kyrene water reclamation plant process overview project no

Final August 2009

City of TempeTempe, Arizona

Kyrene Water Reclamation Plant Process OverviewProject No. 32-997044

SUMMARY REPORTKyrene WRF

Bar Screens Building



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J:\Projects\Tempe\0610L\06 Gen Std & Rept\6.1 Report\0610L_Report_03SEP09.docx

Abbreviations / Acronyms........................................................................................ A-1

1. Introduction ............................................................................................................... 1

2. Evaluation of High H2S Issues at the Bar Screens Building ................................. 1

2.1 Building Layout and Components .......................................................................................1

2.2 H2S Measurements ............................................................................................................2

2.3 FRP Ventilation Alignment and Size ...................................................................................4

2.4 Impact of Bioxide Application ..............................................................................................5

2.4.1 City of Tempe Bioxide Field Testing....................................................................................5

2.4.2 Process Model Sensitivity Analysis – Impact of Bioxide......................................................7

2.4.3 Bioxide Addition Conclusion................................................................................................9

2.5 Conclusions and Recommendations...................................................................................9

3. Screens Building Area Classification.................................................................... 11

3.1 National Electrical Code (NEC) .........................................................................................11

3.2 National Fire Protection Act (NFPA) 820...........................................................................12

3.3 Occupational Safety and Health Standards for General Industry (OSHA) Workplace Standard/Exposure Limits for Hydrogen Sulfide ...............................................................12

3.4 National Institute for Occupational Safety and Health (NIOSH) ........................................12

3.5 American Conference of Governmental Industrial Hygienists (ACGIH) ............................12

3.6 Conclusions and Recommendations.................................................................................13

4. Coarse Screen Drains ............................................................................................. 13

4.1 Current Operation .............................................................................................................13


5. Existing Process Evaluation .................................................................................. 13


5.2 Process Evaluation with Modeling.....................................................................................16

5.3 Model Calibration ..............................................................................................................16

Philipbr

Polygon

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Text Box

Headworks Improvements previously implemented in 2010



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5.4 Model Prediction of Current Operation with HOD..............................................................18

5.5 Effect of Dynamic Flow and Loadings ...............................................................................19

5.6 Daily Operations Analysis Based on SSD.........................................................................24

6. Process Air Evaluation ........................................................................................... 26


6.2 Sensitivity Analysis on Airflows .........................................................................................27

6.3 Long-Term, Diurnal Loading Analysis ...............................................................................29

6.3.1 Scenario 1 – Diurnal Flow and Load with Constant Airflow Rate ......................................30

6.3.2 Scenario 2 – Diurnal Flow and Load with Airflow Rate Paced with Load Variations .........32

6.3.3 Scenario 3 – Diurnal Flow and Load with Constant Airflow Rate, But Increased AX Zone by 33% ..............................................................................................................................33


7. Scum Removal Evaluation ..................................................................................... 35


7.2 Sensitivity Analysis on Waste Biosolids for Existing System.............................................35

7.3 Sensitivity Analysis on Waste Biosolids with Increased AX Zone by 33% ........................37


8. Existing Plant Water System Evaluation............................................................... 40

8.1 Increased Influent Flows ...................................................................................................41

8.2 Increased Use of Equalization Basis.................................................................................41

8.3 Use of Potable Water ........................................................................................................42


List of Tables

Table 1 Screens Building Air Change Study Results .....................................................................................................4Table 2 City of Tempe Bioxide Addition Impact on H2S Concentrations .......................................................................6Table 3 Comparison of Operation and Design Parameters .........................................................................................14Table 4 Existing and Future NPDES Permit Limits on Key Parameters .......................................................................15



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Table 5 Comparison of Model Prediction with SSD .....................................................................................................17Table 6 Mass Balance of Influent COD and Waste Biosolids with SSD .......................................................................18Table 7 Comparison of Model Prediction with HOD .....................................................................................................19Table 8 Mass Balance of Influent COD and Waste Biosolids with HOD ......................................................................19Table 9 Dynamic Analysis Input Data ..........................................................................................................................20Table 10 Continuous Daily Simulation Input Data ........................................................................................................24Table 11 Summary of Existing Air Blowers ..................................................................................................................26Table 12 Summary Sensitivity Analysis on Biosolids Waste Rate ...............................................................................37Table 13 Summary Sensitivity Analysis on Biosolids Waste Rate with Increased AX Zone by 33% ...........................39

List of FiguresFigure 1 Kyrene WRF Process Diagram ........................................................................................................................1Figure 2 Kyrene WRF Bar Screens Building Layout ......................................................................................................2Figure 3 Kyrene WRF Bar Screens Building H2S Data .................................................................................................3Figure 4 Kyrene WRF Bar Screens Building H2S Data .................................................................................................5Figure 5 City of Tempe Bioxide Addition Locations ........................................................................................................6Figure 6 Impact of Bioxide Addition on H2S Concentrations in Bar Screens Building ...................................................7Figure 7 MLSS vs. Maximum Allowable Influent NO3-N Concentration .........................................................................8Figure 8 Proposed Odor Control Alignment for Bar Screens Building ..........................................................................10Figure 9 Proposed External Gas Monitoring System ...................................................................................................11Figure 10 GPS-X Model Layout ...................................................................................................................................16Figure 11 Influent Flow and Load Variations after Equalization ...................................................................................21Figure 12 Results of Sinusoidal Dynamic Simulation - DO variations ..........................................................................22Figure 13 Results of Sinusoidal Dynamic Simulation – Effluent TKN, NO3-N and NH3-N ............................................23Figure 14 Results of Sinusoidal Dynamic Simulation – Effluent COD, BOD and TSS .................................................24Figure 15 Results of Simulation with Continuous Daily Data – Effluent COD ..............................................................26Figure 16 Sensitivity Analysis on Process Air Flow Rate to OX Zone ..........................................................................28Figure 17 Sensitivity Analysis on Cross-Air Flow Rate to MBR Reactors ....................................................................29Figure 18 DO Profile from Simulation of Diurnal Flow and Load with Constant Airflow ...............................................31Figure 19 Effluent NO3-N Profile from Simulation of Diurnal Flow and Load with Constant Airflow .............................32Figure 20 Effluent NO3-N Profile from Simulation of Diurnal Flow and Load with Airflow Paced to Load Variations ...33Figure 21 Effluent NO3-N Profile from Simulation of Diurnal Flow and Load with Constant Airflow but Increased AX

Zone by 33% ..................................................................................................................................................34Figure 22 Sensitivity analysis of Biosolids Waste Rate with SSD ................................................................................36Figure 23 Summary Sensitivity Analysis on Biosolids Waste rate with Increased AX Zone by 33% ............................38Figure 24 Existing Plant Water System .......................................................................................................................41Figure 25 Existing Plant Water System ........................................................................................................................42Figure 26 Plant Water System Modified to Utilize Potable Water ................................................................................43



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List of Appendices

(Data on Separate CD)

Appendix A Kyrene WRF H2S DataAppendix B Process Modeling Results – Impact of Bioxide AdditionAppendix C Screens Building Area Classification ReferencesAppendix D Kyrene WRF Plant Operating DataAppendix E Kyrene WRF Model Calibration DataAppendix F Kyrene WRF Process Modeling Results



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Abbreviations / Acronyms

Abbreviation / Acronym Meaning

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers

AX AnoxicBOD Biochemical Oxygen Demandcfm Cubic Feet Per MinuteCOD Chemical Oxygen DemandDO Dissolved OxygenEQ EqualizationFRP Fiberglass-Reinforced PlasticH2S Hydrogen SulfideHOD Historical Operating DataIPS Influent Pump StationLE Ludzack-EttingerLEL Lower Explosive LimitMBR Membrane BioreactorMLSS Mixed Liquor Suspended Solidsmg/l Milligrams Per LiterMGD Million Gallons per DayNEC National Electrical CodeNFPA National Fire Protection ActNIC Notice of Intended ChangeNPDES National Pollutant Discharge Elimination SystemOSHA Occupational Safety and Health Standards for General IndustryOX AerobicPDD Plant Design Datappm Parts Per MillionRAS Return Activated Sludgescfm Standard Cubic Feet Per MinuteSRB Sulfate-Reducing BacteriaSSD Special Sampling Data



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Abbreviation / Acronym Meaning

STEL Short-Term Exposure LimitTKN Total Kjeldahl NitrogenTN Total NitrogenTSS Total Suspended SolidsTWA Time Weighted AverageWAS Waste Activated SludgeWRF Water Reclamation Facility



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1. IntroductionThe Kyrene Water Reclamation Facility (WRF) in Tempe, Arizona was originally designed as a conventional activated sludge plant. It has been retrofitted to accommodate a membrane bioreactor (MBR). The plant currently operates at an approximate average daily flow of 4 MGD with a Ludzack-Ettinger (LE) configuration. This configuration includes an anoxic zone for substrate-based denitrification and the return of the mixed liquor from the membrane unit to the anoxic zones, as shown in Figure 1.

Figure 1Kyrene WRF Process Diagram

In the past, the plant operations and maintenance staff have identified several areas of concern that appear to affect the overall operation of the plant. These areas primarily include the biological process and the bar screens building.

The analyses and conclusions presented in this document are in no way intended to be a critique,challenge, or rebut of the judgments and opinions on plant design, operating performance or maintenanceeffectiveness of either the City of Tempe or any of the processional consultants the City has retained.Rather, the services described herein are in response to the City of Tempe’s request for further evaluations in an effort to continue to improve upon its record of excellence in owning and operating a pollution control facility.

2. Evaluation of High H2S Issues at the Bar Screens Building

2.1 Building Layout and ComponentsThe bar screens building at Kyrene WRF is a single story building designed to provide coarse and fine screenings for influent flows. The building layout is shown in Figure 2.

WAS

AIR SUPPLY

IMMERSED MEMBRANE

UNIT

BIOREACTOR UNIT

RAS

INFLUENT

FINAL EFFLUENT

77

22

EQ ANOXICP

P

SCUM



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3.6 Conclusions and RecommendationsThe existing conditions at the screens building indicate that an appropriate building classification is Class I, Division I, Group D. This type of building requires the following items be available:

Portable fire extinguisher

Hydrant protection

Combustible gas detection system

In order to modify the bar screens building classification, the number of air changes in the building must be increased to at least 12 air changes per hour and the measured concentrations of H2S must be decreased. The modifications presented in Section 1.5 are intended to achieve this goal.

4. Coarse Screen Drains

4.1 Current OperationThe existing coarse bar screens are connected to an 8-inch pipe that leads to the sewer. Screenings collected by each of the two coarse screens are flushed through a shared trough to the drain line with non-potable water from the plant water system. In the past, blockages in the 8-inch pipe have caused overflows in the bar screens building.

4.2 Conclusions and RecommendationsTo reduce the raw sewage overflows caused by drain blockages, the following alternatives should be further investigated:

Create a new, larger (14-inch) drain line to convey larger screenings to sewer

Install a washer-compacter for each screen

Investigate possible upstream locations for installation of a larger opening bar screen to prevent very large materials from entering the bar screens building

5. Existing Process EvaluationThe existing biological process was evaluated in response to concerns about nitrate levels in the final process effluent. This analysis included the development of a model based on process data collected by the Kyrene WRF staff, which are included in Appendix D. The model calibration data are included in Appendix E and the modeling results are included in Appendix F.

5.1 Current OperationCurrently, the metered sewage enters the plant by pumping. After passing through coarse and fine screens, the influent is equalized in the existing equalization tanks. The equalized influent is pumped into the flow splitter box where it is mixed with mixed liquor return stream and split into eight bioreactors that are divided into anoxic and aerobic reactor zones. The bioreactor effluent enters into ten membrane reactors where separation of solids from the effluent takes place. Bypassed flows from several liquid



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stream treatment units and waste activated sludge from membrane reactors are discharged to the collection system that is tributary to the 91st Avenue Wastewater Treatment Plant (WWTP). The effluent from the membrane system is metered before being reused for golf course irrigation and lakes, or discharge to SRP or storm drain.

In order to evaluate the performance of the current plant operation, the available operating data were compiled for a period from October 2006 through December 2008, as summarized in Appendix D. Aclose examination of the historical operating data showed that one 12-month period of data could providethe most reliable and complete set of operating data. Therefore, the average of 2008 data was selected as historical operating data (HOD) for further analysis.

In addition, the plant staff collected special sampling data (SSD) during December 15 through 25, 2008 to provide data not only for the plant influent and effluent characteristics, but also for operating characteristics and biological treatment performance. The SSD does include internal operating parameters for AX and OX zones of the bioreactors and membrane (MBR) reactors. The SSD were usedfor model calibration. These data are summarized along with original plant design data (PDD) for comparison, as shown in Table 3.

Table 3Comparison of Operation and Design Parameters

Parameter Plant DesignData (PDD)

Special SamplingData (SSD)1

Historical OperatingData (HOD)2

Influent COD, mg/l 740 718 485 BOD5, mg/l 276 296 274 TSS, mg/l 325 2353 214 TKN, mg/l 43.0 43.0 30.5Bioreactors MLSS, mg/l 10,000 9,370 - RAS, mg/l 8,000 8,350 - RAS rate, % 400 495 445

(1) Sampling period: December 15-25, 2008(2) Average of January-December, 2008, except April-September 2007 for TKN(3) Average of two data points available

As shown in Table 3, the operating data based on HOD were substantially lower than the PDD in termsof flow and pollutant concentrations including COD, TSS and TKN. This indicated that the plant has been lightly loaded compared to the design conditions. On the other hand, during the SSD period, while the wastewater flow remained similar level to the HOD, the pollutant concentrations were substantially higher than for the HOD, and were essentially similar to those of PDD. It is interesting to note that the higher pollutant loading during the SSD period may be reflective of the heavier loadings from the Christmas holiday period.



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Current plant operation is intended to produce effluent that meetings existing limits governed by the NPDES permit. Key NPDES effluent limits are shown in Table 4. Current operation with membrane treatment produces an excellent effluent quality, with concentrations well below the permit limits for BOD and TSS. Under normal operation, complete nitrification occurs and daily ammonia nitrogen (NH3-N) concentrations are well below 1 mg/l and TN concentrations are well below 8 mg/l. As a result, current operation does not show any problem meeting the effluent limits.

Table 4Existing and Future NPDES Permit Limits on Key Parameters

Parameter Current Limit

BOD5, mg/l 30TSS, mg/l 30NH3-N, mg/l ?TN, mg/l 8

However, it was reported that there were occasional incidences of ammonia (NH3-N) and nitrate (NO3-N)spikes that caused concern because of potential to violate the TN limit specified in the NPDES permit. If a sufficient amount of process air is not supplied to meet a sudden surge in nitrogen loadings, temporarily high effluent ammonia could result. However, the nitrate spikes in this case are often associated with low loading conditions with constant-air flows usually over the weekend. When the low loadings are combined with normal process-air flow rates, it develops a condition of abnormally high DO in the bioreactors and membrane reactors and this, in turn, produces high nitrate and DO in the return activated sludge (RAS), which could result in poor denitrification in the anoxic zone and a corresponding nitrate spike in the effluent.

Another operational difficulty reported is associated with scum that at times becomes thick and develops intense foam. The foam is unsightly and contains high biosolids. Wasting significant amount of scum as a means of the foam control would result in an unintentional loss of biomass.

Sample analysis indicates that influent to the Kyrene WRF contains an elevated level of hydrogen sulfides that is not only the source of odor at the treatment plant but also a cause of deterioration of sewersupstream of the WRF. To alleviate these problems, the City of Tempe is considering application of Bioxide that contains a main component of nitrate. Although the nitrate will break down in the sewer to release oxygen, a fraction may remain in the sewage and end up as an influent to Kyrene WRF. This will undoubtedly increase the total nitrogen loading to the plant and the operation needs to adapt to handle the additional loading.

The issues of process performance, ammonia and nitrate peaking, scum handling and Bioxide impact areaddressed in detail with process modeling in the following sections.



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5.2 Process Evaluation with ModelingThe GPS-X model was used for the process evaluation. The model is capable of processing of equalization, anoxic and aerobic bioreactors, and membrane reactors in both steady state and dynamic environments.

The biological treatment process at the Kyrene WRF is basically a LE process with membranes used for solids separation from effluent. The equalized flow enters into the splitter box where it is mixed with return activated sludge. The mixed flow enters into six AX bioreactors and then into aerobic bioreactors, and finally into membrane reactors where solids separation takes place.

Key physical units of Kyrene WRF built into the model layout include the equalization (EQ) tanks, AXand OX bioreactors, membrane (MBR) reactors, RAS and waste activated sludge (WAS). To simplify the model and reduce model process time, each of the EQ, AX, OX and MBR units is represented as a single unit with total volume. The model layout is shown in Figure 10.

Figure 10GPS-X Model Layout

5.3 Model CalibrationModel calibration is a process that seeks to match the model prediction with known operational data by calibrating the stoichiochimetric, kinetic and other default parameters used in the model. The SSD data



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were used to generate necessary stoichiometric parameter values. The unit size obtained from the plant design and built drawings and plant operating data from SSD were also used. A few stoichiometric and kinetic parameter values were adjusted from the default values.

The predictions from the calibrated model were within 0-15% of the operating data, except 40% differential for NH3-N concentration in OX zone, as shown in Table 5. The sample from OX zone contains very high TN content, in a range of 750 mg/l, and a little time delay from the time of sampling and to the time of sample preservation or analysis will likely increase the NH3-N concentration from hydrolysis. The fact that the predicted effluent NH3-N concentration exactly matches with the operating data reinforces credibility of the prediction in the OX zone. Overall, the predictions from the calibrated model are considered good.

Table 5Comparison of Model Prediction with SSD

Parameter Sample DataSSD

ModelPrediction

PercentDifference, %

Total COD, mg/l AX zone OX zone MBR zone Effluent

8651922210350

28

867886581004028.4

+0.3-6.1-3.0+1.4

MLSS, mg/l AX zone OX zone MBR zone

820883489370

813081139518

-1.0-2.8+1.6

DO, mg/l AX zone OX zone MBR zone

0.140.632.87

0.130.693.31

-7.1+9.5+15.3

NH3-N, mg/l AX zone OX zone MBR zone Effluent

3.270.741.260.12

3.00.45

-0.12

-8.3-39.2

-0.0

TKN, mg/l AX zone OX zone MBR zone Effluent

6726757780.96

6946908000.90

+3.3+2.2+2.8-6.2



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The model calibration also included a mass balance of COD around the entire biological treatment system. The mass balance includes the COD as influent and its byproduct as waste solids generated in the form of waste activated sludge (WAS) and scum. The model was able to predict the quantity of scum,which is important since this can provide a useful tool for scum control. This is described in detail in alater section of this report. The total quantity of solids lost as a part of effluent was very small compared to WAS or scum and ignored in the mass balance. The mass balance data from the model calibration using the SSD is shown in Table 6.

Table 6Mass Balance of Influent COD and Waste Biosolids with SSD

Parameter ValueInfluent COD, lbs/day 21,900Waste Solids WAS (from operating data), lbs/day Scum (model predicted), lbs/day Total solids wasted, lbs/day Calculated Yield, lbs TSS/lb COD Scum % of total waste solids

2,3806,0908,4700.3972

SRT Predicted, days AX zone OX zone MBR Zone Total SRT

5.912.94.723.5

Calibration processes including data preparations, plot of calibration runs and evaluation of results are summarized in Appendix E.

5.4 Model Prediction of Current Operation with HODThe calibrated model was used to predict the effluent concentrations based on the HOD. The predictions from the calibrated model were within 0-2.3%, except for a 34.5% difference for effluent TKN, as shown in Table 7. A nitrogen balance in the effluent should show the TN as the sum of TKN and NO3-N,assuming NO2-N is negligent as usual for the effluent. A close examination of effluent nitrogen balance from Table 7 shows that TKN plus NO3-N N matches with TN for model prediction, but TKN plus NO3-N for HOD is substantially higher than TN. This indicates that TKN shown in the HOD may beinaccurate and, which could result the high percentage differential between the prediction and operating data. In general, the model prediction results are considered excellent.



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Table 7Comparison of Model Prediction with HOD

Parameter Plant Data(HOD)

ModelPrediction

Difference(%)

Effluent, mg/l COD 17.5 17.1 -2.3 NH3-N 0.12 0.12 0.0 NO3-N 3.45 3.41 -1.2 TKN 1.13 0.74 -34.5 TN 4.11 4.15 +1.0

The model calibration also included a mass balance on the biosolids generated from COD in thebiological treatment system. The mass balance with HOD data is shown in Table 8.

Table 8Mass Balance of Influent COD and Waste Biosolids with HOD

Parameter ValueInfluent COD, lbs/day 14,800Waste Solids WAS (from operating data), lbs/day Scum (model predicted), lbs/day Total solids wasted, lbs/day Calculated Yield, lbs TSS/lb COD Scum % of total waste solids

1,1004,4705,5700.3880

SRT Predicted, days 32.9

The model runs and an evaluation of results are summarized in Appendix F.

5.5 Effect of Dynamic Flow and LoadingsThe strength and advantage of modeling is the capability of analyzing many “what if” scenarios without having to modify plant operation. Since the real time hourly operational data with diurnal flow and loadvariations were not available, an analogous flow and load pattern with sinusoidal variations was selected to evaluate the stability and response of the operations to transient situations. The flow and load pattern was imposed on the HOD as summarized in Table 9.



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Table 9Dynamic Analysis Input Data

Parameter ValueInfluent Characteristics (HOD) Flow, mgd COD, mg/l TKN, mg/l NH3-N, mg/l

3.5748530.520.9

Sinusoidal Factor (1)

Flow amplitude scale, % Load amplitude scale, %

10020

(1) Fluctuation of flow or load by ±% as shown

Variations in flow and load, as shown in Table 9, represent a sinusoidal pattern with a daily peaking factor of 2.0 for flow, and a daily peaking factor of 0.2 for concentrations of COD, TSS and TKN. Asimulation time of 20 days was selected to show stability of the dynamic simulation. As evident from model simulation output data shown on Figure 11, the flow variations are mostly equalized through equalization process although the concentration variations are unchanged since they ride on top of flow variations.



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Figure 11Influent Flow and Load Variations after Equalization

A constant airflow rate was applied to reflect the current operation and the impact on DO concentrations in OX and MBR zones are shown on Figure 12. As shown on Figure 12, model predicted DO variations are within 1-2 mg/l for both OX and MBR zones.



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Figure 12Results of Sinusoidal Dynamic Simulation - DO variations

The impact of the flow and load variations on effluent nitrogen components, including TKN, NO3-N and NH3-N, is shown on Figure 13. As shown on Figure 13, model predicted effluent nitrogen components are bound within a relatively narrow range.



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Figure 13Results of Sinusoidal Dynamic Simulation – Effluent TKN, NO3-N and NH3-N

The impact of the flow and load variations on effluent COD, BOD and TSS is shown on Figure 14. As shown on Figure 14, model predicted effluent COD, BOD and TSS concentrations are within a very narrow range.

The results of sinusoidal dynamic simulation show that the equalization system functions quite well with a peaking factor of 2.0 within the same day but substantial DO variations occur with constant-air input.Unless there are substantial flow and load variations over the weekend, the effluent concentrations of conventional design parameters and nitrogen components are likely to stay within narrow range and to meet the permit limits.



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Figure 14Results of Sinusoidal Dynamic Simulation – Effluent COD, BOD and TSS

Model runs and evaluation of results are summarized in Appendix F.

5.6 Daily Operations Analysis Based on SSDThe calibrated model was used to simulate the continuous daily operating data obtained during the special sampling period. The key influent characteristics and other operating data extracted from the SSD are summarized in Table 10. These data were used to simulate continuous daily operation.

Table 10Continuous Daily Simulation Input Data

Date inDecember

2008

Influent Flow(mgd)

Influent COD(mg/l)

Influent TKN(mg/l)

RAS Flow(mgd)

WAS Flow(mgd)

15 3.81 584 50 16.91 0.01916 3.85 858 40 17.01 0.02217 3.97 723 40 16.26 0.02818 3.76 546 41 16.44 0.033



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Date inDecember

2008

Influent Flow(mgd)

Influent COD(mg/l)

Influent TKN(mg/l)

RAS Flow(mgd)

WAS Flow(mgd)

19 3.65 646 41 18.98 0.03920 3.68 626 42 18.93 0.03821 3.53 1280 40 18.94 0.03522 3.65 649 32 18.97 0.03323 3.76 620 53 18.92 0.03024 3.49 679 56 18.91 0.02625 3.08 682 43 19.07 0.024

Model simulation showed that the individual daily data responded reasonably well when daily waste biosolids quantity, including WAS and scum, was properly adjusted. The WAS quantity was directly from the operating data and scum quantity was the mass of biosolids that must be wasted to match with the MLSS concentrations in the reactors. In other words, the model was able to predict the daily waste biosolids quantity based on the daily operating data.

The effluent data for NO3-N was not available except for a single day. The most available effluent data from SSD was for effluent COD. Therefore, the predicted effluent COD were compared with the operating data as shown on Figure 15.



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Figure 15Results of Simulation with Continuous Daily Data – Effluent COD

Model runs with continuous daily data and evaluation of results are summarized in Appendix F.

6. Process Air Evaluation

6.1 Current OperationBased on the plant design information, the process air blowers summarized in Table 11.

Table 11Summary of Existing Air Blowers

Parameter Bioreactor MBRNumber of Air Blowers 3 4Pressure, psig 6.3 5.0Capacity each, scfm 10,200 3,600Total Capacity, scfm 30,600 14,400Firm Capacity (1 standby), scfm 20,400 10,800



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Currently, with constant speed motors and no inlet guide vanes in the air blowers, there is no positive means of controlling the airflow rates to pace with the loading variations. Therefore, airflow rates for the process air or cross airflows are nearly constant most of the time.

The operating data for airflow rates to the bioreactors or MBR reactors were not available at the time of this analysis. The values pertinent to oxygen transfer, such as oxygen transfer efficiency of diffusers, alpha factor, etc., were also estimated. Therefore, the airflow rate used in the modeling simply represents relative values to raise DO level in the reactors to match with operating data. If actual operating data on airflow rates becomes available, it is possible to estimate the oxygen transfer related coefficients using the model routine to re-calibrate the DO level to match with the operating data.

The validity of the model calibration and capability of sensitivity analysis is not diminished by using the relative airflow rate. The true airflow rate can be rectified when the operating data are available.

6.2 Sensitivity Analysis on AirflowsScum removal evaluations were performed on process airflow to OX of bioreactors, in a range of 2000-7000 scfm. As the increase in airflow increases DO, complete nitrification occurs. At the same time,high rate of RAS recycle to AX zone causes poor denitrification. As a result, TN increases to the permit limit and beyond with NO3-N as the main component of TN. This is demonstrated in Figure 16, which shows that an airflow rate of approximately 4000 scfm is the upper limit at which TN reaches to 8 mg/l.



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Figure 16Sensitivity Analysis on Process Air Flow Rate to OX Zone

As shown on Figure 16, effluent NO3-N was highly sensitive to the airflow rate as it affects DO level in the bioreactor in a relatively short time period. The effluent TN level, with NO3-N being the major component, reached the limit of 8 mg/l at the airflow level of about 4000 scfm. At airflow rates above this level, the effluent TN would exceed the permit limit due to complete nitrification that increases the nitrate to the high level and inability for effective denitrification in the presence of high DO and NO3-N in the recycle into the AX zone.

A sensitivity analysis was also performed on the cross-air flow to the MBR reactors in a range of 2000-7000 scfm. The results showed that the effect of cross-air flow was not as sensitive to effluent TN as the process air flow although the increase in cross-air flow gradually increased effluent TN, as shown on Figure 17.



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Figure 17Sensitivity Analysis on Cross-Air Flow Rate to MBR Reactors

The model simulation results are summarized in Appendix F

6.3 Long-Term, Diurnal Loading AnalysisAs a result of the sensitivity analysis, it was demonstrated that the air flow affects the effluent NO3-N and therefore effluent TN with high sensitivity by increasing DO level in the reactors. Since there is no air flow data available from the operating data, the airflow used in the analysis represents relative air flow. The sensitivity of airflow rate to the changes in effluent TN is still valid in terms of relative airflow rates.

In order to demonstrate long-term, diurnal model sensitivity of the effluent nitrate concentration to the airflow rates, diurnal patterns of influent flow and COD and TKN concentrations were assembled based on historical data analysis. Daily values were prepared in two tiers, weekdays with higher values and weekend days with lower values proportional to the historical operating data of 2008. It is interesting to note that the low two-day loadings occurred through Sunday-Monday period. Regarding diurnal pattern, the flow and load were varied at 0.2-day interval with a 50% peaking at the highest and lowest values since there was no operating data on the diurnal load variations.



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With the tong-term, diurnal loading data assembled as described above, model simulations were made on three different scenarios:

Constant air flow rates (both process air and cross air flow)

Variable air flow rates paced with loading rate variations

Constant air flow rates but increased AX zone by 33%

The simulations ran for 40 days to show stability of the model with sufficient time including weekends.

6.3.1 Scenario 1 – Diurnal Flow and Load with Constant Airflow RateThis scenario represents most likely the current operation, with near constant airflow regardless of the load variations. The results of model simulation show that the equalization tanks provide good flow equalization, the DO levels are elevated to very high level generally during the weekend and this causes the nitrate spike over the weekend, trending the cases seen in current operations.

The plot of DO in OX and MBR zones is shown on Figure 18. The plot of effluent NO3-Nconcentrations from the model output is shown on Figure 19. As shown on Figures 18 and 19, the daily diurnal fluctuations of effluent NO3-N are closely reflective of DO variations. The elevated DO concentrations coincide with the elevated nitrate levels during the weekend operation, replicating the trend of current operation.

The model input data and detailed results of the simulation are summarized in Appendix F.



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Figure 18DO Profile from Simulation of Diurnal Flow and Load with Constant Airflow



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Figure 19Effluent NO3-N Profile from Simulation of Diurnal Flow and Load with Constant Airflow

A summary of the analysis of diurnal variations with constant air input is as follows:

Constant air caused wide fluctuations of DO concentrations in general and very high DO concentrations during weekend

The elevated DO level caused very high NO3-N level during weekend

System response is from excessive DO, not from carbon limit

It confirms historical performance data that indicates nitrate spike trending with weekends

6.3.2 Scenario 2 – Diurnal Flow and Load with Airflow Rate Paced with Load VariationsThis scenario is based on an improved operational scheme in terms of airflow control by pacing the airflow rates with the influent flow and loading rates to mitigate the nitrate spike problem during low loading period. Airflow to both OX zone and MBR reactors had to be shaved during low loading period. The adjustment to the lower airflow rate had to be initiated prior to the beginning of the low flow and loading period and extended until after the period by 0.2-0.4 days.

After the airflow adjustment, the DO over the weekend was approximately equal to the average DO level during the weekdays’ operating period. In this case, the nitrate spike during the weekend disappeared. This confirms that the nitrate spike can be caused by excessive aeration during low loading period; this problem can be mitigated by properly controlling the airflow rate.



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The effluent NO3-N profile from the model output is shown on Figure 20. The model input data and detailed results of the simulation are summarized in Appendix F.

Figure 20Effluent NO3-N Profile from Simulation of Diurnal Flow and Load with Airflow Paced to

Load Variations

A summary of the analysis of diurnal variations with variable air input is as follows:

The air flow during the weekend had to be lowered such that the DO level in the reactors be equal to that of the weekdays

The nitrate peaking during the low flow and loading can be controlled by properly adjusting the airflows

The airflow adjustment should be made in advance and until after the low flow and load period is passed

6.3.3 Scenario 3 – Diurnal Flow and Load with Constant Airflow Rate, But Increased AX Zone by 33%

This is same as scenario 1, except that AX zone was increased by 33%, converting a portion of the OX into an AX zone. Since the current plant load is well below the design load, the shift of the OX to AX did



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not affect nitrification, but increased denitrification capacity substantially, as the model results show. With the increase in AX zone, the nitrate spike during the weekend did not show even with constant airflow because the increased AX zone provided sufficient capacity for de-oxygenation and denitrification during the low loading period.

The effluent NO3-N profile from the model output is shown on Figure 21. The DO profile from model output showed an elevated DO over the weekend, but the NO3-N peak disappeared with the increased AX zone as shown on Figure 21. The model input data and detailed results of the simulation are summarized in Appendix F.

Figure 21Effluent NO3-N Profile from Simulation of Diurnal Flow and Load with Constant Airflow

but Increased AX Zone by 33%

A summary of the analysis of diurnal variations with constant air input and increased AX zone by 33% isas follows:

The system is robust to relatively low levels of MLSS and low SRT

Nitrate spikes are reduced by using increased AX volume, which increases SRT for denitrification

6.4 Conclusions and RecommendationsIn the past, the plant has occasionally encountered nitrate spikes in the effluent, particularly over the weekend. The problem can be linked to over-aeration since the flow and loading were light during the



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weekend and airflow rates were nearly constant. A series of sensitivity analyses and diurnal analyseswere made with a process calibrated model to evaluate potential changes in operation that might alleviate the existing problem. Tentative conclusions are made as follows:

The airflow to the bioreactor had a greater impact on the effluent NO3-N spikes than the cross airflow to the MBR reactor. However, the cross airflow also has impact on the overall effluent nitrate spikes to some degree.

The simulation of diurnal flow and load with constant airflow demonstrated that the airflow caused the DO variations that are closely linked to effluent NO3-N variations, with distinctive nitrate spikes during the weekend.

The weekend nitrate spikes can be controlled by properly pacing the airflow to the wastewater flow and load. Controlling air flow can be achieved using VFDs, installing smaller blowers, or using blow-off valves to dissipate air.

The weekend nitrate spikes become less sensitive to constant airflow if the AX zone is increased.

7. Scum Removal Evaluation

7.1 Current OperationCurrently the scum is removed through the scum trough located between the bioreactors and MBR reactors. The operator removes the scum by tipping the lever handle of the scum trough at times as necessary. The timing and length of scum removal varies depending on the amount and intensity of the scum formation. Sometimes scum becomes so thick it turns into an intense foam that is unsightly. The effort to control foaming often results in removal of large quantities of scum and substantial loss of biosolids that may affect the performance in the bioreactors.

The scum concentrations and scum flow rates usually vary widely and therefore it is very difficult to monitor or record the scum quantity from daily operations. However, as shown in the model calibration and subsequent model simulations, process modeling can predict the mass quantity of scum from mass balance of influent COD and waste solids production. The scum prediction gives an insight into the optimum biomass concentration in the bioreactor in relation to the WAS and scum.

The plant has been in operation with high biomass concentration in the level of around 8,000 mg/l for MLSS or 10,000 mg/l for RAS. Based on historical operating data, it is concluded that the intense scum problem and occasional foaming could be due to the high biomass concentration with intense aeration.The foaming could be due to physical phenomenon or biological changes associated with operational condition. The biological change is rather difficult to verify and take corrective measures. However, physical change is easier to alter by changing the plant operation, such as maintaining lower biomass concentration or air supply. The model can provide a tool to predict the minimum biomass requirement and waste biomass including WAS and scum.

7.2 Sensitivity Analysis on Waste Biosolids for Existing SystemTo evaluate the relationship between the plant performance and biosolids in more detail, a sensitivity analysis was performed with the calibrated model. Three independent parameters for the sensitivity analysis were selected:



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Biosolids waste flow rate

Air flow rate to OX

Air flow rate to MBR

The limits of independent variables were set to cover the range such that the effluent NPDES permit limits be met. The sensitivity on the first issue, biosolids waste flow rate, is discussed in this section.The sensitivity on the next two issues are presented in following section since they are related to aeration issues.

Sensitivity analysis on biosolids waste rate showed that upper limit on the biosolids waste rate was 0.296 mgd so that the effluent TN stays within the permit limit of 8.0 mg/l. If it exceeded this limit, the effluent TN was over the permit limit because bisolids concentration was too low to achieve sufficient denitrification and effluent NO3-N concentration is high. The major component of the TN is NO3-N. All other effluent parameters are well below the permit limits. The results of model output are shown on Figure 20.

Figure 22Sensitivity analysis of Biosolids Waste Rate with SSD

The wasted biosolids consists of WAS and scum, as predicted by the model. Because scum concentrations cannot be known in the model, it was assumed that they are equal to WAS concentrations.Therefore, the WAS and scum concentrations are expressed as mass quantities rather than flow rates. The results of the sensitivity analyses at three critical waste flow rates are summarized in Table 12.

@ WAS+Scum=0.296 mgd TN = 8 mg/lNO3-N = 7.2 mg/lNH3-N = 0.14 mg/lTKN = 0.8 mg/l



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Table 12Summary Sensitivity Analysis on Biosolids Waste Rate

ParameterWAS + Scum Waste Flow Rate, mgd

0.1 0.2 0.296Effluent Concentration, mg/l TN NO3-N NH3-N TKN COD BOD TSS

5.03.8

0.141.2

29.01.27.1

6.35.4

0.120.9

25.61.03.9

8.07.20.140.824.40.92.7

Biosolids Waste, lbs/day WAS Scum Total

08,5208,520

4,4284,7969,224

6,2913,4089,699

MLSS Concentration, mg/l AX zone MBR zone (RAS)

8,65010,210

4,6505,630

3,3203,930

SRT, days AX zone OX zone MBR zone Total

6.313.85.0

25.1

3.26.92.5

12.6

2.24.71.78.6

As shown in Table 12, if the waste is only from scum with a negligible amount from WAS, the predicted WAS (or RAS) concentration is 10,210 mg/l. The RAS concentration can be as low as 3,930 mg/l with majority of waste coming as a form of WAS, but predicted scum quantity is not negligible. In reality, there may not be as much scum generated at this low RAS concentration and WAS must be increased to operate at this RAS level. While the results indicate a wide operable range of RAS concentration, WASis the true controlling parameter to operate the RAS at proper range. Knowing that the RAS can be substantially lower than the current operation to meet the effluent limits, the operation at trial basis can be gradually lower RAS 7,000, 6,000 or 5,000 level to experiment with scum quality or quantity as well as effluent quality.

The model simulation results are summarized in Appendix F.

7.3 Sensitivity Analysis on Waste Biosolids with Increased AX Zone by 33%This analysis is same as the sensitivity analysis described under Section 6.2, except the AX zone was increased by 33% by converting a portion of the OX zone into AX zone. Since the current plant load is



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well below the design load, the shift of the OX to AX did not affect nitrification, but increased denitrification capacity substantially as the model results show.

As a result, the range of operation in terms of waste biosolids or MLSS level was substantially increased.Now the waste flow rate for WAS and scum stretches up to 0.7 mgd or MLSS level as low as 1600 mg/l to have the effluent TN within 8 mg/l. The major component of the TN is NO3-N. All other effluent parameters are well below the permit limits. The results of model output are shown on Figure 23.

Figure 23Summary Sensitivity Analysis on Biosolids Waste rate with Increased AX Zone by 33%

The results of sensitivity analysis at several critical waste flow rates are summarized in Table 13.



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Table 13Summary Sensitivity Analysis on Biosolids Waste Rate with Increased AX Zone by 33%

ParameterWAS + Scum Waste Flow Rate, mgd

0.1 0.2 0.4 0.6 0.7Effluent Concentration, mg/l TN NO3-N NH3-N TKN COD BOD TSS

5.44.2

0.161.2

28.91.27.1

5.54.6

0.140.9

25.61.03.9

6.15.3

0.190.8

23.70.82.1

7.36.5

0.290.8

23.00.71.5

8.07.1

0.370.9

22.80.71.3

Biosolids Waste, lbs/day WAS Scum Total

08,5208,520

4,4004,8209,220

7,4702,620

10,090

8,7801,840

10,620

9,2101,610

10,820MLSS Concentration, mg/l AX zone MBR zone (RAS)

8,42010,120

4,6705,550

2,5703,020

1,8202,130

1,6001,860

SRT, days AX zone OX zone MBR zone Total

8.411.75.0

25.1

4.25.92.5

12.6

2.13.01.36.4

1.52.00.84.3

1.21.70.73.6

@ WAS+Scum=0.7 mgd TN = 8 mg/lNO3-N = 7.1 mg/lNH3-N = 0.37 mg/lTKN = 0.9 mg/l

As shown in Table 13, if the waste is only from scum with negligible amount from WAS, the predicted WAS (or RAS) concentration is 10,120 mg/l. The RAS concentration can be as low as 1860 mg/l with the majority of waste coming as a form of WAS. The predicted scum quantity is small. While the results indicate a wide operable range of RAS concentration, WAS is the true controlling parameter to operate the RAS at proper range. Knowing that the RAS can be substantially lower than the current operation to meet the effluent limits, the operators may gradually lower RAS 7000, 6000 or 5000 or even lower level to experiment with scum quality or quantity as well as effluent quality.

The model simulation results are summarized in Appendix F.



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7.4 Conclusions and RecommendationsThe cause of operational problems with scum and occasional foaming was evaluated by reviewing the historical operating data. A series of sensitivity analyses were made with a calibrated model to evaluate potential changes in operation that might alleviate the existing problem. Preliminary conclusions are made as follows:

Historically, the plant has been operated with very high MLSS concentrations and intense aeration. The high biomass concentrations with intense aeration may be the root cause of build-upof thick scum and foaming.

A sensitivity analysis of the existing system showed that the MLSS concentrations could be in a range of 3000-4000 to meet the effluent permit limit. If operated with high MLSS, most waste biosolids were in the form of scum, but the scum became a very small portion of the waste if operated at low MLSS.

A sensitivity analysis of a modified system, with an increased AX zone created by converting a portion of the existing OX zone, showed that the MLSS level could be about 2000 mg/l or lower to meet the effluent permit limit. The larger AX zone increases the denitrification capacity.However, an increase in the AX zone may be feasible only for the currently under-loaded condition.

The effect of MLSS changes on scum and foaming can be field tested by gradually reducing the MLSS concentration in step into several different levels such as 7000, 6000 and 5000 mg/l. The actual operation is subject to sudden fluctuations in flow and loadings and other environmental changes that provide the conditions not as favorable as model simulation. Therefore, operating at the lowest MLSS concentration found from modeling may not be practical.

8. Existing Plant Water System EvaluationThe existing plant water system is shown in Figure 24.



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Figure 24Existing Plant Water System

Operators at the Kyrene WRF have reported periods of low or no flow from the plant water system, which affects the plant’s ability to operate spray systems and other equipment. The hydropneumatic tank that supplies water to the system can drain completely when the permeate pumps are off (typically early mornings or weekends). The following sections describe alternatives that could address this issue.

8.1 Increased Influent FlowsIncreasing influent flows to Kyrene WRF is likely to increase the amount of water available for filling the plant water system’s hydropneumatic tank. However, this alternative is not currently feasible because the City of Tempe must send a minimum amount of flow to the regional 91st Avenue Wastewater Treatment Plant (WWTP). The City of Tempe does not currently generate enough wastewater to both increase flows to Kyrene WRF and meeting its commitment to the 91st Avenue WWTP.

8.2 Increased Use of Equalization BasisThe Kyrene WRF has existing equalization facilities on site, which could be used to store additional flows during high flow periods and allow the plant to operate closer to a steady-state condition. A preliminary analysis showed that average weekday flows of approximately 4.9 MGD could be equalized with average weekend flows of approximately 4 MGD, with little impact on the city’s commitments to the 91st Avenue WWTP. The equalized flows are shown in Figure 25.

TO PLANT WATER

PSVsFROM PERMEATE PUMPS (VFD)24”

8”

(85-90 PSI)

PLANT WATER PUMPS500 GPM (TYP)H=50 PSI

(85-90 PSI)

HYDROPNEUMATICTANK

TO SRP,GOLF COURSE, AND STORM DRAIN

20,000 gal



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Figure 25Existing Plant Water System

8.3 Use of Potable WaterIf sufficient plant water is not provided by the facility’s effluent flows, potable water may be used to augment the supply. This alternative would require additional coordination to ensure that sufficient water pressure is available. A proposed configuration is shown in Figure 26.

0

5

10

15

20

25

8/31

/200

8

9/1/

2008

9/2/

2008

9/3/

2008

9/4/

2008

9/5/

2008

9/6/

2008

9/7/

2008

9/8/

2008

9/9/

2008

FLO

W (M

GD

)

DATE

PRIMARY INFLUENTEQUALIZATION DISCHARGETP01 MSALT. 2 - INFLUENT WITH EQ

Results from Preliminary Evaluation Model Using EQ:-Weekday Inf luent Flow: ~4.9 MGD-Weekend Inf luent Flow:~4.0 MGD No Impact to Minimum Flows

-At TP01 M.S. and 91st Avenue WWTP



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Figure 26Plant Water System Modified to Utilize Potable Water

8.4 Conclusions and RecommendationsIncreased use of the existing equalization basins should be evaluated further. If this alternative is not viable, the city should proceed with augmenting the plant water system with potable water.

DRAIN

6” WV= 6 ft/sec

ULTRASONIC OR LEVEL SWITCH 500 GPM (TYP)

H=50 PSI

1000 GAL(AT GRADE)

M

TO PLANT WATER

PSV’SFROM PERMEATE PUMPS (VFD)24”

8”

(85-90 PSI)

PLANT WATER PUMPS500 GPM (TYP)H=50 PSI

(85-90 PSI)

TO SRP,GOLF COURSE,

AND STORM DRAIN

20,000 gal

VERIFY PRESSURE AT 6” WATER PIPELINE

kyrene water reclamation plant process overview project no

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