation:
• Slow biodegradable X
8
Colloidal fraction is X
• Colloidal fraction is X, although smaller than 0.45 µm
• No significant error in TSS measurement, if the colloidal fraction is missing (CODCol<<CODTSS)
9
Influence of long SRT and short HRT
• High MLSS concentration
• MLSS=SRT/HRT*…..
• Increased sensitivity of X (advantage of calibration)
• Inert particulate COD build up in MBR
• XI= SRT/HRT*XI,in
• Careful wastewater characterization
• Low active biomass fraction
10
Membrane model
• Simple option (BNR study)
• Point settler and include the colloidal fraction into X
• Complete option (membrane fouling study)
• Define new variable S_SMP (X)
• Define retention of S_SMP by membrane
11
Modelling of settler vs. membrane
• TAS (settler)
• Difficulty in calibrating settling model
• Possible biological processes in settlers
• MBR (Membrane)
• Point separation (no volume)
• No biological processes
• Complete retention of X
• Partial retention of colloidal fraction
12
Modelling of a lab-scale MBR
Parameter/variable
Reference values
Influent rate 108 L/day
Aerobic 17 min
Anoxic mixing 11 min
Anoxic recirculation
12 min
SRT 17 days
HRT 6.4 hr
MLSS 7 g/L
Filtration flux 31.8 L/(m2h)
13
WEST – Configuration
CF_In FC_OutAnaerobic Aerobic
DO_Control
Sludge_Waste
Comb_1 Comb_2
Timer_DOTimer_R
Comb_3
Splitter_2
Internal_R
ASU_Pipe
Loop
Timer_RW
Splitter_RW
Timer_Pump
Timer_Pump2
UFInfluent Effluent
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WEST – Experimentation
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Aerobic_TSSgfedcAerobic_X_HgfedcbAerobic_X_AUTgfedcbAerobic_X_IgfedcbAerobic_X_PAOgfedcbAnaerobic_TSSgfedcAerobic_X_all_CODgfedcAerobic_X_all_TSSgfedc
0.1050.10.0950.090.0850.080.0750.070.0650.060.0550.050.0450.040.0350.030.0250.020.0150.010.005
5,200
5,000
4,800
4,600
4,400
4,200
4,000
3,800
3,600
3,400
3,200
3,000
2,800
2,600
2,400
2,200
2,000
1,800
1,600
1,400
1,200
1,000
800
600
400
Simulation results - Particulate
16
Simulation results - effluent
S_NHgfedcbS_NOgfedcbS_POgfedcbS_Ogfedc
0.1050.10.0950.090.0850.080.0750.070.0650.060.0550.050.0450.040.0350.030.0250.020.0150.010.005
9
8
7
6
5
4
3
2
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Simulation results - user defined
AnaerobicMassFractiongfedcSa_Denitrified/Total_InfluentgfedcbEBP/TotalP_removalgfedcbP_Waste_TSS/TSSgfedcPP/PAO_aerobicgfedc
0.1050.10.0950.090.0850.080.0750.070.0650.060.0550.050.0450.040.0350.030.0250.020.0150.010.005
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
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Objective of modelling MBR fouling
• Prediction of membrane fouling (TMP vs t)
• Facilitate integrated design, upgrading, operation
• Cost reduction
• …
19
Influence of biology on fouling
• Feed to membrane is activated sludge
• The composition of activated sludge is determined by the influent and operation of biological process
• How biology influence fouling
• What is the main foulant?
• Influence of MLSS, SRT, HRT, DO?
20
Foulant in MBRs
• The main foulant in MBRs is up to the influent composition, design and operation
• Particulate and colloidal can be the main foulant
• Colloidal fouling is getting more attention (soluble microbial products)
21
• Identify the main foulant
• Quantify the amount of foulant and their fouling potential
• Estimate the deposit rate of foulant on/in the membrane
• Predict additional resistance due to the foulant
• Estimate the reversibility of foulant by backwashing and chemical cleaning
Steps in the modelling of fouling
22
conclusion
23
• Modelling the biolgical performance of MBR is simpler than TAS
• Modelling of MBR fouling, especially fouling prediction is extremely difficult and pre-mature