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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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0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

Settl

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Velo

city

(ft/h

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Sludge Concentration (g/L)

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Clarifier Stress Testing was Performed to Determine Performance under Variable Loading

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Second

ary  C

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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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 mparker@hazenandsawyer.com

704 357-3150

Alonso Griborio, PhD, PE agriborio@hazenandsawyer.com

954 987-0066

Joe Rohrbacher, PE jrohrbacher@hazenandsawyer.com

843 744-6467