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Utilization of Dynamic Modeling to Evaluate and Design Secondary Clarifiers for the Muddy Creek WWTP
Michael D. Parker, PE Co-Authors: Joe Rohrbacher, PE Alonso Griborio, PE PhD Presented by Joe Rohrbacher, PE
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Agenda
Project Background Dynamic Modeling Approach Secondary Clarifier Modeling Results Project Summary
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Project Background
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City/County Utilities Commission (CCUC) Muddy Creek WWTP
§ 21 mgd rated capacity § Current average flows
~ 15 mgd
§ Monthly/weekly limits with seasonal ammonia limit § BOD – 25/40 mg/L § TSS – 30/45 mg/L § NH3-N – 14/35 mg/L
April-October
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Muddy Creek WWTP Unit Process Summary
Four Rectangular Primary Clarifiers § 7,000 sf each § 750 gpd/sf at 21 mgd
Three Aeration Basins § Provided with RAS step feed capability § Fully aerated for nitrification § No current nutrient removal processes Four Secondary Clarifiers § 110’ Diameter § 550 gpd/sf at 21 mgd § Inset launders
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Existing Secondary Clarifiers
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Occasional High ESS is Observed during Peak Flow Events
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Variable Settleability Impacts Performance
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Secondary Clarifier Improvements Project Objectives
§ Evaluate upgrades to existing secondary clarifiers § Increase center well sizing
§ Evaluate need to construct a fifth secondary clarifier § 110-ft diameter to match existing § Larger secondary clarifier to improve performance
§ Evaluate future secondary process requirements § Future 30 mgd capacity § Potential nutrient limits in the Yadkin River basin
§ Total nitrogen (TN) § Total phosphorus (TP)
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Dynamic Modeling Approach
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Whole Plant Simulator Models such as BioWin use Idealized 1-Dimensional Clarifier Models
§ Idealized solids flux (ex. state point analysis) § Does not account for hydrodynamics, flocculation,
environmental impacts
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2-Dimensional Clarifier Models Address many of these Limitations
§ Utilizes Computational Fluid Dynamics § Models:
§ Hydrodynamics § Flocculation § Turbulence § Temperature
§ Limitations § Wind § Complex inlets § Cannot model biological reactions
§ Simulates mechanical/structural features § Center wells, baffles, launder position, inlets
Concentration (g/L): 0.01 0.02 0.05 0.11 0.23 0.52 1.14 2.50 5.50
8.5 ft 14.8 ft 14 ft
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Calibrated Biological Process and Clarifier CFD Models were Developed
§ BioWin process model and Clarifier 2DC CFD models were linked through iterative dynamic simulation
§ Predict plant performance (solids production, MLSS, and effluent quality) at current and design conditions
§ Assess SC performance at design and peak wet weather flows § Assess potential for implementing BNR to meet future TN and
TP limits
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Intensive Site-Specific Sampling was Performed for Model Calibration
§ Plant historical influent and operational data analysis
§ Site specific sampling by CCUC/H&S to supplement data for process model calibration § Influent wastewater
speciation § Carbon / nitrogen /
phosphorus profiles through treatment process
§ Recycle stream impacts
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Column Testing was Performed to Understand Sludge Settleability Characteristics
Vs(ft/h) = 31.0e-0.523X (g/L)
R² = 0.9799
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5.0
10.0
15.0
20.0
25.0
30.0
35.0
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00
Settl
ing
Velo
city
(ft/h
)
Sludge Concentration (g/L)
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Clarifier Stress Testing was Performed to Determine Performance under Variable Loading
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Second
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larifier SO
R (gpd
/ft2 )
/ MLSS (m
g/L)
SOR
MLSS
3 SCs 2 SCs 1 SCs
Stress Testing Period
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Clarifier CFD Model Calibration
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7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00
Slud
ge Blanket Dep
th (ft)
Day 2 June 7th, 2011 (hour)
Blanket -‐ SC No. 1Blanket -‐ SC No. 2Blanket -‐ SC No. 3Blanket -‐ SC No. 4Blanket + Dispersed Zone -‐ SC No. 2Blanket + Dispersed Zone -‐ SC No. 4Model BlanketModel Blanket +Dispersed
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A Design Storm Hydrograph was Developed for Wet Weather Modeling
Muddy Creek WWTP Design Storm Hydrograph § Longest duration storm event based on several years of data § Peak flow of ~56 mgd coincided with a 30-day rolling average
flow of 21.4 mgd § Representative of sustained peak flow at rated capacity
ß February 3–9, 2010 à
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Secondary Clarifier Modeling Results
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Secondary Clarifier Performance was Evaluated for Maximum Month Conditions
§ Full year-round nitrification § Initial MLSS = 2,300 mg/L § Average SVI = 117 mL/g § 75th percentile SVI = 138 mL/g § 90th percentile SVI – 183 mL/g
§ Biological Nutrient Removal § Initial MLSS = 3,200 mg/L § Average SVI = 117 mL/g § 75th percentile SVI = 138 mL/g § 90th percentile SVI – 150 mL/g
§ The impacts of wet weather step feed operation were also evaluated
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4 vs. 5 Clarifier Operation with Step Feed MLSS = 2,300 mg/L, SVI = 180
4 clarifiers in operation TSS > 100 mg/L, clarifier failure
5 clarifiers in operation TSS < 20mg/L, clarifier performance excellent
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Predicted Effluent TSS at MLSS = 2,300 mg/L
SVI = 138 mL/g TSS < 20 mg/L with step feed or 5th clarifier
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56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100
Efflu
ent TSS (m
g/L)
Simulaton Time (hours)
MLSS = 2,300 mg/L No Step Feed -‐ 4 SC
MLSS = 2,300 mg/L Step Feed -‐ 4 SC
MLSS = 2,300 mg/L No Step Feed -‐ 5 SC
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56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100
Efflu
ent TSS (m
g/L)
Simulaton Time (hours)
MLSS = 2,300 mg/L No Step Feed -‐ 4 SC
MLSS = 2,300 mg/L Step Feed -‐ 4 SC
MLSS = 2,300 mg/L No Step Feed -‐ 5 SC
MLSS = 2,300 mg/L Step Feed -‐ 5 SC
SVI = 183 mL/g Failure with 4 clarifiers in service regardless of step feed Five clarifiers required
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Summary of Secondary Clarifier Modeling Results at 2,300 mg/L MLSS
SVI No. of SCs Step Feed Used?
Average Effluent TSS
Maximum Effluent TSS
Sludge Blanket Depth?
117 4 No
< 15 < 20 OK 5 < 15 < 20 OK
138
4 No
< 20 < 45 High 5 < 15 < 20 OK 4
Yes < 15 < 20 OK
5 < 15 < 20 OK
180
4 No
> 100 > 100 High/Failure
5 < 15 < 20 High
4 Yes
> 50 > 100 High/Failure
5 < 15 < 20 OK
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Summary of Secondary Clarifier Modeling Results at 3,200 mg/L MLSS (BNR)
SVI No. SCs/ Diameter
Step Feed Used?
Average Effluent TSS
Max. Effluent TSS
Sludge Blanket Depth?
117
4 / 110-ft No > 50 > 100 High/Failure 5 / 110-ft < 15 < 20 High 4 / 110-ft Yes > 50 > 100 High/Failure 5 / 110-ft < 15 < 20 High
138
5 / 110-ft No
> 50 > 100 High/Failure 4 / 110-ft +
1/125-ft > 50 > 100 High/Failure
5 / 110-ft Yes
> 50 > 100 High/Failure 4 / 110-ft +
1/125-ft < 15 < 20 High
150
4 / 110-ft + 1/125-ft No
> 50 > 100 High/Failure
4 / 110-ft + 2/125-ft < 15 < 20 OK
4 / 110-ft + 1/125-ft Yes
> 50 > 100 High/Failure
4 / 110-ft + 2/125-ft < 15 < 20 OK
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Secondary Clarifier Modeling Conclusions
§ Current permit limits – MLSS limited to 2,300 mg/L § One new 110-ft diameter clarifier required to treat peak flows under
poor settling conditions § Future potential BNR limits – MLSS increase
§ Risk of failure under 75th percentile settling conditions with five 110-ft secondary clarifiers
§ Two new 125-ft secondary clarifiers may be required under future BNR scenario
§ Construction of one new 125-ft secondary clarifier was recommended § Plan for second 125-ft clarifier if BNR is implemented in the future
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Dynamic Modeling was used to Optimize Fifth Secondary Clarifier Design
Relationship between effluent TSS and effluent launder distance to end wall for inset launders • Minimum distance of 4-feet required • Diminishing improvement beyond 10-feet
• Inset launders not designed for 5th clarifier
Relationship between effluent TSS and clarifier depth • Existing clarifiers 14-ft SWD • New clarifier 17-ft SWD
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Dynamic Modeling was used to Optimize Fifth Secondary Clarifier Design
Relationship between effluent TSS and center well submergence • Existing clarifiers 34.5% • New clarifier 45%
Relationship between effluent TSS and center well diameter • Existing clarifiers 14% • New clarifier 25%
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Project Summary
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The Secondary Clarifier Improvements are Currently Under Construction
§ The fifth clarifier has been placed in service § Existing clarifiers are being refurbished and
mechanisms are being replaced § RAS pumping improvements § MLSS distribution improvements
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Dynamic Modeling is a Powerful Tool in Optimizing Secondary Process Designs
§ Dynamic modeling can simulate multiple operational and design scenarios § Results in optimized designs
§ Integrating CFD and process models can introduce better confidence in simulated performance § Significant optimization/cost savings achievable
§ Dynamic modeling can be used to justify deferment of improvements and allow capacity rerates
§ There is a need in the WW industry for single-package integration of CFD with whole plant process models
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Acknowledgements § City/County Utilities Commission (CCUC)
§ Frank Crump § Keith Jones § Chris Shamel
§ Hazen and Sawyer § Chris White § Tony Greiner § Jim Struve § Paul Pitt
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Questions?
Michael Parker, PE 4944 Parkway Plaza Blvd, Suite 375
Charlotte, NC 28217 [email protected]
704 357-3150
Alonso Griborio, PhD, PE [email protected]
954 987-0066
Joe Rohrbacher, PE [email protected]
843 744-6467
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