E-Andrew Sindt Creative Component S11.pdf

Prediction of nutrient removal with sequencing batch reactorprocess at Grundy Center, Iowa using a process simulatorbyAndrew Donald David SindtA creative component submitted to the graduate faculty in partialfulfillment of the requirements for the degree ofMASTER OF SCIENCEMajor: Civil EngineeringProgram of Study Committee:Say Kee Ong, Major ProfessorThomas LoynachanShihwu SungIowa State UniversityAmes, Iowa2011Copyright © Andrew Donald David Sindt, 2011. All rights reserved.

AbstractIn an effort to minimize negative environmental effects caused by point source nutrient loadings, newnumeric limits on effluent nitrogen (N) and phosphorus (P) concentrations are under review by severalstates. Current efforts for developing and implementing efficient, reliable, and cost effective treatmenttechnologies for biological nutrient removal (BNR) have moved towards the use of models to predictprocess operations and effluent quality. This paper investigates the use of a process simulator to predicteffluent concentrations of a sequencing batch reactor (SBR) process. The investigation includes asensitivity analysis, calibration of two different SBR modes, analysis of COD fractions effects on effluentconcentrations, and analysis of cycle phase duration variation effects on total nitrogen (TN) and totalphosphorus (TP) removal efficiencies. Sensitive simulator parameters were adjusted to reduce variationof predicted effluent concentrations to observed values. Simulator was successfully calibrated for theBNR-S1 mode, however, efforts were unsuccessful in reducing deviation of effluent TN and NO 3 - -Nconcentrations to within 20% of observed values. COD fractions were found to have a greater influenceon effluent concentrations of calibrated runs than default runs. Cycle phase duration variation analysisfound improved nutrient removal efficiencies for increasing anaerobic and anoxic phase lengths. Theoptimum cycle sequence of anaerobic/anoxic/oxic was found to be 140/90/40 minutes, respectively.

Table of ContentsList of Figures ..................................................................................................................................... 5List of Tables ...................................................................................................................................... 8List of Equations ................................................................................................................................. 10Chapter 1: Introduction ..................................................................................................................... 111.1 Importance of Point Source Reductions ............................................................................ 111.2 Modeling Biological Nutrient Removal (BNR).................................................................... 111.3 Report Objectives .............................................................................................................. 12Chapter 2: Literature Review ............................................................................................................. 132.1 Nutrient Removal in Wastewater Treatment Plants ......................................................... 132.1.1 Nitrogen Removal ....................................................................................................... 132.1.2 Phosphorus Removal................................................................................................... 132.2 Biological Nutrient Removal (BNR) ....................................................................................... 142.2.1 Nitrogen Removal ....................................................................................................... 142.2.1.1 Biomass Storage and Decay .............................................................................. 152.2.1.2 Classical Predenitrification ................................................................................ 162.2.1.3 Simultaneous Nitrification and Denitrification ................................................. 162.2.1.4 Barnard Process ................................................................................................. 172.2.1.5 Sequencing Batch Reactor (SBR) ....................................................................... 182.2.2 Phosphorus Removal................................................................................................... 192.2.2.1 EBPR with Classical Predenitrification ............................................................... 202.2.2.2 Bardenpho Process ............................................................................................ 212.2.2.3 EBPR in SBRs ...................................................................................................... 212.3 Previous SBR Work ................................................................................................................ 222.3.1 General SBR Process for BNR ...................................................................................... 221

2.3.2 SBR Cycle Times ........................................................................................................... 232.3.3 SBR Controllers for Automatic Cycle Time Adjustment .............................................. 242.4 Wastewater Treatment Process Modeling ........................................................................... 242.4.1 International Water Association (IWA) Models .......................................................... 242.4.2 EBPR Models (summarized from Melcer et al., 2003) ................................................ 252.4.3 BioWin Wastewater Process Simulator ...................................................................... 252.4.4 Simulator Data Requirements ..................................................................................... 252.4.5 Dynamic Modeling ...................................................................................................... 262.4.6 Simulator Parameters ................................................................................................. 262.4.7 Previous Work in Modeling SBRs for BNR ................................................................... 262.5 Summary of Previous Work .................................................................................................. 27Chapter 3: Methods ........................................................................................................................... 283.1 Wastewater Treatment Plant Description ............................................................................ 293.2 SBR Modes ........................................................................................................................... 303.3 Data Interpretation ............................................................................................................... 313.4 BioWin Simulator Setup ........................................................................................................ 333.4.1 SBR Dimensions ........................................................................................................... 343.4.2 SBR Operation ............................................................................................................. 353.4.3 SBR Cycle Sequences in BioWin .................................................................................. 353.5 Sensitivity Analysis ................................................................................................................ 363.6 Simulator Calibration ............................................................................................................ 383.6.1 Calibration of Regular mode ....................................................................................... 393.6.2 Calibration of BNR-S1 mode........................................................................................ 403.7 Analysis of BNR-S2 mode ...................................................................................................... 403.8 Effect of Width ...................................................................................................................... 402

3.9 Effect of COD Fractions ......................................................................................................... 413.10 BNR-S1 Mode Cycle Optimization ..................................................................................... 41Chapter 4: Results and Discussion ..................................................................................................... 424.1 COD Fractions ....................................................................................................................... 424.1.1 Regular Mode COD Fractions ...................................................................................... 424.1.2 BNR-S1 Mode COD Fractions ...................................................................................... 424.1.3 COD Fractions Discussion ............................................................................................ 434.2 Sensitivity Analysis ................................................................................................................ 444.2.1 Regular Mode Sensitivity Analysis .............................................................................. 444.2.1.1 Ordinary Heterotrophic Organism (OHO) Yield................................................. 444.2.1.2 Maximum Specific Growth Rate Discussion ...................................................... 464.2.1.3 Ammonia Oxidizing Bacteria (AOB) maximum specific growth rate ................. 464.2.1.4 Nitrite Oxidizing Bacteria (NOB) maximum specific growth rate ...................... 484.2.1.5 Maximum Vesilind Settling Velocity Discussion ................................................ 504.2.1.6 Maximum Vesilind Settling Velocity .................................................................. 514.2.2 BNR-S1 Sensitivity Analysis ......................................................................................... 534.2.2.1 Substrate (NH + 4 ) half-saturation concentration ................................................ 534.2.2.2 Aerobic P/PHA uptake and P/Ac release ratio .................................................. 544.3 Simulator Calibration ............................................................................................................ 544.3.1 Regular Mode Calibration ........................................................................................... 544.3.2 BNR-S1 Mode Calibration............................................................................................ 564.3.3 Evaluation of BNR-S2 with BNR-S1 Calibrated Parameters ........................................ 614.4 Effect of SBR width................................................................................................................ 624.4.1 Regular Mode .............................................................................................................. 634.4.2 BNR-S1 Mode .............................................................................................................. 633

4.5 Effects of COD Fractions ....................................................................................................... 644.5.1 Regular Mode .............................................................................................................. 644.5.2 BNR-S1 Mode .............................................................................................................. 654.6 Variation of Cycle Phase Duration ........................................................................................ 654.6.1 Anaerobic-Oxic Phase Variation .................................................................................. 664.6.2 Anoxic-Oxic Phase Variation ....................................................................................... 66Chapter 5: Conclusion ........................................................................................................................ 685.1 Summary .............................................................................................................................. 685.2 Future Work .......................................................................................................................... 69Chapter 6: References ........................................................................................................................ 70Appendix I: BioWin Simulation Setup ................................................................................................ 74Appendix II: Selected BioWin Simulation Data .................................................................................. 84Appendix III: Monthly Mechanical Monitoring Reports .................................................................... 94Appendix IV: BioWin User Manual Process Model Formulation ....................................................... 105Acknowledgements ............................................................................................................................ 1374

List of FiguresFigure 2.1- Biomass storage and decay ............................................................................................. 15Figure 2.2- Classical predenitrification............................................................................................... 16Figure 2.3- Simultaneous nitrification and denitrification ................................................................. 17Figure 2.4- Bernard process schematic .............................................................................................. 18Figure 2.5- Typical SBR cycle for 90% N removal ............................................................................... 19Figure 2.6- Required components for P removal ............................................................................... 20Figure 2.7- Phoredox process ............................................................................................................. 21Figure 2.8- Bardenpho process .......................................................................................................... 21Figure 2.9- Characteristic EBPR behavior in an anaerobic/aerobic SBR ............................................ 22Figure 2.10- COD profile with BioWin state variables ....................................................................... 26Figure 3.1- Flowchart of activities for BioWin Simulations ................................................................ 28Figure 3.2- Grundy Center wastewater treatment plant schematic.................................................. 29Figure 3.3- BioWin screen system layout ........................................................................................... 34Figure 3.4- SBR dimensions ................................................................................................................ 35Figure 3.5- BioWin SBR operation input screen for BNR-S1 mode .................................................... 36Figure 4.1- Influent specifier COD fractions page for regular mode .................................................. 42Figure 4.2- Influent specifier COD fractions page for BNR-S1 mode ................................................. 43Figure 4.3- Heterotrophic bacteria metabolism ................................................................................ 44Figure 4.4- OHO yield sensitivity plot for regular mode .................................................................... 45Figure 4.5- Percent error plot for OHO yield for regular mode ......................................................... 46Figure 4.6- AOB maximum specific growth rate sensitivity plot for regular mode ........................... 47Figure 4.7- AOB maximum specific growth rate percent error plot for regular mode ...................... 48Figure 4.8 -NOB maximum specific growth rate sensitivity plot for regular mode ........................... 49Figure 4.9 -NOB maximum specific growth rate sensitivity plot for regular mode (NO - 2 ) ................ 495

Figure 4.10- NOB maximum specific growth rater percent error plot for regular mode .................. 50Figure 4.11- Demonstration of layered method in modified Vesilind settler model ........................ 51Figure 4.12- Maximum Vesilind settling velocity sensitivity plot for regular mode .......................... 52Figure 4.13- Maximum Vesilind settling velocity percent error plot for regular mode ..................... 52Figure 4.14- Progression of regular mode calibration ....................................................................... 55Figure 4.15- BNR-S1 mode calibration progression ........................................................................... 58Figure 4.16- Effluent TSS calibration for BNR-S1 mode ..................................................................... 59Figure 4.17- Effluent NH 3 -N concentration calibration for BNR-S1 mode ......................................... 59Figure 4.18- Effluent TP concentration calibration for BNR-S1 mode ............................................... 60Figure 4.19- Flow distribution in single tank SBR............................................................................... 63Figure 4.20- Percent removal plot for BNR-S1 mode for anaerobic-oxic period length variations ... 66Figure 4.21- Percent removal plot for BNR-S1 mode for anoxic-oxic period length variations ........ 67Figure AI.1- Influent specifier input page for regular mode .............................................................. 75Figure AI.2- Influent specifier fraction estimate page for regular mode ........................................... 76Figure AI.3- Influent specifier COD fractions page for regular mode ................................................ 76Figure AI.4- Influent specifier input page for BNR-S1 mode .............................................................. 77Figure AI.5- Influent specifier fraction estimation page for BNR-S1 mode ....................................... 78Figure AI.6- Influent specifier COD fractions page for BNR-S1 mode ................................................ 78Figure AI.7- SBR specifications screen ............................................................................................... 79Figure AI.8- Influent itinerary for regular mode ................................................................................ 79Figure AI.9- SBR operation page for regular mode ............................................................................ 80Figure AI.10- D.O. set point itinerary for regular mode ..................................................................... 80Figure AI.11- Underflow itinerary for regular mode .......................................................................... 81Figure AI.12- Influent itinerary for BNR-S1 mode .............................................................................. 81Figure AI.13- SBR operation page for BNR-S1 .................................................................................... 826

Figure AI.14- D.O. set point itinerary for BNR-S1 mode .................................................................... 82Figure AI.15- Underflow itinerary for BNR-S1 mode ......................................................................... 83Figure AII.1- Regular mode calibration progression for combination 1 ............................................ 89Figure AII.2- Regular mode calibration progression for combination 2 ............................................ 90Figure AII.3- Regular mode calibration progression for combination 3 ............................................ 91Figure AII.4- Regular mode calibration progression for best run in Table AII.3 ................................ 92Figure AII.5- BNR-S1 mode calibration progression ........................................................................... 937

List of TablesTable 1.1- Commercial wastewater simulators ................................................................................. 12Table 2.1- Optimum parameters for nitrification .............................................................................. 14Table 3.1- Regular and biological nutrient removal modes ............................................................... 30Table 3.2- Wastewater characteristics .............................................................................................. 32Table 3.3- Wastewater treatment performance for different modes ............................................... 32Table 3.4- Influent specifier input values regular mode .................................................................... 33Table 3.5- Sensitivity parameters for regular mode .......................................................................... 37Table 3.6- Simulator parameters for regular mode calibration ......................................................... 39Table 3.7- Simulator parameters for BNR-S1 mode calibration ........................................................ 40Table 4.1- Regular mode effluent quality from default simulator parameter settings ..................... 54Table 4.2- Calibrated simulator parameters for regular mode .......................................................... 56Table 4.3- Regular mode effluent quality for calibrated simulator parameter settings .................... 56Table 4.4- BNR-S1 mode effluent quality for default simulator parameter settings ......................... 57Table 4.5- BNR-S1 mode effluent quality with calibrated regular mode simulator parameters ....... 57Table 4.6- Calibrated simulator parameters for BNR-S1 mode ......................................................... 60Table 4.7- BNR-S1 mode effluent quality for calibrated simulator parameters ................................ 61Table 4.8- BNR-S2(20.1 o C) mode default effluent quality ................................................................. 61Table 4.9- BNR-S2 (20.1 o C) mode calibrated effluent quality ............................................................ 61Table 4.10- BNR-S2 (15.7 o C) mode default effluent quality .............................................................. 62Table 4.11- BNR-S2 (15.7 o C) mode calibrated effluent quality.......................................................... 62Table 4.12- Width adjustment effects on regular mode effluent quality .......................................... 63Table 4.13- Width adjustment effects on BNR-S1 mode effluent quality ......................................... 64Table 4.14- Influent wastewater COD fractions effects on regular mode effluent quality ............... 64Table 4.15- Influent wastewater COD fractions effects on BNR-S1 mode effluent quality ............... 658

Table AI.1- Influent specifier input values regular mode................................................................... 75Table AI.2- Influent specifier input values BNR-S1 mode .................................................................. 77Table AII.1- Percent deviation from effluent value at default for + 60% adjustment of simulatorparameter for regular mode ................................................................................................. 85Table AII.2- Percent deviation from effluent value at default for - 60% adjustment of simulatorparameter for regular mode ................................................................................................. 87Table AII.3- Combination 1 ................................................................................................................ 89Table AII.4- Combination 2 ................................................................................................................ 90Table AII.5- Combination 3 ................................................................................................................ 91Table AII.6- Expansion on best run from Table AII.3 .......................................................................... 92Table AII.7- BNR-S1 calibration progression ...................................................................................... 939

List of EquationsEquation 1: Ammonium to nitrite ...................................................................................................... 14Equation 2: Nitrite to nitrate ............................................................................................................. 14Equation 3: Overall nitrification reaction .......................................................................................... 14Equation 4: Denitrification reaction .................................................................................................. 14Equation 5: Percent deviation in effluent parameter ........................................................................ 36Equation 6: Sum absolute value of percent deviation from observed effluent parameter .............. 39Equation 7: Vesilind settling velocity in a given layer ........................................................................ 51Equation 8: Substrate utilization rate ............................................................................................... 5310

Chapter 1: Introduction1.1 Importance of Point Source ReductionsPoint and non-point source nutrient loadings on streams and waterways have recently been the focus oflawmakers and regulators due to negative environmental effects caused by over loadings. Excessiveamounts of nitrogen (N) and phosphorus (P) result in nuisance algae blooms, increased biochemicaloxygen demand, and significantly contribute to eutrophication in closed water systems. Iowa’s waters,for example, generally contain 2-10 times the levels of N and P as considered appropriate for Midweststreams (Iowa- DNR, 2011). Non-point source pollution is estimated to account for 92 and 80 percent ofIowa’s N and P stream loads, respectively (Libra et al., 2004). Even though the majority of excessnutrients present in streams is the result of non-point source pollution (e.g., agriculture runoff), animportant quantity is derived from point source dischargers such as wastewater treatment plants.Typical municipal sewage in the United States has 400 mg/L COD and 6 to 10 mg/L phosphorusconcentrations. Assuming algal composition can be described by the stoichiometric equationC 106 H 263 O 110 N 16 P, the discharge of 6 mg/L phosphorus could potentially result in COD production by algalgrowth equivalent to 828 mg/L, more than double the COD of typical raw wastewater (Randall et al.1992). Although much of the algal biomass will slowly biodegrade, organics accumulate in the bottomsediments where long term biodegradation occurs creating a long term oxygen demand.In order to control the growth of photosynthetic life in aquatic environments, nutrient limitingconditions must be observed. In natural environments, nutrient limiting conditions change withseasonal changes in eutrophic systems. Due to the relatively large quantities of N and P required forbiomass growth, it is likely that either N or P will be limiting growth factor (Randall et al., 1992). Ingeneral, wet weather patterns cause phosphorus limiting conditions as a result of excessive nitrogenrunoff from non-point sources. Warm weather can cause assimilation and eventually denitrification toproduce nitrogen limiting conditions. Any introduction of a limiting nutrient, no matter how small, intoone of the above environments results in the growth of more biomass (Randall et al., 1992).In an effort to minimize negative environmental effects, new numeric limits on effluent N and Pconcentrations are under review by several states, most notably Florida (US EPA, 2011). The need forreliable, cost effective upgrades to existing WWTPs for nutrient removal is increasing as future effluentnutrient regulations become more likely. While better management practice programs have beendeveloped for non-point source pollution, control over point source pollution is more readily achievablethrough the National Pollution Discharge Elimination System (NPDES) permit requirements.In order to meet new stringent standards for wastewater effluent nutrient concentrations, focus onefficient, reliable, and cost effective treatment technologies needs to be considered.1.2 Modeling Biological Nutrient Removal (BNR)Current efforts for developing and implementing efficient, reliable, and cost effective treatmenttechnologies for BNR have moved towards the use of models to predict process operations and effluentquality. The main benefit of using a model is the potential cost savings. A model that is correctly11

calibrated can provide data that would otherwise need to be collected from costly pilot-scale or fullscalestudies. Models provide an abundance of information in a relatively short period of time. Variousoperating conditions can be quickly analyzed to observe potential consequences.These benefits have led to the development of a number of commercial wastewater simulators (Table1.0). One drawback to using wastewater simulators is the extensive data input they require.Table 1.1- Commercial wastewater simulators *Simulator Supplier Location WebsiteASIMEAWAG (Swiss Federal Institute forEnvironmental Science & Technology)Switzerland www.eawag.chBioWin EnviroSim Associates Limited Canada www.envirosim.comEFOR DHI, Inc Denmark www.dhigroup.comGPS-X Hydromantis, Inc. Canada www.hydromantis.comSIMBA IFAK-System GmbH Germany www.ifak-system.comSTOAT WRc Group United Kingdom www.wrcplc.co.ukWEST Hemmis N.V. Belgium www.hemmis.com* adapted from Melcer et al., 20031.3 Report ObjectivesThe primary objective of the following report was to analyze the ability of the BioWin wastewatersimulator to predict effluent quality at the wastewater treatment facility of the City of Grundy Center,Iowa. Predicted effluent concentrations were compared to observed data collected by Ersu et al.(2008). Objectives include:• Characterize the City of Grundy Center wastewater flow COD fractions using the influentspecifier (EnviroSim, 2010)• Sensitivity analysis to determine sensitive simulator parameters• Simulator calibration using data from sensitivity analysis to minimize deviation from observeddata• Analysis of using calibrated simulator parameters obtained with an SBR with a certain cyclesequence on an SBR with a different cycle sequence• Analysis of cycle phase duration length adjustment effects on effluent TN and TP• Analysis of COD fractioning effects on effluent quality12

Chapter 2: Literature Review2.1 Nutrient Removal in Wastewater Treatment Plants2.1.1 Nitrogen RemovalInfluent nitrogen concentrations, predominantly in the forms of ammonia (NH 3 ), ammonium (NH 4 + ), andorganic nitrogen are removed via two mechanisms. The first mechanism is the natural physiologicalnitrogen requirement for cell metabolism and growth. The second is the combination of nitrification,conversion of ammonium to nitrite and nitrate, and denitrification, the conversion of nitrate to nitrogengas. Nitrification and denitrification will be discussed further in detail in the section Biological NutrientRemoval.2.1.2 Phosphorus RemovalPhosphorus removal from wastewater streams relies on converting soluble phosphorus species toparticulate form, then removing the particulate through settling, conventional filters, and/or membraneprocesses. Orthophosphate, soluble in nature, is the most dominant phosphorus species in wastewaterand is used for biological metabolism. Additionally, other forms of phosphorus are converted toorthophosphates which are also available to support biological growth (Neethling, 2008). Thus, solublephosphorus is taken up by the biomass and held in particulate form amounting to 1-2% of the totalsuspended solids mass in the mixed liquor (Lesjean et al., 2003). This is also reflected by thestoichiometric formula for biomass given as C 5 H 7 O 2 NP 0.1 (Rittman and McCarty, 2001).Phosphorus uptake into biomass can be further enhanced with appropriate conditions which allow forenhanced biological phosphorus removal (EBPR) or luxury phosphorus uptake. EBPR is defined asphosphorus uptake by bacteria that exceeds the 2.3% phosphorus by weight typical of conventionalactivated sludge type biomass (Randall et al., 1997). EBPR depends on the proliferation of phosphorusaccumulating organisms (PAOs) to increase the phosphorus concentration up to about 7% total solids.The required environment for PAOs to outcompete other microorganisms consists of alternating theoperating conditions from anaerobic to aerobic.Phosphorus can also be removed by physicochemical fixation of phosphate through precipitation andadsorption. This can be achieved naturally (with appropriate pH and presence of cations) or artificiallythrough dosing chemical coagulants such as iron or alum salts. Although chemical phosphorus removalcurrently holds an advantage over EBPR in terms of better control, many drawbacks do exist. Thesedrawbacks include increases in sludge production (up to 25%), chemical costs, and effluent salinity aswell as potential adverse conditions for biological nitrification (low alkalinity and pH). Due to thesenegative aspects associated with chemical phosphorus removal, EBPR is considered a preferredtreatment technology for phosphorus removal.13

2.2 Biological Nutrient RemovalBiological nutrient removal (BNR) process for N and P removal involves three major microorganismgroups: autotrophic nitrifying organisms, heterotrophic denitrifying organisms, and phosphorusaccumulating organisms.2.2.1 Nitrogen RemovalAutotrophic nitrifying organisms carry out nitrification, the process of oxidizing ammonium to nitrite(equation 1) and then to nitrate (equation 2) under aerobic conditions with the first step (equation 1) asthe limiting step. The two major genera of microorganisms that accomplish this are Nitrosomonas tooxidize ammonium to nitrite (equation 1) and Nitrobacter to convert nitrite to nitrate (equation 2).2NH 4 + + 3O 2 → 2NO 2 - + 4H + +2H 2 O (rate limiting) [1]2NO 2 - + O 2 → 2NO 3-[2]NH 4 + + 2O 2 → NO 3 - + H 2 O + 2H + (overall reaction) [3]Important factors that impact nitrification kinetics include temperature, pH, dissolved oxygen (DO)concentration, and solids retention time (SRT). Optimum parameters for nitrification are given in Table2.1. Nitrification rates reduce with lower temperatures. At normal pH, ammonia (NH 3 ) is present insolution in the form of ammonium cation (NH 4 + ) (Davis and Cornwell, 2008).Table 2.1 *Optimum parameters for nitrificationTemperature 25-35°CpH 7.5-9DO> 2.0 mg/LSRT> 10 days* (Process Design Manual for Nitrogen Control, 1993)Heterotrophic denitrifiers use organic carbon substrates as electron donors under anoxic conditions fornitrate reduction to nitrogen gas (equation 4). Energy required by the denitrifying bacteria to convertnitrate to nitrogen gas is provided by organic substrates located either within or outside of the cell(Davis and Cornwell, 2008).2NO 3 - + organic carbon ↔ N 2 (gas) + CO 2 + H 2 O [4]Achieving near complete denitrification for a one sludge system, which is generally preferred over twosludge systems due to cost considerations, can be difficult. A significant amount of nitrogen can beremoved with a pre-anoxic process (anoxic process prior to the oxic process). The carbonaceousbiochemical oxygen demand (cBOD) in the raw wastewater is used as the carbon source fordenitrification. A large fraction of the nitrate that is produced by the nitrifiers in the oxic process isremoved by high rates of oxic mixed liquor recirculation to the anoxic process. The oxic process effluent14

has a very low cBOD concentration and a significant nitrate concentration. A supplemental source ofcarbon and separate anoxic process following the oxic process is required for achieving nearly completenitrogen removal.In an effort to reduce chemical and operating costs (e.g., aeration costs), and to ensure completenitrogen removal before discharge to lakes or streams, single sludge denitrification needs to achieve twogoals that seem to be in conflict with each other. First, the system must provide aerobic conditions thatallow for full nitrification in order to generate the nitrate required for denitrification. Second, the singlesludge system must reserve a sufficient amount of organic electron donor for anoxic denitrification(Rittman and McCarty, 2001). Engineers have developed three basic strategies to handle this problem(Rittman and McCarty, 2001). These strategies include biomass storage and decay, classicalpredenitrification, and simultaneous nitrification and denitrification.2.2.1.1 Biomass Storage and DecayBiomass storage and decay relies on the electron equivalents that are sequestered by microorganismsthrough growth and synthesis. These electron equivalents were originally derived from the cBOD andcan be released to propel denitrification through endogenous respiration. This process is also known asa Wuhrmann biomass decayer (Wuhrman, 1964).As shown in Figure 2.1, biomass storage and decay starts with an aerobic tank where nitrification occursand cBOD is partially oxidized and partially stored through biomass synthesis. The resulting nitrate andbiomass are then sent to the anoxic tank where denitrification takes place, driven by the endogenousrespiration of biomass.Figure 2.1- Biomass storage and decay (Rittman and McCarty, 2001)Two major reasons prevent this approach from being implemented as a stand-alone process fornitrogen removal. First, the kinetics of endogenous respiration are slow, which then requires a highmixed liquor suspended solids (MLSS) concentration and a long hydraulic retention time (HRT) in the15

anoxic tank. This increases capital costs significantly. A longer HRT in the anoxic tank can causeoperational problems with poor solids settling characteristics and floating solids in the clarifier. Next,biomass decay always results in the release of NH 4 + -N. Although N concentrations released will be lowerthan influent, the process is counterproductive compared to other strategies.2.2.1.2 Classical PredenitrificationFor classical predenitrification process, the denitrification reaction is driven by carbon supplied by theinfluent substrate, prior to the wastewater reaching the aerobic tank. As depicted in Figure 2.2,denitrification occurs in the first tank nitrification occurs in the second tank. This process requires alarge recycle flow from the second tank to the first in order to supply the denitrification process withNO 3 - produced during nitrification. Any NO 3 - that is not recycled escapes treatment and leaves in theeffluent stream. Fractional removal of N is roughly equal towhere Q is the plant influent flowrateand Q r is the sludge recycle flowrate. Recycle flows typically range from 100-400% or more ( ) in orderto recycle enough NO 3 - to ensure substantial total N removals, which can require significant piping andpumping costs.Figure 2.2- Classical Predenitrification (Rittman and McCarty, 2001)With a reliance on influent substrate as the carbon source, the reaction kinetics for classicalpredenitrification are much greater compared to the biomass storage and decay process. Ammoniumthat is released in the anoxic process is oxidized to nitrate in the aerobic process prior to discharge. Theuse of influent cBOD for denitrification also reduces aeration costs as compared to strictly aerobicremoval of cBOD. One main disadvantage to this system is the large nitrate recycle stream that must bemaintained for significant total nitrogen removal.2.2.1.3 Simultaneous Nitrification and DenitrificationSimultaneous nitrification with denitrification, shown in Figure 2.3, is the third strategy for one sludgedenitrification. Achieving nitrification and denitrification concurrently is achieved by holding the16

dissolved oxygen (D.O.) at a sufficiently low level, typically below 1 mg/L, so as to not suppress variousnitrogen reductases for denitrification. D.O. concentration inside the aggregates that normally form inwastewater treatment systems is depressed as well, further promoting denitrification inside the floc aslong as an electron donor is present inside (Rittman and McCarty, 2001).The reliability of simultaneous nitrification and denitrification systems is still in question, although nearly100% N removal has been reported (Rittman and Langeland, 1985). Conservative values for HRT (>10hours) and SRT (>15 days) were used for past documented successful systems for typical municipalwastewater. Further research is needed to determine the combinations of SRT, hydraulic residencetime, and D.O. concentration that guarantee reliability (Rittman and McCarty, 2001).Figure 2.3- Simultaneous Nitrification and Denitrification (Rittman and McCarty, 2001)2.2.1.4 Barnard ProcessDisadvantages presented in the previous three strategies provided an incentive for Dr. J. Barnard ofSouth Africa to develop the Barnard process (Barnard, 1975), considered the most well known combinedprocess system (Rittman and McCarty, 2001). The process blends aspects of both the biomass storageand decay and classical predenitrification strategies starting with an anoxic predenitrification basinwhich utilizes influent cBOD as the carbon source to drive denitrification (Figure 2.4). Nitrate is suppliedfrom nitrification in basin 2 via a recycle line which typically recycles 400% of the plant flow. A recycleflow of 400% results in the denitrification of approximately 80% of the influent TKN to nitrogen gas, atthe same time around 20% of the raw TKN load leaves basin 2 in the form of nitrate (Rittman andMcCarty, 2001).Endogenous respiration provides carbon required to drive the denitrification of NO 3 - from basin 2, inbasin 3. Ammonium released from cell decay amounts to approximately 0.3 mg NH 4 + -N/mg NO 3 - -N. Forexample, if influent TKN was 50 mg/L, the NO 3 - -N concentration leaving basin 2 would then equal about10 mg/L and NH 4 + -N concentration leaving basin 3 would be equal to about 3 mg/L. After nitrification inthe basin 4 and settling in the clarifier, effluent from the Barnard process will contain about 6% of the17

influent TKN in the form of nitrate (Rittman and McCarty, 2001). The process can achieve morecomplete nitrogen removal if a supplemental carbon source is feed to the second anoxic basin (basin 3).The major disadvantage to the Barnard process is the number of basins (at least four) and large recycleflow required between basins 1 and 2. The Barnard process requires the relatively long hydraulicretention times in basins 1 and 2, and this results in larger basin volumes (Rittman and McCarty, 2001).Figure 2.4- Barnard Process Schematic (Rittman and McCarty, 2001)2.2.1.5 Sequencing Batch ReactorStrategies discussed in the previous sections have been in reference to applications at continuous flowactivated sludge plants with multiple process tanks. The same goals in nitrogen removal can beachieved through the use of one tank, called a sequencing batch reactor (SBR). Generally an SBR will runa sequence of fill, react, settle, and decant phases. A typical SBR cycle sequence for BOD and N removalfrom is shown in Figure 2.5 (Rittman and McCarty, 2001). This sequence functions somewhat similar tothe Barnard process, however, there are no recycle flows and only one process tank is required.Influent cBOD is used as the carbon source for denitrification during the anoxic fill mode. Nitratepresent in the reactor during the fill mode is left over from the previous cycle’s aerobic (React 3) andsettling modes as the reactor volume is usually decanted to about 50-70% of full. According toTchobanoglus et al. (2002), denitrification during a mixed nonaerated fill stage is the most efficientmethod for nitrate removal while also providing a selector operation to prevent filamentous sludgebulking. The influent ammonium is converted to NO 3 - through nitrification during React 1.18

Denitrification using biomass decay as the carbon source during React 2 converts the NO 3 - to N 2 gas withsome NH 4 + release due to cell decay. This NH 4 + is converted to NO 3 - through nitrification during React 3.Next, the reactor is in a quiescent mode in order to allow the solids to settle in preparation for decantmode. During decant mode, the settled water on top of the reactor is syphoned off and discharged(Rittman and McCarty, 2001).Complete denitrification is difficult to achieve due to deficient readily biodegradable organic carbon inthe final anoxic stage prior to decanting. As a result, numerous strategies have been investigated toachieve nearly complete N removal in SBR processes. Alleman and Irvine (1980) found through lab-scaleexperiments that cellular storage products can provide the necessary electron equivalents that arerequired by the denitrification process. Other strategies to ensure a suitable amount of carbon fordenitrification rely on feeding raw wastewater during the final anoxic stage.Figure 2.5-Typical SBR cycle for 90% N removal (Rittman and McCarty, 2001)2.2.2 Phosphorus RemovalPhosphorus accumulating organisms (PAOs) achieve luxury phosphorus uptake by anaerobically takingup carbon substrates and storing energy as poly-β-hydroxyalkanoates (PHA). The degradation ofinternal polyphosphate (poly P) which releases phosphorus in the anaerobic process and glycogenprovides energy and reducing power. Under aerobic conditions, the internally stored PHA is oxidizedand used for growth, polyphosphate synthesis and glycogen production (Li and Irvin, 2007).Phosphorus is removed from the liquid phase and stored in the biomass as poly P during the aerobicprocess. Alternating aerobic and anaerobic conditions are required by PAOs in order to gain acompetitive advantage over other microorganisms (Rittman and McCarty, 2001).The following objectives need to be completed to achieve significant phosphorus removal in EBPRsystem. First, PAOs should be selected. Second, storage of poly P is induced. Third, solids rich in poly Pshould be wasted (Rittman and McCarty, 2001). Four principal components, shown in Figure 2.6, arerequired in order to complete these three key points. First, the influent wastewater and mixed liquor19

ecycle must mix in an anaerobic reactor while electron acceptors (NO 3 - , O 2 ) are prohibited to thegreatest degree possible to insure that cBOD oxidation is insignificant during this step. The mixed liquorrecycle line provides a high biomass level which allows fermentation and hydrolysis to occur in theanaerobic tank, however, with the absence of a terminal electron acceptor, the electron equivalents arenot transferred but instead sequestered. Electrons and carbon are stored in insoluble intracellular solidssuch as, for example, poly-β-hydroxyalkanoates (PHA).The next component is the main activated sludge bioreactor(s), depending on the specific system setup,where O 2 is provided as the terminal electron acceptor through aeration. With sufficient sludge age,nitrification will occur resulting in the production of NO 3 - which could also be used as a terminal electronacceptor, specifically for denitrification.The mixed liquor then flows from the bioreactors to a clarifier where it is settled. Most of settledbiomass will be returned to the anaerobic zone to mix with the influent wastewater in order to ensurethat the sludge experiences alternating anaerobic and respiring conditions, which is the thirdcomponent. Sludge in the bioreactor that is enriched in poly P is wasted in order to control SRT andremove biomass that is high in phosphorus content (Rittman and McCarty, 2001). If the process isoperated at long SRTs that result in nitrification, the phosphorus process is adversely impacted by thenitrate in the sludge recycle to the anaerobic reactors.Figure 2.6- Required components for P removal in an active sludge EBPR process2.2.2.1 EBPR with Classical PredenitrificationFor N and P removal an anaerobic step can be added to the front of a classical predenitrification systemwhich was previously described. This system is known as Phoredox (Figure 2.7). The anaerobic tank atthe head of the plant allows for electrons and carbon to be sequestered and for the sludge toexperience alternating anaerobic aerobic environments. This process reduces the nitrate load to theanaerobic process and thereby provides a better environment for PAO growth.20

Figure 2.7- Phoredox process (Rittman and McCarty, 2001)2.2.2.2 Bardenpho ProcessA similar modification can be made to the Barnard process as shown in Figure 2.8. With the addition ofan anaerobic tank to the front of the process, and a recycle line to bring mixed liquor back to it, thebiomass will experience alternating anaerobic and aerobic environments.Figure 2.8-Bardenpho process (Rittman and McCarty, 2001)2.2.2.3 EBPR in SBRsEBPR in SBRs is achieved with the additions of an anaerobic step followed by an aerobic step to the SBRcycle. Furthermore, in order for PAOs to compete favorably, it is important for substrate to be availablein the reactor during the anaerobic period (Manning and Irvine, 1985). As shown in Figure 2.9, substrateis consumed by the biomass under anaerobic conditions and phosphorus uptake occurs during thefollowing aerobic conditions while PHA is consumed. A summary of relevant research on nutrientremoval in SBR process is presented in the following section.21

Figure 2.9- Characteristic EBPR behavior in an Anaerobic/Aerobic SBR (Copp, 1998)2.3 Previous SBR workThe concept of SBRs has been around since the early 1900s. Interest in SBRs for BNR treatmentincreased throughout the mid 1980s and through the 1990s due, in part, to a successful EPA funded,full-scale study in Culver, Indiana completed by Irvine et al. (1983) (Sedlack et al., 1991). Another reasonfor SBRs increase in popularity was the availability of new programmable control technology that madeautomated control a reality. Several studies strived to improve the efficiency of SBRs by optimizingvarious control parameters. The following provides an overview of optimization work completed onSBRs over the past 30 years.2.3.1 General SBR Process for BNRManning and Irvine (1985) showed that phosphorus removal could be achieved at a relatively lowCOD:TKN ratio of 7.5 using a bench-scale SBR reactor. A key to their success was the presence of excesssubstrate during the anaerobic period allowing PAOs to compete beneficially. Excellent sludge settlingcharacteristics were reportedly achieved. However, poor sludge settling characteristics were also notedas the result of some phosphorus removal strategies (Manning and Irvine, 1985).Two pilot-scale SBR reactors treating domestic wastewater were operated for 5 months in order toculture sludge capable of BNR and to evaluate long term BNR performance in a study completed byBernardes and Klapwijk (1996). The pilot plant achieved average effluent concentrations of less than 1mg P/L and less than 12 mg N/L. Authors found that P release rate was a key parameter in P removal.Also, the ratio of influent flow to the volume of remaining sludge (after decanting) is a key parameter forgood performance. Finally, authors found that the quick dissipation of nitrate during the anoxic periodis important for P-removal because this maximizes the period of anaerobic conditions. The highdenitrification rates were the result of readily biodegradable substrate present in the raw wastewaterplus acetate addition as a supplemental carbon source.Danesh and Oleszkiewicz (1996) compared the performance of a conventional SBR system to a twostageanaerobic-aerobic SBR system in parallel lab-scale systems. The anaerobic step (prefermentation)22

increased the mean volatile fatty acid (VFA)/soluble P ratio to 11.3. This enhanced the P removal, sludgeconsistency, and settleablilty in the following SBR unit. Effluent Ortho-P concentrations for the twostagereactor were consistently less than 0.5 mg/L whereas the conventional SBR effluent ortho-Pconcentrations were greater than 1.5 mg/L.Surampalli et al. (1997) compared performance of a typical SBR design for BOD 5 and TSS removal to theperformance of a slightly modified SBR design for BNR using three full-scale SBR plants. Modifications tothe SBR design to achieve BNR included changes to the cycle sequence and aeration schedule to providealternating aerobic-anaerobic periods. SBRs achieved effluent concentrations of 1-2 mg/L ammonianitrogenand less than 1 mg/L phosphorus without chemical addition.Effluent total phosphorus concentrations of 0.9 mg/L were achieved by Rim et al. (1997) in a pilot-scaleSBR plant. Effluent total nitrogen concentrations were 13.6 mg/L. Effluent quality was achieved bycontrolling the quantity of water decanted and operation cycles. The pilot study indicated that a highlyvariable flow of wastewater generated at a recreational facility could be treated with the SBR process.2.3.2 SBR Cycle TimesKargi and Uygur (2004) used varying combinations of operational period durations in a lab-scale SBR forBNR with a sequence of anaerobic, anoxic, oxic, anoxic, oxic, and settling phases treating syntheticwastewater. The authors found an optimum cycle sequence by varying each phase by four differentvalues and selecting the sequence that gave the greatest removal efficiency. The optimum cyclesequence consisted of anaerobic, anoxic, oxic, anoxic, oxic, and settling phases of 2, 1, 4.5, 1.5, 1.5hours, respectively. These times provided removal efficiencies for COD, nitrogen (NH 4 + -N), andphosphate (PO 4 -P) of 96, 87, and 90%, respectively.Debik and Manav (2009) used analysis of variance to determine the best combination of cycle sequencetimes for their lab-scale SBR. The BNR SBR was treating municipal wastewater. A total of four differentcycles were evaluated. The cycle that consisted of fill, anaerobic, aerobic1, anoxic, aerobic2, settle, anddecant phases of 0.5, 2, 2, 1, 0.75, 1, 0.5 hour durations, respectively. The results indicated that similarremoval efficiencies in all parameters could be achieved by using shorter total aeration time than thosereported by previous studies.Fongsatitkul et al. (2008) operated a lab-scale SBR at a 12-hour cycle length with alternating anoxic/oxicphases treating municipal wastewater. The authors observed improving performance in correlationwith increasing the first anoxic period’s fraction of time in the cycle.Third et al. (2005) operated a lab-scale SBR fed with acetate, using poly hydroxybutyrate (PHB) aselectron donor for denitrification for simultaneous nitrification and denitrification (SND). Termination ofaerobic phase occurred automatically for a continuous duration based on ammonia depletion, usingspecific oxygen uptake rate as the control parameter. The authors observed an increase in capacity forN-removal through SND by the biomass and improved sludge settling characteristics.23

2.3.3 SBR Controllers for Automatic Cycle Time AdjustmentIn order to better control aeration time provided per cycle and thereby reduce the energy cost of excessaeration, researchers have analyzed various operational parameters in SBRs for signs of completenitrification and/or denitrification. Hamamoto et al. (1997) developed an online controller that usesconstantly relayed measurements of D.O., pH, oxidation reduction potential (ORP) and water level toautomatically determine best possible mixing and aeration period times. The controller uses “fuzzylogic”, a program developed by the authors. Full-scale testing indicated average nitrogen andphosphorus removal rates of 96% and 93%, respectively. Kalker et al. (1999) found that a fuzzy logicbased controller outperformed two conventional controllers in terms of effluent quality and energysavings. Fuzzy logic was further investigated by Baroni et al. (2006) at a full-scale plant for about oneyear. Long term effluent ammonia concentrations were reduced from 9.6 to 7.6 mg N/L and varianceswere reduced from 10.9 to 4.0 mg N/L, respectively. The payback period for implementing the fuzzylogic system was estimated to be about 3 years based on four percent energy savings per year.Lee et al. (2001) operated a lab-scale SBR that operated with a sequence of anaerobic-aerobic-anoxicaerobicperiods for BNR using fixed time operation and real-time controlled operation. The real-timecontrolled operation used real-time measurements of pH, ORP, and D.O. to adjust the length of eachoperational period. The controlled operation of the SBR showed better removal than the fixed time SBRwith N removal at (74.6 ± 3.2% versus 68.7 ± 4.2%) and P removal at (96.8 ± 1.1% versus 94.0 ± 1.2%).Li and Irvin (2007) compared the use of alkalinity to pH and ORP to see which parameter gave a morefavorable indication for complete nitrification/ denitrification in a lab-scale SBR. They found thatalkalinity offered a more accurate indication of nitrification and denitrification, particularly when theefficiency of nitrification/ denitrification was progressively decreasing.2.4 Wastewater Treatment Process ModelingWastewater treatment process models have a history dating back to 1914 when Arden and Lockett firstdescribed some of the fundamental mathematics behind the activated sludge process (WEF MOP no. 31,2009). Since then, numerous models have been developed for many different applications. For moreinformation on wastewater treatment process modeling, see WEF Manual of Practice no. 31 (2009) andWERF publication Methods for Wastewater Characterization in Activated Sludge Modeling (Melcer et al.,2003).2.4.1 IWA modelsSome of the more well known models were developed by a task group for the International WaterAssociation (IWA). The IWA models were developed by focusing on single sludge systems in whichcarbon oxidation, nitrification, and denitrification were accomplished. A series of well defined reactionsteps were developed which included: the identification of fundamental processes within each system,the determination of kinetic and stoichiometric parameters for each process, and the incorporation ofprocess rate expressions into mass balance equations which represented the actual configuration of thesystem being modeled. Finally, these equations then need to be solved for the specific process designparameters and conditions (Melcer et al., 2003). Several IWA model versions exist. The most widely24

used model is the Activated Sludge Model 1 (ASM1) (WEF MOP no. 31, 2009). It is important to notethat IWA models are limited to carbon and nitrogen removal. Further details on IWA models areincluded in the following publications: Grady et al. (1986), Henze et al. (1987a, 1987b), and Gujer andHenze (1991) (Melcer et al., 2003).2.4.2 EBPR models (summarized from Melcer et. al, 2003)EBPR has been addressed in several of models including those developed by Comeau et al. (1986),extended and modified by Wentzel et al. (1986), and by Mino et al. (1987). All three of these models aregenerally in agreement with each other concerning biochemical control mechanisms and the essentialrequirements for a system to achieve EBPR. An additional BNR model was developed by Dold (1990,1991) which combines the ASM1 model for heterotrophic and autotrophic organisms (Henze et al.1987a, 1987b) and the Wentzel et al. (1989a) model for PAOs with a number of extensions andmodifications incorporated throughout. After the initial development of the combined nitrification,denitrification, and EBPR model (Dold, 1990, 1991), extensive evaluations were completed using datafrom laboratory-scale and full-scale treatment plants. The evaluations provided further refinement tothe model. Barker and Dold (1997) presented a summary of the resulting model which now includes,among other things, fermentation processes and hydrolysis of enmeshed slowly biodegradable CODunder anoxic and anaerobic conditions (Barker and Dold, 1997).2.4.3 BioWin Wastewater Process SimulatorBarker and Dold (1997) provided the basis for a wastewater simulator named BioWin (EnviroSim, 2010).The BioWin wastewater simulator provides a user-friendly interface for evaluating wastewatertreatment plant performance. A simulator incorporates multiple models, set or sets of equations solvedin a matrix, to describe each individual wastewater treatment process (WEF MOP no. 31, 2009). BioWinutilizes multiple icons which represent different unit processes in wastewater treatment (e.g., aerationbasin, clarifier, SBR). Each unit process may include multiple sub-models that describe individualcomponents of the unit process. For example, the SBR module, represented by a single icon, containssub-models used to describe aeration basins as well as clarifiers.2.4.4 Simulator Influent Data RequirementsInfluent wastewater characteristics can vary widely depending on many factors. The most prominentfactor is industrial contributions to municipal wastewater streams. Wastewater simulators require asignificant amount of data in order correctly characterize the wastewater for each specific application.Useful data include parameters regularly monitored by operators such as influent cBOD, TSS, TKN andammonia-nitrogen as well other parameters that may not be regularly monitored, if ever. Examples ofparameters not regularly monitored include influent VSS, filtered COD, flocculated and filtered COD,filtered cBOD 5 , ortho-phosphate, and influent acetate. These parameter values are needed in order toappropriately fraction COD, nitrogen, and phosphorus. An example of COD fractionation for BioWin isgiven in Figure 2.10. Fractioning is required in order to define the state variables (e.g., fraction ofunbiodegradable particulate COD, up) upon which wastewater models are built (WER MOP no. 31,2009). This subject is covered in great detail in the WERF publication Methods for WastewaterCharacterization (2003).25

Figure 2.10- COD profile with BioWin state variables (adapted from Tchobanoglous et al., 2003)2.4.5 Dynamic ModelingDynamic modeling is a term used to describe mathematical models that use differential equations as afunction of time (WEF MOP no. 31, 2009). SBRs must be modeled dynamically because the biologicalprocess is not operating at steady state as a cycle sequence progresses in time. The BioWin wastewatersimulator contains a prepackaged dynamic SBR module that can be implemented through the use of onesimple icon. Dynamic models are capable of predicting dynamic process responses when time varyinginputs, such as a diurnal flow pattern with changing influent concentrations, are implemented. Thedynamic model for SBRs can be used to give long-term process steady state solutions when constantinputs (influent flow, concentrations, return rate, etc.) are used and the model is run for an extendedperiod of time (WEF MOP no. 31, 2009). According to WEF MOP no. 31 (2009), an extended period oftime is within 3 SRTs. The important point is not that a specific amount of time be modeled, but thatthe SBR has reached an overall process state of steady operation and little variability in operationalparameters is observed.2.4.6 Simulator ParametersIn order to describe the complex events that occur in wastewater treatment, a significant number ofkinetic, stoichiometric, settling, biofilm, and other parameters are necessary. Parameters used in theBioWin wastewater simulator have been calibrated over a wide range of systems (e.g., aerobic systems,EBPR systems, BNR systems [under steady state and dynamic conditions]) and over a range of operatingconditions (e.g., sludge age and recycle rates) (Barker and Dold, 1997). Accurate predictions for allsystems using one general set of model parameters were reported by Barker and Dold (1997). Oneexception was reported, however. The maximum specific growth rate of the nitrifiers was observed tohave significant variability depending on the influent wastewater composition.2.4.7 Previous work completed in modeling of SBRs for BNRCoelho et al. (2000) studied the nitrification step of a BNR process performed in an SBR. The authorsused an adapted version of the ASM1 model for SBRs to determine optimal conditions. These optimal26

conditions included a discrete fill strategy with symmetric pulses for influent wastewater and oxygensupply to the system. This strategy was then implemented on a small scale SBR and a significantreduction in batch time was achieved.Furumai et al. (1999) used a modified version of the ASM2 model to predict long term dynamic behaviorof BNR in SBRs. Model parameters were calibrated based on experimental data collected from a labscaleSBR. Following calibration, changes in total organic carbon (TOC), NH 4 + -N, NO 3 - -N, NO 2-- N, andPO 4 3- -P could be readily predicted by the model. To test the model’s ability to predict dynamic behavior,the authors increased and decreased the influent TOC in step-wise manner for 5 weeks. The modelpredictions were found to match experimental results well.Chang et al. (2000) developed a model for BNR in SBRs using material balances and Monod kinetics.Model parameters were determined based on the best fit of predicted results to experimental results ofa lab-scale SBR. Authors found that reduced P uptake during the aerobic period limited overall Premoval. PAO washout was also observed under low organic loading conditions. The model successfullysimulated operations of a full-scale SBR and was useful in optimizing hydraulic retention times.Velmurugan et al. (2010) proposed a simplified SBR model for carbon and N removal in SBRs. Themathematical model contains fewer variables than most other models and was calibrated and validatedbased on data obtained from a full-scale SBR plant treating municipal wastewater. Following validation,the model was used to redesign the existing full-scale plant. The resulting design reduced the reactorvolume by approximately 11% and eliminated a total of 1.99 hours of aeration per cycle.2.5 SummaryFurther investigation of BNR in SBRs is needed to understand how to enhance removal efficiencies andreliability of processes under dynamic conditions.The SBR has been researched extensively in the past in part because of the ease that a one tank processprovides to researchers on a lab-scale level. SBRs also hold the unique advantage of small site arearequirements as compared with conventional activated sludge processes. New effluent nutrientrequirements could result in an additional SBR benefit over conventional: the ability to achieve nearlycomplete BNR without any additional construction. By simply changing the SBR cycle sequence, anoperator could potentially turn an SBR that treats for carbon and nitrogen removal into a BNR process.Accurately modeling the changes to an SBR cycle sequence to find optimum settings beforeimplementation on a full-scale level could save the operator significant time and money.Currently, most of the studies completed on BNR modeling focus on developing new models and not onapplying developed models to full-scale processes. No papers were found in which the investigatingauthors did not have a direct connection to the development of the model being investigated.Specifically, no papers were found on calibrating the BioWin wastewater simulator to predict effluentcharacteristics by a third party investigator. More information is needed on typical simulator parametervalue ranges and calibration techniques for smaller sized wastewater treatment facilities that havelimited amounts of data available.27

Chapter 3.0 MethodsAnalysis of a full-scale sequencing batch reactor (SBR) plant was performed with the use of a commercialwastewater simulator BioWin (EnviroSim, 2010). The BioWin analysis draws on data collected during afull-scale study completed in 2005 at the wastewater treatment facility of the city of Grundy Center,Iowa (Ersu et al., 2008). In this study a regular SBR treatment sequence, consisting of mixed fill, aeratedfill, and aerated sequences (identified as regular mode) was compared to two biological nutrientremoval modes (identified as BNR-S1 and BNR-S2), consisting of anaerobic, anoxic, and aeratedsequences to observe differences in contaminant removal efficiencies. Changes in the SBR sequences isa simple and inexpensive upgrade for SBR plants to meet potentially more stringent permitrequirements for nitrogen and phosphorus. The upgrade investigated was a change to the cyclesequence and the addition of an anaerobic phase to the SBR operations. If sequences can be effectivelyoptimized through the use of wastewater treatment simulators, even greater benefits could be realizedwithout the need for full-scale studies.The following modeling analysis is an attempt to calibrate the BioWin simulator to the Grundy Centerwastewater treatment facility process performance data. The analysis includes data interpretation,influent characterization, simulator setup, sensitivity analysis, regular mode calibration, comparison ofBNR-S1 mode simulation using regular mode calibrated simulator parameters to observed BNR-S1 modedata, calibration of BNR-S1 mode, and verification of BNR-S1 calibrated simulation using BNR-S2 data(Figure 3.1). The analysis was designed to evolve from the regular mode on to BNR-S1 and BNR-S2modes. For example, this evolution is shown by steps 6 and 7 in Figure 3.1. Information from thecalibration of the regular mode was used to calibrate the BNR-S1 mode.Figure 3.1 - Flowchart of activities for BioWin simulations. IS = influent specifier, CSP = calibratedsimulator parameter28

3.1 Wastewater Treatment Plant DescriptionThe Grundy Center wastewater treatment plant treats municipal wastewater from the Grundy Centercommunity, population of 2596 (2000 census) and a food-processing industry (salad dressingmanufacturer). The two elliptical shaped SBR basins (12.6 m wide [radius of 6.3 m], 23 m long, and 5.2m deep) were originally oxidation ditches before being renovated to their present use as SBR reactors.The facility contains a rectangular flow equalization pond (44 m wide, 97 m long, and 2.7 m deep) and asludge storage and treatment facility (Figure 3.2). The average flowrate to the treatment plant in 2005was 2680 m 3 /d (708,000 gal/d), while the design flowrate is 3150 m 3 /d (832,000 gal/d). Each SBR has amaximum operating volume of 1340 m 3 (354,000 gal), a design hydraulic retention time of 10.2 hours,and a design SRT of 20 days. A pipe located approximately 2.1 m above the floor of the SBR allowsinfluent wastewater to flow into the basin from one end and a surface decanter removes treatedeffluent at the opposite end of the basin.Figure 3.2 - Grundy Center wastewater treatment plant schematic, not to scale (Ersu et al., 2008)29

3.2 Sequencing Batch Reactor ModesNormal operation for the SBRs (regular mode) consisted of 5 cycles/d, with a sequence of 1 hour mixedfill, 1.4 hours aerated fill (oxic), 0.6 hour aeration, 0.9 hour settle, and 0.9 hour decant/waste periods,for a total of 4.8 hours/cycle (see Table 3.1). Original treatment objectives for the regular modeincluded carbonaceous and ammonia removal.BNR-S1 was the first BNR mode tested and operated with a sequence of anaerobic-oxic-anoxic-oxicperiods whereas the BNR-S2 mode had a sequence of anaerobic-anoxic-oxic. The two biological nutrientremoval modes had a total length of 6.0 hours/cycle and operated 4 cycles/day. Specifically, BNR-S1had periods of 1.5 hours simultaneous fill and decant (anaerobic conditions), 1.5 hours fill only (oxicconditions with air on), 1.5 hours mixing (anoxic conditions), 0.6 hour aeration (oxic condition), and 0.9hour settle period.Table 3.1- Regular and biological nutrient removal modesfor Grundy Center wastewater treatment plant.Modes Time (hours) NotesRegular mode (5 cycles/d)Fill + mixing 1Oxic (aeration) + fill 1.4Oxic (aeration) 0.6Settle 0.9Decant/waste 0.9Total 4.8BNR-S1 (4 cycles/d)Anaerobic + fill + decant 1.5 SimultaneouslyOxic (aeration) + fill 1.5 1.5 < DO < 3.0 mg/LAnoxic (mixing) 1.5 0.1 < DO < 0.3 mg/LOxic (aeration) 0.6 1.5 < DO < 3.0 mg/LSettle 0.9Total 6.0BNR-S2Anaerobic + fill + decant 1.5 SimultaneouslyAnoxic (intermittent aeration+ mixing) + fill 1.5 0.1 < DO < 0.3 mg/LAnoxic (mixing) 0.5 0.1 < DO < 0.3 mg/LOxic (aeration) 1.6 1.5 < DO < 3.0 mg/LSettle 0.9Total 6.0From Ersu et al., 200830

BNR-S2 operated with 1.5 hours simultaneous fill and decant (anaerobic conditions), 1.5 hours fill(anoxic conditions and intermittent aeration), 0.5 hour mixing only (anoxic conditions), 1.6 hoursaeration, and 0.9 hour settle period.The intermittent aeration during the anoxic period(s) of the BNR-S1 and BNR-S2 modes maintained adissolved oxygen concentration (D.O.) between 0.1 and 0.3 mg/L. This aeration was provided in order toprevent anaerobic conditions and to promote a favorable anoxic environment. During the oxic period ofboth modes, the D.O. concentration was maintained between 1.5 and 3.0 mg/L. An SRT ofapproximately 13 days was maintained through the wasting of mixed liquor towards the end of thesettling period.3.3 Data InterpretationEnviroSim’s influent specifier (IS) requires the input of several wastewater variables in order to fractionthe influent COD according to the specific application (See Appendix I). This requires the collection of asignificant amount of data that is not typically monitored by wastewater operators. Due to cost, thecollection of this data was not possible for this report. Assumptions were documented and a full list ofdetailed calculations can be found in Appendix I. In general, three types of values were used: valuesderived directly from data available taken in the form of an average, values estimated using ratios ofhistorical data, and values estimated using ratios of IS default values. More information on wastewatercharacterization and COD fractioning is presented by Melcer et al. (2003).Available data for the analysis include influent data collected in 2004 and 2005 as shown in Table 3.2and 24-hour composite samples of influent and effluent taken for each of the 3 cycle sequences asshown in Table 3.3.Performance data for the regular mode (Table 3.3) was collected during the month of May, 2005 with atemperature of approximately 20˚C. This data is based on 3 sets of 24-hour composite samples andincludes influent and effluent values for pH, soluble COD (sCOD), 5-day biochemical oxygen demand(BOD 5 ), total nitrogen, ammonia, nitrite, nitrate, total phosphorus, and effluent values for totalsuspended solids (TSS).Performance data for BNR-S1 presented in Table 3.3 is based on a total of 8 composite samples thatwere collected during August, 2005 with a temperature of approximately 24˚C. Influent and effluentvalues are given for pH, sCOD, BOD 5 , total nitrogen, ammonia, nitrite, nitrate, and total phosphorus andeffluent values are given for TSS.Two different time periods are compared for BNR-S2 in Table 3.3. A total of 5 composite samples weretaken for the first period during the month of October, 2005 with a temperature of approximately 20˚C.The second period tested BNR-S2 during winter conditions when eight 24-hour composite samples weretaken during the months of January and February, 2006 with a temperature of approximately 15.7 o C .Monthly monitoring reports to the Iowa Department of Natural Resources were also available for theappropriate time period. An emphasis was placed on the use of data from corresponding time periodsto the respective cycle. For example, data for the regular cycle in Table 3.3 was collected during the31

month of May, 2005 so the flow data used in the simulation of the regular mode was taken from themonthly mechanical monitoring reports for May, 2005 (see Appendix III).In addition to these data sources, two 24-hour composite samples were taken on October 20, 2010 andanalyzed to determine the volatile fraction of the total suspended solids.Table 3.2- Wastewater characteristics (combinedinfluent and industrial wastewater a )CombinedConcentration (mg/L)IndustrialComposition Average b Average b Low High1644 ±Flowrate (m 3 /d)1108 91 ± 37 11 214pH 7.7 ± 0.2 8.6 ± 1.8 5.1 12.34260 ±COD 510 ± 9.11280 1440 11800sCOD 396 ± 8.0 NA NA NA2286 ±BOD 5 295 ± 10.91462 453 6500Oil and grease NA 382 ± 268 64 1213Total nitrogen 42.6 ± 2.0 NA NA NAAmmonia-N 22.9 ± 1.8 NA NA NANitrite-N 0.2 ± 0.1 NA NA NANitrate-N 0.1 ± 0.1 NA NA NAOrganic N 20.7 ± 2.6 NA NA NATotal Soluble P 11.4 ± 0.9 NA NA NASuspended Solids 75.4 ± 8.4 NA NA NANo. of Measurements 104 24a Based on records for 2004 and 2005; NA = not availableb Statistical 95% confidence intervalFrom Ersu et al., 2008Table 3.3- Wastewater treatment performance for different modes apH sCOD BOD5 TN NH 3 NO - 2 -N NO - 3 -N TP TSSRegular May 20˚CInfluent 7.5 430 175 41.9 21.2 0.2 0.4 10.8 -Effluent 6.8 26.7 11.3 19 1.5 3.4 10.5 6 15.1Removal (%) 93.4 93.2 54.7 93.2 - - 44.7 -BNR-S1 August 23.8Influent 7.2 608 342 39.6 24.1 0.1 0.3 9.5 -Effluent 6.9 29.3 14.2 6.6 1.4 1.3 2.6 1.5 7.9Removal (%) 95.5 95.6 84.3 94.5 - - 88.2 -BNR-S2 October 20.1Influent 7.4 738 433 34.4 20.9 - - 9 -32

Effluent 7.2 35 7.8 3.5 0.8 0.8 1.9 1.6 4.7Removal (%) 94.3 97.9 89.4 97.3 - - 86.9 -BNR-S2 (colder) Jan/Feb 15.7Influent 7.4 830 NA 33.7 27.7 - - 10.4 -Effluent NA 34.7 NA 7.7 0.6 1.7 5.4 2.1 4.1Removal (%) 95.4 NA 81 96.1 - - 70.4 -a Number of measurements for regular, BNR-S1, BNR-S2, and BNR-S2 (Jan) were 3, 8, 5, and 8, respectively. NA = not available. Mean valuesgiven as mg/L (condensed version from Ersu et al., 2008)Table 3.4 contains calculated values of total COD, influent flocculated and filtered COD (FF COD), cBOD,and filtered carbonaceous biochemical oxygen demand (fcBOD). Total COD for each mode wascalculated from the ratio of the long term (2 yr average) COD/sCOD times the sCOD for the given mode.This method was selected over the typical method of using BOD to predicted COD because of aninconsistency in the BOD data for regular mode. Projected estimates for values not given were made byusing ratios of longer term data (Table 3.2).Table 3.4- Influent specifier input values regular modeParameter Value Units Data SourceFlow 1911.6 m 3 Mechanical monthly monitoring reports/d(May, 2005)Influent total COD 554 mg/L Estimated using Table 3.2 and 3.3 dataTKN 41.3 mg/L Calculated from Table 3 dataTotal P 10.8 mg/L Table 3.3Nitrate 0.4 mg/L Table 3.3pH 7.5 - Table 3.3Alkalinity 300 mg CaCO 3/L DefaultCalcium 80 mg/L DefaultMagnesium 15 mg/L DefaultD.O. 0 mg/L DefaultEffluent filtered COD 26.7 mg/L Table 3.3Influent filtered COD 430 mg/L Table 3.3Influent FF COD 241 mg/LEstimated using Table 3 data and defaultvaluesAmmonia 21.2 mg/L Table 3.3Acetate 12 mg/L DefaultOrtho-phosphate 5 mg/L DefaultInfluent cBOD 5 277 mg/L Estimated using COD/cBOD = 2Influent filtered cBOD 5 215 mg/L Estimated using sCOD/fcBOD = 2Influent VSS 151 mg/L Estimated from sample taken 10/20/10Influent TSS 183 mg/LMechanical monthly monitoring reports(May, 2005)3.4 BioWin Simulator SetupBioWin wastewater simulator contains a prepackaged dynamic SBR module that can be implementedthrough the use of one simple icon (Figure 3.3). For this report, only constant value inputs were useddue to limited data availability. Steady state results were achieved by running the dynamic simulation33

for 40 days (more than 3 times SRT) and taking an average of the 41 st day effluent parameter values.Confirmation of steady state conditions was determined by visual inspection of plots of key operatingparameters.Figure 3.3 - BioWin screen system layout (EnviroSim, 2010)In order to save time simulating, one SBR was used to simulate the Grundy Center facility instead of two.With two SBRs, the computer must complete twice the number of calculations causing a significantincrease in simulation time. To compensate for simulating half of the actual treatment plant, theflowrate was entered into the BioWin influent editor as a variable flow that is turned on and off inaccordance to the fill schedule for each mode. The regular and BNR-S1 mode influent itineraries aregiven in Appendix I. From time = 0 to time = 2.4 hours in the cycle, the flowrate was 1911.6 m 3 /d andthe influent concentrations were constant values but after 2.4 hours have passed in the cycle, theflowrate was 0 until the cycle starts over again at time 0.3.4.1 SBR DimensionsAll SBR modes were setup with the same physical SBR dimensions based on effective dimensions insteadof actual dimensions. For example, the SBR specifications state that the sidewall depth is 5.2 m,however, the high level alarms are set at 4.66 m. As a result, the volume located above 4.66 m will mostlikely never be used. A depth of 4.1 m was used for simulation purposes and was based on the depth inthe SBR on typical flow days giving a reactor volume of 1048.5 m 3 . According to the wastewateroperator in Grundy Center, the plant typically decants to a level between 3.2 and 3.5 m. This meansthat approximately 20% of the reactor volume is decanted leaving 80% in the basin. Therefore, thedecant option has been set to decant to 80% of full on all SBRs. The influent layer feed option was set atlayer 5 (out of 9 total) because the influent pipe is located just over half way up the side wall atapproximately 2.1 m above the SBR floor.An error was made initially in defining the width of the SBR as the actual width specified of 12.6 m. Thewidth dimension called for in BioWin is the distance from the influent pipe to the decanter per BioWinuser’s manual (EnviroSim, 2010). The decanter is located approximately 15.7 m from the influent pipewall (see Figure 3.4). Since the shape of the SBR is elliptical and not rectangular, an effective lengthshould be used because the area per unit length is not constant across the rounded ends. The total areaacross the distance of 15.4 m from the influent pipe wall to the decanter totals 173.5 m 2 . Applying a34

constant width of 12.6 m over this area gives an effective length of 13.8 m, or 1.2 m longer thanoriginally defined. An analysis was performed to determine the implications of this change on thesimulations already completed.Figure 3.4 - SBR dimensions3.4.2 SBR OperationAccording Ersu et al. (2008), the SRT was maintained at approximately 13 days through the wasting ofmixed liquor towards the end of the settling phase. The actual SBRs in Grundy Center do not have flowmeters on their solids wasting valves so the actual amount of solids wasted per day could not bedetermined (per interview with WWTP operator). In order to ensure an SRT of 13 days in the simulator,one thirteenth of the total reactor volume was wasted each day. This was accomplished by wasting atan appropriate flowrate for 10 minutes at the end of each react cycle for all SBR modes. Figure AI.11and AI.15 shows the SBR underflow itinerary for the regular mode and BNR-S1 modes, repectively. SeeAppendix I for detailed calculations.Influent fractions, calculated using the influent specifier, along with influent wastewater flows andconcentrations were entered into the influent editor specifically for each mode. Cycle sequences foreach mode were entered into the SBR editor box.3.4.3 SBR Cycle Sequences in BioWinThe BioWin SBR module contains a set format for scheduling cycle length, mix period, and settlingperiod as shown in Figure 3.5. A separate aeration schedule is used to input D.O. concentration valuesas a function of time. Aeration may only be controlled during the mix period. BioWin assumes noaeration during settling and decant periods. The time periods for the regular mode were entered just asshown in Table 3.1, however, a slight adjustment was required for the entry of BNR-S1 and BNR-S2modes. As shown in Table 3.1, decant period takes place during the first step (Anaerobic + Fill + Decant).Due to the set format in BioWin for decant scheduling, the decant period had to be entered as the laststep in the sequence. This also means that the fill schedule must be split into two different segments,one 1.5 hour period at the end of the cycle and one 1.5 hour period at the beginning of the cycle. Thisminor rearrangement of sequence should have no effect on SBR performance since the SBR operates ina loop. The ending period of the sequence is always followed by the first period when the loop iscontinuously repeated, resulting in the same sequence as shown in Table 3.1.35

Figure 3.5 - BioWin SBR operation input screen for BNR-S1 mode (EnviroSim, 2010)3.5 Sensitivity AnalysisA sensitivity analysis was completed using the regular and BNR-S1 mode SBR setups. A sensitivityanalysis is a useful tool for modelers because it reveals the parameters that have the largest effect on agiven unit process or effluent characteristic. A full list of parameters evaluated is given in Table 3.5.More information on each parameter is provided in Appendix IV. The sensitivity analysis was carried outby varying one simulator parameter, running the simulation, and then comparing the results of effluentparameters to BioWin default values. Percent deviation was calculated for each effluent parameter in agiven simulation run (see equation 5). Effluent parameters evaluated include TSS, pH, total nitrogen,ammonia, nitrite, nitrate, total phosphorus, and filtered COD (soluble COD). These particular effluentparameters were chosen based on the availability of observed data from Ersu et al., (2008).Where: a = value of effluent parameter for simulation run with a single simulator parameteradjusted= simulator parameter= percent adjustment to simulator parameter (± 20, 40, 60%)b = simulation run with BioWin default values[5]36

Table 3.5- Sensitivity Parameters for Regular modeStoichiometricParametersHeterotrophs (OHO)DefaultValueUnitsHeterotrophic yield 0.666 mgCOD/mgCODAmmonia oxidizing bacteria (AOB)Yield 0.15 mgCOD/mgNNitrite oxidizing bacteria (NOB)Yield (NOB) 0.09 mgCOD/mgNPAOsYield Aerobic 0.639 -Yield anoxic 0.52 -Aerobic P/PHA uptake 0.95 mgP/mgCODAnoxic P/PHA uptake 0.35 mgP/mgCODYield of PHA sequestration 0.889 -Fraction to endogenous part. 0.25 -Inert fraction of endogenous solution 0.2 -P/Ac release ratio 0.49 mgP/mgCODYield of low PP 0.94 -CommonKineticParticulate substrate COD:VSS ratio 1.6 -Particulate inert COD:VSS ratio 1.6 -Heterotrophs (OHO)Heterotrophic max. spec. growth rate 3.2 1/dSubstrate half sat. 5 mgCOD/LAnoxic growth factor 0.5 -Heterotrophic aerobic decay rate 0.62 1/dAnoxic/Anaerobic decay 0.3 1/dHydrolysis rate (AS) 2.1 1/dHydrolysis half sat. (AS) 0.06 -Anoxic hydrolysis factor 0.28 -Anaerobic hydrolysis factor 0.5 -Adsorption rate of colloids 0.8 L/(mgCOD d)Ammonification rate 0.04 1/dAssimil. nitrite/nitrate reduction rate 0.5 1/dFermentation rate 3.2 1/dFermentation half saturation 5 mgCOD/LAnaerobic growth factor (AS) 0.125 -Hydrolysis rate (AD) 0.1 1/dHydrolysis half sat. (AD) 0.15 mgCOD/LAmmonia oxidizing bacteria (AOB)37

6) for each simulation run was compiled and compared to find the simulation run that best fit theobserved data. A percent value was used in order to better demonstrate error present in smallernumbers. An emphasis was placed on matching effluent total nitrogen and total phosphorusconcentrations.∑ [6]Where:= value of predicted (simulated) effluent parameter= value of observed effluent parameter (data from Table 3.3)= effluent parameter3.6.1 Calibration of Regular ModeThree simulator parameters found to have significant effects on effluent total nitrogen, ammonia,nitrite, and nitrate concentrations were chosen for the calibration of the regular mode. These threesimulator parameters were heterotrophic yield, ammonia oxidizing bacteria (AOB) maximum specificgrowth rate, and nitrite oxidizing bacteria (NOB) maximum specific growth rate (Table 3.6). Threetables, one for each combination of variables, were set up in which one parameter was varied from zeroto 60% by increments of 10% across the top and the other two parameters were varied at the same ratedown the length of the table (see Appendix II, Table AII.3). The parameter values from the simulationrun which provided the least percent error were selected. After that, the maximum Vesilind settlingvelocity was adjusted to reduce the deviation of predicted effluent TSS values from observed. Next, anexpansion on the selected simulation run was completed with a 1% incremental variation of eachsimulator parameter in the range of -4% to +4% of the initial run optimum parameters (see Appendix II,Table AII.6). From this analysis, the simulation run yielding the lowest percent error was selected andcalibration for regular mode was complete.Table 3.6- Simulator parameters for regular mode calibrationAdjustment Range fromDefault ValueSimulation ParameterLower Upper Increment ofvariationTarget EffluentParameter(s) cHeterotrophic yield -60% 0 10% TN, NH 3, NO 3AOB max. spec. growth rate -60% 0 10% TN, NH 3, NO 2, TSSNOB max spec growth rate -60% 0 10% NO 2, TSSMax. Vesilind settling velocity -60% 0 10% NH 3, NO 2, TP, TSSc bold denotes primary target effluent parameter for specific simulation parameter39

3.6.2 Calibration of BNR-S1 ModeThe simulator parameter values for the calibrated regular mode were entered in the BNR-S1 setup andthe simulation was run to see how well the simulator predicted effluent quality for the BNR-S1 mode.Effluent parameters requiring correction were noted and a table was setup (Appendix II, Table AII.7) forthe adjustment of simulator parameters required to achieve a more accurate prediction of totalnitrogen, ammonia, nitrite, nitrate, and total phosphorus. However, this time the variations in the threesimulator parameters were by 5% increments (Table 3.7). The simulation run that provided the leastamount of percent error was selected and the maximum Vesilind settling velocity was adjusted toachieve maximum accuracy in TSS prediction. Next, accuracy of ammonia prediction was improved byadjusting the substrate ammonia half saturation concentration for ammonia oxidizing bacteria. Finally,the effluent TP concentration prediction was improved by adjusting the aerobic P/PHA uptake ratio. Alladjusted simulator parameters were selected based on the sensitivity of the effluent characteristics tothe simulator parameters.Table 3.7- Simulation parameters for BNR-S1 mode calibrationVariation Range fromDefault ValueSimulation ParameterLower Upper Increment ofvariationTarget Effluent Parameter(s) cHeterotrophic yield -30% 0 5% NH 3, NO 2, TP, F. COD, TSSAOB max. spec. growth rate -40% -15% 5% NH 3, NO 2, NO 3NOB max spec growth rate -40% -15% 5% TN, NO 2, NO 3, TSSMax. Vesilind settling velocity -30% 0 5% NH 3, NO 2, TP, TSSNH 3 half saturation conc. (AOB) 0 80% 5% NH 3, NO 2, TPP/Ac release ratio (PAO) 0 30% 5% TPc bold denotes primary target effluent parameter for specific simulation parameter3.7 Analysis of BNR-S2 modeCalibrated simulator parameter values for the BNR-S1 mode were entered into the BNR-S2 setup andthe simulation was run. The ability of the simulator to predict effluent quality using calibrated BNR-S1simulator parameter values in the BNR-S2 setup was assessed.3.8 Effect of WidthThe implications of an error in the width of the SBR as it was defined in BioWin were investigated inorder to determine if modeling data obtained up to that was still representative of the SBR. Data fromthe SBR with the initial width (12.6 m) was compared with data obtained using the corrected width (13.8m).40

3.9 Effect of COD FractionsThe influent specifier was used to calculate COD fractions which were then entered into the BioWinsimulator in order to better define the wastewater flow at Grundy Center. An analysis was performed inorder to better understand the implications of the calculated fractions compared to default values.3.10 BNR-S1 Cycle OptimizationAn analysis was completed on the BNR-S1 mode to investigate the effects of varying the duration of theanaerobic, anoxic, and oxic periods on effluent TN and TP concentrations. The total cycle time and filltime were held constant at 6 hours and 3 hours, respectively. SRT was maintained at 13 days and solidswasting continued to occur in the last 10 minutes of the mixed oxic period. Variations in cycle phaseduration time were made in pairs: anaerobic and oxic; anoxic and oxic. An increase in one period lengthresulted in an equal and opposite decrease in length of the other period’s length. This was completedfor both default and calibrated simulator parameter settings for BNR-S1 mode.41

Chapter 4 Results and Discussion4.1 COD Fractions4.1.1Regular Mode COD FractionsInfluent data for the regular mode (Table 3.4) were entered into the influent specifier (see Appendix Ifor complete data set) and COD fractions were calculated by the influent specifier (see Figure 4.1).Notable differences between default and calculated values include the readily biodegradable COD(RBCOD) fraction (F bs ) and the acetate fraction (F ac ) . The calculated RBCOD fraction was nearly 2.5times the default value while the calculated acetate fraction is only 37% of the default value. Also, thecalculated non-colloidal slowly biodegradable fraction (F xsp ) was only one third of the default value. Thecalculated fractions of unbiodegradable soluble COD (F us ), unbiodegradable particulate COD (F up ),ammonia (F na ) and phosphate (F po4 ) were slightly less than the default values.Figure 4.10- Influent specifier COD fractions page for regular mode (EnviroSim, 2010)4.1.2 BNR-S1 mode COD FractionsCOD influent data for the BNR-S1 mode (Appendix I, Table AI.2) were entered into the influent specifierand COD fractions were calculated by the influent specifier (see Figure 4.2). Values of the fractions weresimilar to the regular mode with the most notable deviations from default values being the highcalculated RBCOD (F bs ) and acetate (F ac ) values as compared with the default values. The calculated noncolloidalslowly biodegradable COD (F xsp ) was significantly less than the default value. The fractions ofunbiodegradable soluble COD (F us ), unbiodegradable particulate COD (F up ), and ammonia (F na ) wereslightly less than the default values while phosphate (F po4 ) was slightly greater than default values.42

Figure 4.2 - Influent specifier COD fractions page for BNR-S1 mode (EnviroSim, 2010)4.1.3 COD Fractions DiscussionAccording to Melcer et al. (2003) RBCOD and unbiodegradable COD are arguably the most significantmunicipal wastewater characteristics for BNR modeling. It is important to note the variations betweenthe calculated and default COD fractions for RBCOD and, to a lesser extent of variation, unbiodegradableparticulate COD. RBCOD is an important food source for PAOs in the anaerobic phase as they work tosequester electrons (Rittman and McCarty, 2001). The ratio of RBCOD concentration to phosphorusconcentration can determine the success of a nutrient removal system designed for excess phosphorusremoval. If the RBCOD concentration to P concentration is too low, insufficient energy will be availableto PAOs in the anaerobic phase. This could result in the PAOs failing to adequately establish themselvesin the process and EBPR will not be achieved. The BNR-S1 mode should not be limited by RBCOD inachieving EBPR because the RBCOD fraction is nearly 2.5 times the default and the influent TPconcentration is around the typical value of 6 mg/L.The unbiodegradable particulate COD fraction has important implications for sludge production andoxygen demand (Melcer et al., 2003). These particulate fractions occupy space in the bioreactor butprovide no benefit to the process. This accumulation of unbiodegradable particulate leads to anincrease in MLSS concentration at a given SRT. Excessive MLSS concentration leads to solids/liquidseparation issues in secondary clarifiers or in SBR settling operations. A mixed liquor that contains highconcentrations of unbiodegradable particulate COD will be less effective in treating wastewater thanone that has normal or low levels of unbiodegradable particulate COD on a per unit MLSS basis.Increasingly long sludge age can intensify this condition as the unbiodegradable particulate CODaccumulates in the system. Unbiodegradable particulate COD should not have a significant effect on thepredicted effluent total nitrogen or phosphorus concentration in the regular or BNR-S1 modes.According to Melcer et al. (2003), “…The quality of the data used to calibrate the simulator will have adirect impact on the reliability of the predictions made when using the calibrated simulator.” In order todefine the COD fractions as accurately as possible, data in addition to that typically monitored atwastewater treatment facilities will need to be collected. Recommended data gathering activities43

include 24-hour composite samples taken over a two week period and analyzed for an array ofwastewater parameters. Examples of wastewater parameters to analyze include influent COD, filteredCOD, flocculated and filtered COD, cBOD 5 , filtered cBOD 5 , VSS, TSS, TKN, NH 3 -N, NO 3 — N, Total P, solublereactive P, and alkalinity as well as effluent filtered COD. Grab samples should also be taken on anhourly or bi-hourly basis during the same two week sample collection period (Melcer et al., 2003).4.2 Sensitivity Analysis4.2.1 Regular mode Sensitivity AnalysisThe sensitivity data was analyzed on two levels. First, the percent variation in effluent parametersbetween the adjusted simulator parameter run and the default simulator parameter run were calculatedand graphed. Next, the predicted effluent parameter values for each simulation run in which asimulation parameter was adjusted were compared to the observed effluent parameter values and thepercent error was calculated and graphed for each.4.2.1.1 Ordinary Heterotrophic Organism (OHO) YieldThe heterotrophic yield is the mass of biomass produced (g) per mass of substrate consumed (g). Morespecifically, the BioWin user manual describes OHO yield as the, “Amount of biomass COD producedusing one unit of readily biodegradable complex substrate COD. The remaining COD is oxidized”(EnviroSim, 2010). Substrate (i.e., carbon source) can be used by microorganisms for biomassproduction (synthesis) or for energy to feed metabolism (see Figure 4.3). Aerobic microorganismmetabolism requires oxygen input which serves as the terminal electron acceptor in the energyproducing chemical reactions. Cell synthesis requires energy and carbon but also requires nutrientssuch as N and P. These nutrients are taken from the soluble wastewater substrate and incorporatedinto the newly formed cells. When the biomass is wasted, the nutrients contained in the cells areremoved from the tank. Consider the formula C 12 H 87 O 23 N 12 P as representative of biomass. This means12.2g of nitrogen and 2.3 g of phosphorus are required for every 100 g of biomass produced(Tchobanoglous et al., 2003). A higher yield value means that the biomass produces a greater amountof new cells per gram substrate, and thereby consumes more nutrients per gram substrate consumed.Conversely, when the yield value is reduced, fewer nutrients will be required per gram substrate utilizedbecause fewer new cells are being produced. The substrate is instead consumed for metabolism ratherthan for cell synthesis.Figure 4.3 - Heterotrophic bacteria metabolism (adapted from Tchobanoglous et al., 2003)44

Percent deviation from effluent value at defaultThe simulator output indicates that the heterotrophic (OHO) yield parameter had a significant effect onthe largest number of monitored effluent parameters (Figure 4.4). Effluent parameters that changed bygreater than 30% as a result of changes in the OHO yield simulator parameter include TN, TP, NH 3 -N,NO 3 - -N, and cBOD. The largest percent change of these effluent parameters was by NO 3 - -N with anincrease of 182% in effluent concentration for a 60% decrease in the OHO yield parameter.200150100TNTPNH3-NcBODNO3-N500-50-100-150-80 -60 -40 -20 0 20 40 60 80Percent adjustment of OHO yieldFigure 4.4 - Heterotrophic (OHO) yield sensitivity plot for regular mode (0% represents default OHOyield value)Figure 4.5 compares predicted effluent concentrations at varying percent adjustments of OHO yield withobserved concentrations in the form of a percent deviation value. This shows the corrective power thatOHO yield has on the four sensitive effluent parameters. It is possible to fully correct either NO 3 - -N or TPeffluent concentrations by adjusting OHO yield. The value of OHO yield that corrects effluent NO 3 - -Nconcentration over-predicts the effluent TP concentration (predicts greater removal of P than wasobserved). This indicates that other simulator parameters need to be adjusted in order to effectivelycalibrate the simulator.Although effluent NH 3 -N concentration appeared to be sensitive to changes in OHO yield value (Figure4.4), the relative magnitude of the changes in effluent NH 3 -N concentrations are not effective in thecalibration of the predicted to observed values (Figure 4.5).45

Perecent deviation (predicted - observed)604020TNNO3-NTPNH3-N0-20-40-60-80-100-120-80 -60 -40 -20 0 20 40 60 80Percent adjustment of OHO yieldFigure 4.5 - Percent error plot for OHO yield for regular mode (0% represents default OHO yield value)4.2.1.2 Maximum Specific Growth Rate DiscussionThe maximum specific bacterial growth rate is defined as the grams of new cells generated per gram ofcells per day. Ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) maximum specificgrowth rates have a direct effect on a system’s capacity for nitrification (BioWin user manual). For theIAWPRC ASM1 model, nitrification maximum specific growth rate kinetic coefficient values can varywidely and are directly related to aeration tank volume requirements and SRT (Tchobanoglous et al.,2003). Rate coefficients have been reported in the range of 0.20-0.90 g VSS/g VSS/d (Tchobanoglous etal., 2003).Barker and Dold (1997) found marked variations in the maximum specific growth rate of nitrifiersbetween systems treating different wastewaters. The quantity of industrial input to the wastewaterappears to correlate with the extent of variation observed. This leads to the suggestion that increasedindustrial inputs can lead to an increased risk of nitrification organism inhibition (Barker and Dold,1997).The industrial input in Grundy Center may cause a change in the maximum specific growth rate of AOBand NOB. The flows and loadings of the industry appear to be highly variable, which at a smaller sizedplant, could have significant impacts to the kinetics of the activated sludge microorganisms.4.2.1.3 Ammonia Oxidizing Bacteria (AOB) Maximum Specific Growth RateThe AOB maximum specific growth rate results are shown in Figure 4.6. It appears that increasing AOBmax specific growth rate has much less influence on effluent parameters than decreasing its value. Themodel was run at 13 day SRT. At 13 day SRT, the default AOB maximum specific growth rate is adequate46

Percent deviation from effluent value atdefaultfor nearly 100% nitrification efficiency. Therefore, increasing the AOB maximum specific growth rateabove the default value can have no impact on nitrification efficiency. A reduction of the AOB maximumspecific growth rate would simulate a growth inhibition. Effluent ammonia was the parameter with thegreatest percent change. This concept of increasing effluent ammonia concentration with decreasedAOB growth rate is similar to the critical SRT concept in the activated sludge process design. If the solidsretention time is less than the net AOB growth rate, the effluent ammonia concentration will increase.Loss of mixed liquor suspended solids that results in an SRT less than the critical SRT is referred to as“solids washout.” The effluent ammonia concentration increased by 13,580% (essentially nonitrification) for a reduction of 60% to the AOB maximum specific growth rate. All lines trend smoothlywith one exception; the percent NO 2 - -N concentration decreases sharply from -50% to -60% AOBmaximum specific growth rate. The cause for the increase in nitrite percent deviation with decreasedAOB maximum specific growth rate is not known.18001600140012001000800600NO2-NNO3-NTNNH3-N4002000-200-80 -60 -40 -20 0 20 40 60 80Percent adjustment of AOB maximum specific growth rateFigure 4.6 - AOB maximum specific growth rate sensitivity plot for regular mode. NH 3 valuescorresponding to -50% and -60% simulator parameter values are 8600% and 13580%, respectively. (0%represents default AOB maximum specific growth rate value in simulator)The percent error plot for AOB maximum specific growth rate shows that it is possible to fully correcteither TN or NH 3 -N effluent concentrations by reducing the AOB maximum specific growth rate by 30-50% (Figure 4.7). The increase in NO 2 - -N concentration is not significant enough to raise it to observedlevels for the ±60% range evaluated. Also, compared to observed values, NO 3 - -N effluent concentrationswere affected negatively by decreasing the AOB maximum specific growth rate by greater than 40%.47

Percent deviation (predicted-observerved)100500-50TNNO2-NNO3-NNH3-N-100-150-80 -60 -40 -20 0 20 40 60 80Percent adjustment of AOB maximum specific growth rateFigure 4.7 - AOB maximum specific growth rate percent error plot for regular mode. NH 3 valuescorresponding to -50% and -60% simulator parameter values are 850% and 1400%, respectively. (0%represents default AOB maximum specific growth rate value in simulator)4.2.1.4 Nitrite Oxidizing Bacteria (NOB) Maximum Specific Growth RateNitrite oxidizing biomass (NOB) maximum specific growth rate results are presented in Figure 4.8. Fordecreasing values of NOB maximum specific growth rate, a sharp increase in NO 2 - -N concentrations anda significant decrease in TP concentrations were observed in the effluent. The increase in nitriteconcentrations is expected because if the growth rate of the bacteria responsible for converting nitriteto nitrate is significantly reduced, fewer of those bacteria will be present to carry out the reactions and agreater concentration of nitrite will result. This is similar to the critical SRT concept discussed in theprevious section (4.2.1.3) for AOB maximum specific growth rate. A proportional decrease in NO 3 - -Nconcentration is observed as the NO 2 - -N concentration increases. A proportional decrease in TPconcentration is also observed for an increasing NO 2 - -N concentration. The cause of this relationshipbetween TP concentration and NOB maximum specific growth rate is not known.No significant changes were observed in the effluent parameters as NOB maximum specific growth rateis increased beyond the default value. Similar to AOB maximum specific growth rate, this indicates thatthe simulator is achieving the maximum possible nitrification process efficiency by the AOBs at defaultsettings and at the 13 day SRT process operating point. No further improvements in effluent quality canbe made by increasing AOB maximum specific growth rate.48

Percent deviation from effluent value atdefaultPercent deviation from effluent value atdefault200-20-40-60-80TPNO3-N-100-80 -60 -40 -20 0 20 40 60 80Percent adjustment of NOB maximum specific growth rateFigure 4.8 - NOB maximum specific growth rate sensitivity plot for regular mode for TP and NO 3 - -Neffluent parameters (0% represents default AOB maximum specific growth rate value in simulator)1200010000800060004000NO2-N20000-80 -30 20 70-2000Percent adjustment of NOB maximum specific growth rateFigure 4.9 - NOB maximum specific growth rate sensitivity plot for regular mode for NO 2 - -N effluentparameter (0% represents default AOB maximum specific growth rate value in simulator)NOB maximum specific growth rate percent error plot shows that either TP or NO 2 - -N concentrationscan be corrected by adjusting the NOB maximum specific growth rate parameter value (Figure 4.10).49

Percent deviaton in value (model-observerved)However, NO 3 -N concentration is negatively affected by decreasing the AOB maximum specific growthrate.40200-20-40-60-80NO2-NTPNO3-N-100-120-80 -60 -40 -20 0 20 40 60 80Percent adjustment of NOB maximum specific growth rateFigure 4.10 - NOB maximum specific growth rate percent error plot for regular mode (0% representsdefault NOB maximum specific growth rate value in simulator)4.2.1.5 Maximum Vesilind Settling Velocity DiscussionThe BioWin process simulation model includes three types of settling models: point, ideal, and fluxbased models. Point separation models require the user to specify the percent of solids captured on amass basis. The incoming solids are separated into two streams (thickened stream, and clarified stream)by a simple mass balance according to the user specified solids capture efficiency (BioWin user manual).The ideal model for separation is similar to the point model with one major exception; the ideal modeltakes volume into account whereas the point model does not.BioWin contains two flux-based separation models: Modified Vesilind and Double Exponential. Allmodeling for this report used the Modified Vesilind model. More information on the DoubleExponential model is included in the BioWin user manual. The Modified Vesilind flux-based separationmodel applies mass balances in a layered approach, focusing only on the vertical movement of solids.Figure 4.11 shows a representation of how BioWin implements a layered approach in the ModifiedVesilind settler model. There are essentially three different zones in the settling tank: the zone abovethe feed zone, the feed layer, and the zone below the feed layer. Influent flow will disperse to zonesabove and below the influent feed layer. Gravitational settling pulls solids down across all layers. Thesettling velocity in a given layer is calculated using equation 7.50

Figure 4.11 - Demonstration of layered method in modified Vesilind settler model (BioWin user’smanual)V S,i = V 0 e -KXi [7]Where V s,I = the velocity in a given layer i, V 0 = maximum settling velocity (m/d), K = settling parameter(m 3 /kg TSS), and X i = the TSS concentration (kgTSS/m 3 ) in layer i. (BioWin user’s manual)When the maximum settling velocity is reduced, the settling velocity in each layer will be reduced. If thesettling velocity in the top layer is reduced (where decant water is taken from), the effluent TSSconcentration will increase because the solids are not progressing down into the tank as far as theypreviously were for the same duration of settling period.4.2.1.6 Maximum Vesilind Settling VelocityA decrease in the maximum Vesilind settling velocity results in a significant increase in effluentconcentrations of TSS, cBOD, COD, NO 2 - -N, NO 3 - -N, and TN (Figure 4.12). On the other hand, increasesto the maximum Vesilind settling velocity result in relatively minor changes in effluent concentrationsfrom default operating parameter settings. This means that the default settling velocity is currently setat a rate where no further significant gains in effluent quality can be achieved through increasedmaximum settling velocity parameter at the 1 hour 48 minute SBR settling and decant phase duration.51

Percent deviation in value (modelobserverved)Percent deviation from effluent value atdefault500400300TSScBODCODNH3-NTNNO2-N2001000-100-80 -60 -40 -20 0 20 40 60 80Percent adjustment of maximum Vesilind settling velocityFigure 4.12 - Maximum Vesilind settling velocity sensitivity plot for regular mode (0% represents defaultmaximum Vesilind settling velocity value in simulator)The percent error plot shows that TSS is the only parameter that can be corrected to observed values byadjusting maximum Vesilind settling velocity (Figure 4.13). A decreasing maximum velocity significantlyincreases the effluent TSS concentration.250200150100500-50TNNH3-NNO2-NTSS-100-150-80 -60 -40 -20 0 20 40 60 80Percent adjustment of maximum Vesilind settling velocityFigure 4.13 - Maximum Vesilind settling velocity percent error plot for regular mode (0% representsdefault maximum Vesilind settling velocity value in simulator)52

4.2.2 BNR-S1 Sensitivity AnalysisIn addition to the regular mode sensitivity analysis, a less intensive analysis was performed on the BNR-S1 mode. This was deemed necessary for the BNR-S1 simulator calibration because of the differencesbetween the regular and BNR-S1 mode operating phases. The BNR-S1 mode contains an anaerobicphase and the regular mode does not. Also, the BNR-S1 mode contains alternating oxic-anoxic phaseswhich are not included in the regular mode. An analysis of the complete list of simulator parametersgiven in Table 3.5 was completed at values of +20% and -60% of the simulator parameter default values.A table containing the results of this analysis is included in Appendix II. Including the parameterscovered in the regular mode sensitivity section above, two additional operating parameters showedsignificant importance to the calibration of the BNR-S1 mode. These two simulator parameters are theheterotrophic kinetic parameter- substrate ammonia half saturation concentration, and the PAOstoichiometric parameter- aerobic P/PHA uptake. Also, an increase of 20% in the PAO stoichiometricparameter P/Ac release ratio showed an 18% change in effluent TP concentration.4.2.2.1 Substrate (NH 4+) half-saturation concentrationThe ammonia half-saturation concentration is the concentration of ammonia that supports an uptakerate of one-half the maximum rate. The BioWin user manual simply explains the substrate (NH 4 + ) halfsaturationconcentration as impacting the residual ammonia concentration in the effluent (BioWin usermanual). The manual also notes that the parameter value is typically low for municipal plants. Thisassumption may not apply since the Grundy Center wastewater treatment plant receives industrialwaste.Substrate half saturation concentration can be related to the maximum specific growth rate of AOBsthrough the typical equation for substrate utilization rate, (equation 8). Equation 7 is from Metcalf &Eddy’s Wastewater Treatment and Reuse text and is a general equation which may not describe theexact process in BioWin. This equation can be used to describe COD consumption by heterotrophicbacteria or NH 3 consumption by autotrophic AOBs.For autotrophic AOBs, = substrate utilization rate, g NH 3 /m 3 /d= maximum specific growth rate of AOBs, g new cells/g cells/d= AOB biomass concentration, g/m 3= growth limiting substrate concentration, g NH 3 /m 3= true yield coefficient, g new cells/g NH 3 consumption= NH 3 half-saturation constant, g/m 3The range for ammonia half-saturation values has been reported to be between 0.5-1.0 g NH 4 + -N/m 3(Tchobanoglous et al., 2003).[8]53

4.2.2.2 Aerobic P/PHA uptake and P/Ac release ratioThe aerobic P/PHA uptake parameter is described by the BioWin user manual as the “Amount of Pstored per unit PHA oxidized in aerobic conditions” (EnviroSim, 2010). The P/Ac release ratio parameteris explained as the, “Amount of P released for one mg (milligram) of acetate sequestered in the form ofPHA” (EnviroSim, 2010).As previously discussed, enhanced biological phosphorus removal (EBPR) involves a number of stepsincluding alternating oxic-anaerobic phases in order to successfully achieve enhanced phosphorusuptake. During the anaerobic phase, the hydrolysis of poly P releases phosphorus from the cells into thetank. Then, during the aerobic phase PAOs use stored electrons as energy to “invest” in the storage ofpoly P (Rittman and McCarty, 2001). In order for EBPR to be successful, more P must be taken up duringthe aerobic phase than released during the anaerobic phase. The initial consensus was that in order tooptimize EBPR, the anaerobic P release must be minimized. This was attempted by boosting volatilefatty acids and other RBCOD components in order to limit the amount of P released back into the tankduring the anaerobic phase.A full-scale study completed by Narayanan et al. (2006) suggests that aerobic P uptake is more critical tothe success of the process than anaerobic P release. The researchers never observed P release as alimitation on the process performance through all testing completed. Further, the researchers foundnearly all performance upsets were linked to a disruption of aerobic P uptake. Either parameter couldbe used to adjust the TP effluent concentration in BioWin. Aerobic P/PHA uptake was selected as theparameter to adjust effluent TP concentration for the BNR-S1 mode based on the research workcompleted by Narayanan et al. (2003).4.3 Simulator CalibrationData obtained from sensitivity analyses were used to calibrate the regular mode and BNR-S1 mode tothe respective observed data obtained by Ersu et al. (2008). According to WEF MOP No. 31, deviationsof predicted values from observed values of 10 to 40% are not uncommon for dynamic simulations. As ageneral rule, acceptable deviations of predicted values from observed values were those that were lessthan 20%, preferably less than 10% with a few exceptions discussed below.4.3.1 Regular Mode CalibrationCalibration for the regular mode focused on fitting the model of observed TSS, TN, NH 3 -N, NO 2 - -N, andNO 3 - -N effluent concentrations. Table 4.1 contains a comparison of observed values from Ersu et al.(2008)Table 4.1- Regular mode effluent quality from default simulator parameter settingsTSS pH cBOD TN NH 3-N NO 2 - -N NO 3 - -N TP COD F. CODObserved (mg/L) 15.1 6.8 - 19 1.5 3.4 10.5 6 - 26.7Default (mg/L) 8.81 6.94 4.31 7.63 0.16 0.04 4.55 6.46 37.15 26.33% deviation -41.66 2.06 - -59.84 -89.33 -98.82 -56.67 7.67 - -1.3954

Sum absolute value percent deviation from observedFirst, maximum Vesilind settling velocity was adjusted to -30% of its default value in order to correct theTSS concentration. This was determined directly from sensitivity analysis data collected for the regularmode. Next, OHO yield, AOB maximum specific growth rate, and NOB maximum specific growth ratewere chosen to be adjusted in order to reduce the deviation between the simulator prediction andobserved values. OHO yield was chosen because of the sensitivity shown in TN and NO 3 - -N. AOBmaximum specific growth rate was chosen for its effects on effluent NH 3 -N and TN concentrations. NOBmaximum specific growth rate was chosen because of its influence on NO 2 - -N concentrations. The threeparameters were evaluated in combinations in which two parameters were varied jointly against thethird. The best calibration progression is shown in Figure 4.14. See Appendix II for details on the othercombinations evaluated. Based on data from the sensitivity analysis, effluent parameters were notsufficiently sensitive to any other evaluated simulator parameters.5004003002001000-60-50-40-30-20-10Percent adjustment OHO yield00-10-20-30-40Percent adjustmentAOB and NOBmaximum specifcgrowth rate400-500300-400200-300100-2000-100Figure 4.14 - Progression of regular mode calibrationCalibrated simulator parameters are summarized in Table 4.2. Typical ranges for simulator parametervalues specific to BioWin could not be located but comparisons can be made to general activated sludgemodel parameter ranges. Reported maximum specific growth rates for nitrifiers range from 0.2 to 1.0d -1 (at 20 o C) (Melcer et al., 2003). The calibrated values of AOB and NOB maximum specific growth ratesfall well within this range. Typical OHO yield values with units of mgCOD/mgCOD could not be located.Also, typical maximum Vesilind settling velocity values could not be found.55

Calibrated simulator parameters for regular modeParametersDefaultValueCalibratedValuePercentChangeUnitsStoichiometricHeterotrophic yield (OHO) 0.666 0.5128 -23 mgCOD/mgCODKineticAOB max. spec. growth rate 0.9 0.603 -33 1/dNOB max. spec. growth rate 0.7 0.469 -33 1/dSettlingMax. Vesilind settling velocity 170 119 -30 (m/d)Effluent quality characteristics for the calibrated simulation run for the regular SBR mode aresummarized in Table 4.3. Effluent TSS, NH 3 -N, NO 2 -N concentrations were improved markedly fromdefault simulator settings. Calibration efforts were unsuccessful in correcting the effluent TN and NO 3 - -N concentrations to within 20% of the observed values. Predicted effluent TN concentration currentlyhas nearly 50% error. Predicted effluent NO 3 - -N concentrations are actually further from observedvalues at nearly 89% error. The increase in deviation for effluent NO 3 - -N is the unintended result fromcorrecting the other monitored parameters, namely TN.The simulator appears to not have the flexibility that is required to achieve a match to observed data foreffluent TN and NO 3 - -N concentrations by adjusting simulator parameters only. This difficulty could alsobe attributed to inadequate or erroneous input data on raw wastewater and effluent characteristics.Increased flexibility in wastewater simulators has advantages and disadvantages. On one hand, a veryflexible simulator would be able to achieve a match to nearly any data set, no matter how erroneous itmay be. While the ease of calibration may be helpful from one point of view, major mistakes in processmodeling become inherently possible if the model is forced to fit available process performance datawithout considering basic process fundamental relationships.Table 4.3- Regular mode effluent quality for calibrated simulator parameter settingsTSS pH cBOD TN NH 3-N NO - 2 -N NO - 3 -N TP COD F. CODObserved (mg/L) 15.1 6.8 - 19 1.5 3.4 10.5 6 - 26.7Calibrated (mg/L) 14.82 6.93 5.94 9.74 1.45 3.74 1.18 5.93 43.84 26.45% deviation -1.84 1.95 - -48.71 -3.34 10.08 -88.78 -1.23 - -0.924.3.2 BNR-S1 Mode CalibrationTable 4.4 is a comparison of the calculated effluent quality at the default simulator settings for the BNR-S1 mode to observed data obtained by Ersu et al. (2008). Effluent parameters requiring the greatestcorrection include NH 3 -N, NO 2 - -N, NO 3 - -N, and TP. The simulated over predicted NH 3 -N and NO 2 - -Nremovals while the concentration of NO 3 - -N was much higher than observed. These disparities mayindicate an unaccounted for inhibition to the nitrification process. The inhibition could be in theammonia oxidation reaction, the nitrite oxidation reaction, or both. The error in NO 3 - -N concentration56

could be due to the reduced nitrite oxidation or possibly the observed denitrification was better thansimulated, or some combination of both was present.The observed effluent TP concentration was much higher than that simulated. This could be due tosomething inhibiting PAOs that is currently not accounted for in the simulator. The over-prediction ofeffluent TP could also be due in part to the way the simulator handles the significantly higher RBCODfraction determined by the influent specifier. This possibility of RBCOD fraction affecting effluent TPconcentrations is investigated further in sub-section 4.5.2.Total nitrogen, the effluent parameter that was most difficult to adjust in regular mode, was alreadywell within the 20% margin for error at 11.4% for default simulator parameter values for the BNR-S1 SBRmode.Table 4.4- BNR-S1 mode effluent quality for default simulator parameter settingsTSS pH cBOD TN NH 3-N NO - 2 -N NO - 3 -N TP COD F. CODObserved (mg/L) 7.9 6.9 - 6.6 1.4 1.3 2.6 1.5 - 29.3Default (mg/L) 5.6 7.0 3.4 7.4 0.3 0.1 4.7 0.3 40.1 33.3% deviation -29.3 1.9 11.4 -75.5 -89.6 81.0 -78.9 13.7Table 4.5 compares observed effluent quality for the BNR-S1 mode to predicted effluent quality for theBNR-S1 mode using the regular mode calibrated simulator parameter values. Improvements fromdefault predictions were made to TSS, NO 2 - , TP and filtered COD. Regular mode calibrated simulatorparameters created increased deviations from observed values in TN and NO 3 - .Table 5.5- BNR-S1 mode effluent quality with calibrated regular mode simulator parametersTSS pH cBOD TN NH3-N NO2-N NO3-N TP COD F. CODObserved (mg/L) 7.9 6.9 - 6.6 1.4 1.3 2.6 1.5 - 29.3regular mode calibration (mg/L) 8.9 7.0 3.9 11.4 0.7 1.7 6.6 1.1 42.3 31.9% deviation 12.2 1.4 72.4 -47.8 29.8 153.7 -25.1 8.9Calibration of the BNR-S1 mode built upon the calibration of the regular mode in that the first step ofthe BNR-S1 mode calibration was to optimize the settings for OHO yield and NOB and AOB maximumspecific growth rate (Figure 4.15). Similar to the regular mode, these three parameters had the mostsignificant effects on the largest number of effluent parameters. Side effects of simulator parameteradjustment were greatly reduced by optimizing these three first before adjusting the simulatorparameters with more targeted effects. The simulator parameters that yielded more targeted effluentparameter concentration changes and the effluent parameter they target include maximum Vesilindsettling velocity for effluent TSS, AOB substrate half-saturation concentration for effluent NH 3 , andaerobic P/PHA uptake for effluent TP.57

Sum absolute value of percent deviation fromobserved5004003002001000-15-10-50Percent adjustment from default OHO yield5-20-25-30-35-40Percent adjustmentfrom default AOBand NOB maximumspecific growth rate400-500300-400200-300100-2000-100Figure 4.15 - BNR-S1 mode calibration progressionCorrection of effluent TSS concentration was accomplished by adjusting the maximum Vesilind settlingvelocity (Figure 4.16). Adjustments of -10% to -30% give a TSS value with less than 20% error. A value of-25% of the default value for maximum Vesilind settling velocity was selected because this parametervalue gave the smallest amount of error in effluent TSS.58

Absolute value of percent deviationfrom observedAbsolute value of percent deviationfrom observed252015105TSS0-40% -30% -20% -10% 0%Percent adjustment of max. Vesilind settling velocityFigure 4.16 - Effluent TSS calibration for BNR-S1 mode by adjustment of maximum Vesilind settlingvelocity (0% represents default maximum Vesilind settling velocity value in simulator)The substrate (NH 4 + ) half-saturation concentration was adjusted to correct the effluent NH 3concentration. A side effect of adjusting the substrate (NH 4 + ) half-saturation concentration is asignificant decrease in the effluent NO 3 concentration (Figure 4.17). This decrease in effluent NO 3 - -Nresults in an increase in percent error for NO 3 - -N. A choice needed to be made between minimizing theerror in NH 3 -N while increasing the error of another NO 3 - -N versus minimizing the error in bothparameters, but the error in NH 3 -N to a lesser extent than the first option. The latter was chosenbecause it placed the error in both NH 3 -N and NO 3 - -N below 20%.504540353025201510500% 20% 40% 60% 80% 100%Percent adjustment of substrate (NH 4+ ) half-saturationconcentrationNH3NO3Figure 4.17 - Effluent NH 3 -N concentration calibration for BNR-S1 mode by adjustment of AOB substratehalf-saturation concentration (0% represents default substrate (NH 4 + -N) half-saturation value insimulator)59

Absolute value of percent deviationfrom observedEffluent TP concentrations were corrected by adjusting the aerobic P/PHA uptake rate (Figure 4.18). Anadjustment of -19% to aerobic P/PHA uptake gave the lowest percent error at 2.7% for effluent TP.25020015010050TP0-40% -30% -20% -10% 0%Percent adjustment of aerobic P/PHA uptakeFigure 4.18 - Effluent TP concentration calibration for BNR-S1 mode by adjustment of PAO aerobicP/PHA uptake parameterCalibrated simulator parameters for BNR-S1 mode are summarized in Table 4.6. The BNR-S1 moderequired adjustments to two additional simulator parameters (aerobic P/PHA uptake and substrate(NH 4 + ) half-saturation concentration) in order to correct the simulated effluent concentrations toobserved values. The simulator default values for OHO yield, aerobic P/PHA uptake, AOB maximumspecific growth rate, NOB maximum specific growth rate, and maximum Vesilind settling velocity wereall revised (reduced) because the predicted TSS, NH 3 -N, NO 2 -N and TP removal efficiencies were too highwhen using the default values. In order to increase the predicted effluent NH 3 -N concentrations toconform with observed concentrations, the substrate (NH 4 + ) half-saturation concentration wasincreased significantly.Table 4.6- Calibrated simulator parameters for BNR-S1 modeParametersDefaultValueCalibratedValuePercentChangeUnitsStoichiometricHeterotrophic yield (OHO) 0.666 0.6327 -5 mgCOD/mgCODAerobic P/PHA uptake (PAO) 0.95 0.7695 -19 mgP/mgCODKineticAOB max. spec. growth rate 0.9 0.630 -30 1/dSubstrate (NH + 4 ) half-saturation 0.7 1.05 50 mgN/LNOB max. spec. growth rate 0.7 0.49 -30 1/dSettlingMax. Vesilind settling velocity 170 127.5 -25 (m/d)60

Effluent characteristics for BNR-S1 mode with calibrated simulator parameters are given in Table 4.7. Allsimulator effluent parameters fall within 20% error of observed values with one exception, NH 3 -N whichhas an error of -21.8%. However, this is only a difference of 0.3 mg/L.Table 4.7- BNR-S1 mode effluent quality for calibrated simulator parametersTSS pH cBOD TN NH 3-N NO 2 - -N NO 3 - -N TP COD F. CODObserved 7.9 6.9 - 6.6 1.4 1.3 2.6 1.5 - 29.3Calibrated 8.2 7.0 4.1 6.9 1.1 1.2 2.3 1.5 42.9 32.9% deviation 3.8 2.1 4.7 -21.6 -8.8 -11.6 2.7 12.24.3.3 Evaluation BNR-S2 with BNR-S1 Calibrated ParametersTable 4.8 compares observed effluent characteristics with predicted effluent characteristics usingdefault simulator parameter values for the BNR-S2 mode. Significant deviations were found for effluentNH 3 -N, NO 2 — N, NO 3 — N, and TP.Table 4.8- BNR-S2 (20.1 o C) mode default effluent qualityTSS pH cBOD TN NH 3-N NO 2 - -N NO 3 - -N TP COD F. CODObserved (mg/L) 4.7 7.2 - 3.5 0.8 0.8 1.9 1.6 - 35Default (mg/L) 5.2 7.0 2.5 3.2 0.4 0.4 0.2 0.2 44.9 38.0% deviation 10.2 -2.1 -8.1 -45.8 -45.4 -89.1 -89.2 8.5sum absolute value % deviation = 298Table 4.10 compares observed effluent characteristics with predicted effluent characteristics using BNR-S1 calibrated simulator parameter values for the BNR-S2 mode. Significant deviations were found foreffluent TSS, TN, NH 3 -N, NO 2 — N, NO 3 — N, and TP. These results indicate that calibrated BNR-S1 simulatorparameters increase deviation between observed and predicted values over default simulatorparameter values. The sum absolute value of percent deviation increases from 298 for default simulatorparameter values to 516 for BNR-S1 calibrated simulator parameter values.Table 4.9- BNR-S2 (20.1 o C) mode calibrated effluent qualityTSS pH cBOD TN NH 3-N NO 2 - -N NO 3 - -N TP COD F. CODObserved 4.7 7.2 - 3.5 0.8 0.8 1.9 1.6 - 35BNR-S1 mode calibration 7.6 7.1 2.9 5.2 2.3 0.6 0.1 0.3 48.1 37.9% deviation 62.0 -2.0 49.6 186.3 -30.0 -94.3 -83.2 8.3sum absolute value % deviation = 516Tables 4.9 compares observed effluent characteristics with predicted effluent characteristics usingdefault simulator parameter values for the BNR-S2 (colder) mode. Significant deviations were found foreffluent TSS, TN, NH 3 -N, NO 2 — N, NO 3 — N, and TP.61

Table 4.10- BNR-S2 colder (15.7 o C) mode default effluent qualityTSS pH cBOD TN NH 3-N NO - 2 -N NO - 3 -N TP COD F. CODObserved 4.1 - - 7.7 0.6 1.7 5.4 2.1 - 34.7Default 5.5 6.9 3.7 3.7 1.0 0.3 0.1 0.2 46.9 40.0% deviation 33.6 -52.4 63.2 -80.1 -98.0 -89.0 15.2sum absolute value % deviation = 432Table 4.11 compares observed effluent characteristics with predicted effluent characteristics using BNR-S1 mode calibrated simulator parameter values for the BNR-S2 colder mode. Significant deviations werefound for effluent TSS, NH 3 -N, NO 2 - -N, NO 3 - -N, and TP. Default simulator parameter predictions gave alower sum of deviations from observed values than BNR-S1 mode calibrated simulator parametervalues. Calibrated simulator parameter values gave a better prediction for effluent TN concentration.The effluent ammonia concentration had the largest deviation.Table 4.11- BNR-S2 colder (15.7 o C) mode calibrated effluent qualityTSS pH cBOD TN NH 3-N NO - 2 -N NO - 3 -N TP COD F. CODObserved 4.1 - - 7.7 0.6 1.7 5.4 2.1 - 34.7BNR-S1 mode calibration 8.1 7.0 4.6 7.8 5.2 0.2 0.0 0.4 50.5 40.2% deviation 97.7 1.6 763.8 -89.3 -99.1 -81.1 16.0sum absolute value % deviation = 1122Overall, the BNR-S2 mode at 20.1 o C was better predicted by BioWin than the BNR-S2 mode at 15.7 o C.This difference could be the result of an error in the temperature correction equations or another factorattributed to temperature change that is not accounted for currently in the model. The betterpredictions may be the result of data collection or some other factor and is independent oftemperature.These results indicate that it is not appropriate to use calibrated simulator parameter values from onemode on a different mode without further calibration. The results also signal the dangers of adjustingsimulator parameters away from default values. The calibrated simulator parameters gave effluent NH 3 -N concentrations that varied significantly from observed concentrations. This implies that theadjustment of substrate (NH 4 + ) half-saturation concentration may not have been justified. The increasein effluent ammonia concentration may be the result of some other unaccounted for effect. The finalconclusions can be drawn without further data collection, simulation analysis, and hypothesis testing.4.4 Effect of SBR WidthAs previously mentioned in section 3.4.1, an error was discovered in the dimensions of the SBR as theyare defined by the BioWin user manual. The length of the SBR is used as the width dimension in BioWin62

to define the distance from the inflow pipe to the decanter (Figure 4.19). The length of the SBR is thendivided into the three equal sized zones. This error was discovered near the end of the calibration ofBNR-S1 mode and the width parameter was corrected. Next, an analysis was completed on the regularmode calibration and BNR-S1 calibration to identify any differences in effluent parameters caused by thechange in width.Figure 4.19 - Flow distribution in single tank SBR (from BioWin user manual)4.4.1 Effects on Regular ModeNo significant changes in effluent parameters for the regular mode with default and calibrated simulatorparameter settings were observed after correcting the width to 13.8 m (Table 4.12). Based on theseresults, it was determined that the change 1.2 m in width had no significant effects on the regular mode.Table 4.12- Width adjustment effects on regular mode effluent quality aWidth (m) TSS pH cBOD TN NH 3-N NO 2 - -N NO 3 - -N TP COD F. CODDefault Simulator Parametersoriginal, 12.6 8.8 6.9 4.3 7.6 0.2 0.04 4.6 6.5 37.1 26.3corrected, 13.8 8.8 6.9 4.3 7.6 0.2 0.04 4.6 6.5 37.2 26.3Calibrated Simulator Parametersoriginal, 12.6 14.8 6.9 5.9 9.7 1.4 3.7 1.2 5.9 43.8 26.5corrected, 13.8 14.9 6.9 6.0 9.8 1.5 3.7 1.2 5.9 43.9 26.5a concentrations given as mg/L4.4.2 Effects on BNR-S1 ModeNo significant changes in effluent parameters for the BNR-S1 mode with default and calibrated simulatorparameter settings were observed after correcting the width to 13.8 m (Table 4.13). Based on theseresults, it was determined that the change 1.2 m in width had no significant effects on the BNR-S1mode.63

Table 4.13- Width adjustment effects on BNR-S1 mode effluent quality aWidth (m) TSS pH cBOD TN NH 3-N NO - 2 -N NO - 3 -N TP COD F. CODDefault Simulator Parametersoriginal, 12.6 5.6 7.0 3.4 7.4 0.3 0.1 4.7 0.3 40.1 33.3corrected, 13.8 5.6 7.0 3.4 7.4 0.3 0.1 4.7 0.3 40.1 33.3Calibrated Simulator Parametersoriginal, 12.6 8.2 7.0 4.1 6.9 1.1 1.2 2.3 1.3 43.0 33.0corrected, 13.8 8.2 7.0 4.1 6.9 1.1 1.2 2.3 1.5 42.9 32.9a concentrations given as mg/L4.5 Effects of COD FractionsIn order to gauge the importance of influent wastewater characterization on the monitored effluentparameters, a comparison between default and calibrated settings for COD fractions was completed.4.5.1 Regular ModeA comparison of effluent concentration obtained from using default COD fractions and COD fractionscalculated using the influent specifier is given in Table 4.14. Effluent parameters that changed by morethan 1.0 mg/L included TP, COD, and filtered COD. The TP concentration increased by 2.0 mg/L for thecalibrated settings, most likely due to the differences in RBCOD fractions. For the regular mode,conditions are not favorable for PAOs. Therefore, the effect of increased RBCOD has no impact onphosphorus uptake by PAOs. In the regular mode, biomass uptake is the primary mode of partialphosphorus removal.Table 4.14- Influent wastewater COD fractions effects on regular mode effluent quality bTSS pH cBOD TN NH 3-N NO 2 - -N NO 3 - -N TP COD F. CODObserved 15.1 6.8 - 19 1.5 3.4 10.5 6 - 26.7Default Simulator ParametersDefault COD fractionsmg/L 8.9 6.9 4.5 7.3 0.0 0.0 4.5 6.8 40.4 29.2% deviation -41 1 -61 -97 -100 -57 13 9Adjusted COD fractionsmg/L 8.8 6.9 4.3 7.6 0.2 0.0 4.6 6.5 37.2 26.3% deviation -41 2 -60 -89 -99 -57 8 -1Calibrated Simulator ParametersDefault COD fractionsmg/L 14.8 6.9 6.2 9.5 1.2 3.8 1.2 7.9 47.8 29.2% difference b -2 1 -50 -18 12 -89 31 9Adjusted COD fractionsmg/L 14.9 6.9 6.0 9.8 1.5 3.7 1.2 5.9 43.9 26.5% deviation -1 2 -49 -3 10 -89 -1 -1b % deviation = [(calculated - observed)/ observed] * 10064

4.5.2 BNR-S1 ModeA comparison of effluent concentration obtained from using default COD fractions and COD fractionscalculated using the influent specifier is summarized in Table 4.15. Adjusted COD fractions improvepredictions for effluent filtered COD concentrations for simulations using default and calibratedsimulator parameters. Deviations from observed values for filtered COD were reduced from around50% to 12-14%. This indicates that COD fractions correct filtered COD to observed values.Improvements in deviation from observed effluent values were also observed for effluent ammonia andTP for simulations using calibrated simulator parameters. These results indicate that effluent ammoniaand TP concentrations are more sensitive to COD fractions when calibrated simulator parameters areused in place of default simulator parameter values. This relationship is likely due to the increasedRBCOD fraction from default value for calculated COD frations.Table 4.15- Influent wastewater COD fractions effects on BNR-S1 mode effluent quality bWidth (m) TSS pH cBOD TN NH 3-N NO 2 - -N NO 3 - -N TP COD F. CODObserved (mg/L) 7.9 6.9 - 6.6 1.4 1.3 2.6 1.5 - 29.3Default Simulator ParametersDefault COD fractionsmg/L 5.6 7.0 2.8 6.0 0.3 0.1 3.4 0.3 51.0 44.1% deviation -29 2 -9 -81 -90 31 -80 50Adjusted COD fractionsmg/L 5.6 7.0 3.4 7.4 0.3 0.1 4.7 0.3 40.1 33.3% deviation -29 2 11 -75 -90 81 -79 14Calibrated Simulator ParametersDefault COD fractionsmg/L 8.2 7.0 3.5 6.0 0.8 1.0 1.8 0.8 53.8 43.7% deviation 4 2 -10 -43 -21 -30 -44 49Adjusted COD fractionsmg/L 8.2 7.0 4.1 6.9 1.1 1.2 2.3 1.5 42.9 32.9% deviation 0 2 5 -22 -9 -12 3 12b% deviation = [(calculated - observed)/ observed] * 1004.6 Variation of Cycle Phase DurationAn analysis was completed on the BNR-S1 mode to investigate the effects of varying the duration of theanaerobic, anoxic, and oxic periods while maintaining the same 6 hour total cycle length (Table 3.1).Variations in duration were made in pairs; anaerobic and oxic; anoxic and oxic. An increase in oneperiod length resulted in an equal and opposite decrease in length of the other period’s length. This wascompleted for default and calibrated simulator parameters for BNR-S1 mode.4.6.1 Anaerobic-Oxic Phase VariationFigure 4.20 is a plot comparing TN and TP percent removals for default and calibrated simulatorparameter settings for differing oxic and anaerobic period lengths. Calibrated settings show a greatersensitivity to period length than default settings for TP removal. Total phosphorus removal is nearlyconstant for anaerobic/oxic period lengths 60-140 minutes for default settings. Calibrated settings show65

Percent removala linear trend from anaerobic/oxic periods of 40-110 minutes with a more gradual slope from 120-140minutes. These results indicate that the modifications to the simulator parameter default values resultin a significant increase in the impacts of anaerobic period length on effluent phosphorus concentration.The differences in TP removal are probably due to the adjustment made to the aerobic P/PHA uptakeratio for the calibrated settings. The aerobic P/PHA uptake ratio was decreased by 19% meaning theamount of P stored per unit PHA oxidized under aerobic conditions was reduced. In other words, morePHA is required per unit P stored by PAOs. PHA is produced by PAOs during the anaerobic phase. Thedecrease to aerobic P/PHA uptake appears to have increased the importance of PAOs production of PHAduring the anaerobic phase since TP removal decreases as anaerobic period decreases.100959085807570656055500 50 100 150Anaerobic period length (min)Default TNDefault TPCal. TNCal. TPFigure 4.20 - Percent removal plot for BNR-S1 mode for oxic-anaerobic period length variations. Dashedline signifies default period length (90min). Oxic period length (min)= 180 (min) - anaerobic periodlength (min)Total nitrogen removal decreases with decreasing anaerobic phase length from 120- 40 minutes for bothsettings. This indicates that part of the anaerobic period is actually an anoxic period in whichdenitrification limits TN removal. This is probably related to the system’s ability to denitrify duringanaerobic conditions. A reduced amount of NO 3 - is converted to N 2 gas given a shorter anaerobic phase.From 120 minutes to 140 minutes, calibrated and default settings TN removals diverge. Calibrated TNremoval decreases when the anaerobic period is greater than 120 minutes. This is most likely due toincomplete nitrification during the oxic phase. Calibrated settings reduced the system’s ability to nitrifythrough reductions to AOB and NOB maximum specific growth rates.4.6.2 Anoxic-Oxic Phase VariationA plot of the results for combinations of anoxic-oxic period durations is given in Figure 4.21. Similar tothe anaerobic –oxic combinations, TN removal trends the same for calibrated and default settings whileTP does not. Increased TP removal for increasing anoxic period duration was observed for the calibrated66

Percent removalsettings. The cause for this significant variation between the phosphorus effluent concentrations fordefault values and calibrated values was identified. Total nitrogen removal for both settings increasesas anoxic period length increases. This appears to be a function of denitrification, similar to theanaerobic-oxic plot.100959085807570Default TNDefault TPCal. TNCal. TP65600 50 100 150Anoxic period length (min)Figure 4.21 - Percent removal plot for BNR-S1 mode for oxic-anoxic period length variations. Dashedline signifies default period length (90min). Oxic period length (min)= 180 (min) - anoxic period length(min)4.6.3 Phase Variation SummaryIn both analysis the percent removal of TP and TN were maximized when the oxic period length wasminimized. Therefore, the cycle which contained the lowest fraction of oxic phase provided the bestpercent removals. Increasing the anaerobic phase length resulted in great TP removal for the calibratedmodel. Therefore, the optimum anaerobic/anoxic/oxic cycle sequence was 140/90/40 minutes.67

Chapter 5: Conclusion5.1 SummaryBiological nutrient removal is an important wastewater treatment process for removing nitrogen andphosphorus from wastewater before discharge and reducing eutrophication in our water bodies.Optimization of BNR processes may be achieved with the aid of wastewater treatment processsimulators. There is a need for more investigation of practical applications of wastewater simulators toreal world problems. This report proposed the application of the BioWin wastewater simulator to thewastewater treatment facility of the City of Grundy Center, Iowa. The following conclusions were made:• The RBCOD fraction as calculated by the influent specifier for the Grundy Center rawwastewater was greater than typical (default) values. The calculated acetate and non-colloidalslowly biodegradable COD fractions less than default values. A two week intensive samplegathering period should be conducted to ensure the accuracy of values used in the influentspecifier.• Sensitivity analysis on regular and BNR-S1 modes indicated that the three most sensitivesimulator parameters are OHO yield, AOB maximum specific growth rate, and the NOBmaximum specific growth rate. The BNR-S1 mode indicated two additional sensitive simulatorparameters: substrate (NH 4 + ) half-saturation concentration and the aerobic P/PHA uptake ratio.• The simulation of the regular mode did not accurately predict the reported processperformance. The predicted effluent TN and NO 3 - concentrations in the regular modesimulation were not to within 20% of the observed values.• Calibration of the BNR-S1 mode was successful in reducing deviation of effluent concentrationsfrom observed values to less than 20% (except NH 3 at 21.6% deviation).• Default simulator parameter values were found to better predict effluent concentrations thancalibrated simulator parameter values from one mode applied to a different mode, i.e., BNR-S2.Calibrated simulator parameters from one mode (regular mode) were adequate for predictingperformance in another mode (BNR-S1 mode). Recalibration of simulator parameters for thespecific operation mode was required.• COD fractions were determined to have a significant effect on effluent TP but have only minoreffects on other effluent parameter concentrations for the calibrated regular mode.• COD fractions were found to have a significant influence on effluent NH 3 -N, NO 2 - -N, NO 3 - -N, TP,and filtered COD concentrations for the calibrated BNR-S1 mode.• Variation of cycle phase duration was found to have a greater sensitivity in TP removalefficiencies for runs with calibrated simulator parameters than for default simulator parameters.• Variation of cycle phase duration showed improved predicted TN and TP removal efficiencies forincreasing anaerobic and anoxic phase length (decreasing oxic phase length). Based on theanalysis completed for this report, the optimum anaerobic/anoxic/oxic times is 140/90/40.The amount of data produced by a BioWin simulation run is astounding. The quality of the dataproduced needs to be verified before it is used to make any significant changes to processoperations or design.68

5.2 Future WorkFurther investigation could include the following topics:• Develop a range of values for simulator parameters that are found to be acceptable forcalibration of BioWin• Evaluate why default simulator settings are so robust in resisting changes in percent removalsfor TP given the wide range of anoxic and anaerobic period lengths tested in section 4.6• Use calibrated BioWin models with good quality operational data to find optimum SBR cyclesequences• Develop a procedure for characterizing raw wastewater characterisitics (i.e., determine actualCOD fractions)• Identify the stoichiometric, kinetic, and settling parameters that are functions of specificwastewater characteristics (those parameters that should be evaluated with pilot testing orlaboratory analysis for application of the simulator to a specific wastewater or process design)• Develop a procedure for determining the values of the most significant stoichiometric, kinetic,and settling parameters used by simulators69

Chapter 6: ReferencesAlleman, J.E., & Irvine, R.L. (1980) Storage-induced denitrification using sequencing batch reactoroperation. Water Research, 14, 1483-1488.Barker, P.S., & Dold, P.L. (1997) General model for biological nutrient removal activated sludge systems:model presentation. Water Environment Research, 69, 969-984.Barnard, J. (1975) Biological nutrient removal without the addition of chemicals. Water Research, 9, 485.Baroni, P., Bertanza, G., Collivignarelli, C., & Zambarda, V. (2006) Process improvement and energysaving in a full scale wastewater treatment plant: air supply regulation by a fuzzy logic system.Environmental Technology, 27, 733-746.Bernardes, R. S., & Klapwijk, A. (1996) Biological nutrient removal in a sequencing batch reactor treatingdomestic wastewater. Water Science and Technology, 33, 29-38.Chang, H.N., Moon, R.K., Park, B.G., Lim, S.J., Choi, D.W., Lee, W.G., Song, S.L., Yong, H.A. (2000)Simulation of sequential batch reactor (SBR) operation for simultaneous removal of nitrogenand phosphorus. Bioprocess Engineering, 23, 513-521.Coelho, M.A.Z., Russo C., & Araújo, O.Q.F. (2000) Optimization of a sequencing batch reactor forbiological nitrogen removal. Water Resources, 34, 2809-2817.Comeau, Y., Hall, K.J., Hancock, R.E.W., & Oldham, W.K. (1986) Biochemical model for enhancedbiological phosphorus removal. Water Research, 20, 1511-1521.Copp, John B., (1998) COD balances in biological nutrient (nitrogen and phosphorus) removal activatedsludge systems. PhD dissertation, McMaster University.Danesh, S., & Oleszkiewicz, J. (1997) Use of a new anaerobic-aerobic sequencing batch reactor system toenhance biological phosphorus removal. Water Science and Technology, 35, 137-144.Davis, M.L., Cornwell, D.A. (2008) Introduction to Environmental Engineering. New York: McGraw-Hill.Debik, E., Manav, N. (2009) Sequence optimization in a sequencing batch reactor for biological nutrientremoval from domestic wastewater. Bioprocess and Biosystems Engineering, 33, 533-540.Dold, P.L., (1990) A general activated sludge model incorporating biological excess phosphorus removal.In Proc. Annual Conf C.S.C.E., Hamilton, Ontario, Canada.Dold, P.L. (1991) Modification du modèle général des boues activées pour tenir compte de ladéphosphatation. Sciences et Techniques de L’eau, 24, 229.EnviroSim Associates Ltd (BioWin and BioWin User Manual). (2010) Hamilton, Ontario, Canada.www.envirosim.com70

Ersu, C., Arslankaya, E., Ong, S.K., Fox, D., Aldrich, L., Copeman, J. (2008) Modification of a full-scalesequencing batch reactor operational mode for biological nutrient removal. Water EnvironmentResearch, 80, 257-266.Fongsatitkul, P., Wareham, D.G., & Elefsiniotis, P. (2008) The influence of organic loading and anoxictimes on the removal of carbon, nitrogen and phosphorus from a wastewater treated in asequencing batch reactor. Journal of Environmental Science and Health Part A, 43, 725-730.Furumai, H., Kazmi, A.A., Fujita, M., Furuya, Y., & Sasaki, K. (1999) Modeling long term nutrient removalin a sequencing batch reactor. Water Resources, 33, 2708-2714.Grady, C.P.L., Gujer, W., Henze, M., Marais, G.v.R., & Matsuo, T. (1986) A model for single sludgewastewater treatment systems. Water Science and Technology, 18, 47-61.Gujer, W., & Henze, M. (1991) Activated sludge modeling and simulation. Water Science andTechnology, 23, 1011-1023.Hamamoto, Y., Tabata, S., & Okubo, Y. (1997) Development of the intermittent cyclic process forsimultaneous nitrogen and phosphorus removal. Water Science and Technology, 35, 145-152.Henze, M., Grady, C.P.L., Gujer, W., Marais, G.v.R, & Matso, T. (1987a) Activated sludge model no. 1.IAWPRC Scientific Technical Report No. 1, London, UK: International Water Association.Henze, M., Grady, C.P.L., Gujer, W., Marais, G.v.R, & Matso, T. (1987b) A general model for single sludgewastewater treatment systems. Water Research, 21, 505-515.Iowa Department of Natural Resources website; accessed January, 2011.http://www.iowadnr.gov/water/nutrients/index.htmlKalker, T. J. J., van Goor, C. P., Roeleveld, P.J., Ruland, M. F., & Babška, R. (1999) Fuzzy control ofaeration in an activated sludge wastewater treatment plant: design simulation and evaluation.Water Science Technology, 39, 71-78.Kargi, F., Uygur, A. (2004) Hydraulic residence time effects in biological nutrient removal using five-stepsequencing batch reactor. Enzyme and Microbial Technology, 35, 167-172.Lee, D. S., Jeon, C. O., & Park, J. M. (2001) Biological nitrogen removal with enhanced phosphate uptakein a sequencing batch reactor using single sludge system. Water Resources, 35, 3968-3976.Lesjean, B., Gnirss, R., Adam, C., Kraume, M., & Luck, F. (2003) Enhanced biological phosphorus removalprocess implemented in membrane bioreactors to improve phosphorus recovery and recycling.Water Science and Technology, 48, 87-94.Li, B., Irvin, S. (2007) The comparison of alkalinity and ORP as indicators for the nitrification anddenitrification in a sequencing batch reactor (SBR). Biochemical Engineering Journal, 34, 248-255.71

Libra, R. D., Wolter, C. F., and Langel, R. J. (2004) Nitrogen and phosphorus budgets for Iowa and Iowawatersheds (Technical Information Series 47). Iowa Department of Natural Resources.Narayanan, B., Johnson, B., Baur, R., Mengelkoch, M. (2006) Critical role of aerobic uptake in biologicalphosphorus removal. Water Environment Foundation.Neethling, J. B. (2008) Low P concentration measurements. Water Environment Research Foundation.Manning, J. F., Irvine, R. L. (1985) The biological removal of phosphorus in a sequencing batch reactor.Water Pollution Control Federation, 57, 87-94.Melcer, H., Dold, P. L., Jones, R. M., Bye, C. M., Takacs, I. Stensel, H. D., Wilson, A. W., Sun, P., Bury, S.(2003) Methods for wastewater characterization in activated sludge modeling. WaterEnvironment Research Foundation.Process Design Manual for Nitrogen Control (1993) United States Environmental Protection Agency,Cincinnati, Ohio.Randall, C. W., Barnard, J. L., & Stensel, H. D. (1992) Design and retrofit of wastewater treatment plantsfor biological nutrient removal. Lancaster: Technomic Publishing Company.Rim, Y. T., Yang, H. J., Yoon, C. H., Kim, Y. S., Seo, J. B., Ryu, J.K., & Shin, E. B. (1997) A full-scale test of abiological nutrients removal system using the sequencing batch reactor activated sludgeprocess. Water Science and Technology, 35, 241-247.Rittman, B., Langeland, W. (1985) Simultaneous denitrification with nitrification in single-channeloxidation ditches. Journal Water Pollution Control Federation, 57, 300-308.Rittman, B. E., McCarty, P. L. (2001) Environmental Biotechnology: Principals and Applications. New York:McGraw-Hill.Sedlack, R. I. (1991) Phosphorus and Nitrogen Removal form Municipal Wastewater Principals andPractice. Chelsea: Lewis Publishers.Surampalli, R. Y., Tyagi, R.D., Scheilble, O. K., & Heidman, J. A. (1997) Nitrification, denitrification andphosphorus removal in sequential batch reactors. Bioresource Technology, 61, 151-157.Tchobanoglous, G., Burton, F. L., & Stensel, H. D. (2003) Wastewater Engineering Treatment and Reuse.New York: McGraw-Hill.Third, K.A., Gibbs, B., Newland, M., & Cord-Ruwicsh, R. (2005) Long-term aeration management forimproved N-removal via SND in a sequencing batch reactor. Water Research, 39, 3523-3530.United States Environmental Protection Agency (US EPA) website. “Federal Water Quality Standards forthe State of Florida” Accessed January, 2011.http://water.epa.gov/lawsregs/rulesregs/florida_index.cfm72

Velmurugan, S., Clarkson, W. W., Veenstra, J. N. (2010) Model based design of sequencing batch reactorfor removal of biodegradable organics and nitrogen. Water Environment Research, 82, 462-474.Water Environment Foundation (WEF) Manual of Practice (MOP) Number 31 (2009) An Introduction toProcess Modeling for Designers.Wentzel, M.C., Lotter, L.H., Loewenthal, R.E., & Marais, G.v.R. (1986) Metabolic behavior ofAcinetobactor spp. In enhanced biological phosphorus removal-a biochemical model. Water SA,12, 209-224.Wentzel, M.C., Ekama, G.A., Loewenthal, R.E., Dold, P.L., and Marais, G.v.R. (1989) Enhancedpolyphosphate organism cultures in activated sludge systems. Part II: experimental behavior.Water SA, 15, 71-88.Wuhrmann, K. (1964) Nitrogen removed in sewage treatment processes. Verhandlungen desInternationalen Verein Limnologie XV: 580.73

Appendix IBioWin Simulation Setup74

Table AI.1- Influent specifier input values regular modeParameter Value Units Data SourceFlow 1911.6 m 3 Mechanical monthly monitoring reports (May,/d2005)Influent total COD 554 mg/L Estimated using Table 2 and 3 dataTKN 41.3 mg/L Calculated from Table 3 dataTotal P 10.8 mg/L Table 3Nitrate 0.4 mg/L Table 3pH 7.5 - Table 3Alkalinity 300 mg CaCO 3/L DefaultCalcium 80 mg/L DefaultMagnisium 15 mg/L DefaultD.O. 0 mg/L DefaultEffluent filtered COD 26.7 mg/L Table 3Influent filtered COD 430 mg/L Table 4Influent FF COD 241 mg/LEstimated using Table 3 data and defaultvaluesAmmonia 21.2 mg/L Table 3Acetate 12 mg/L DefaultOrtho-phosphate 5 mg/L DefaultInfluent cBOD 5 277 mg/L Estimated using COD/cBOD = 2Influent filtered cBOD 5 215 mg/L Estimated using sCOD/fcBOD = 2Influent VSS 151 mg/L Estimated from sample taken 10/20/10Influent TSS 183 mg/LMechanical monthly monitoring reports (May,2005)Figure AI.1 - Influent specifier input page for regular mode75

Figure AI.2 - Influent specifier fraction estimate page for regular mode (guess and check red colorednumbers until the best match is found)Figure AI.3 - Influent specifier COD fractions page for regular mode. Values were input into BioWin forregular mode.76

Table AI.2- Influent specifier input values BNR-S1 modeParameter Value Units Data SourceFlow 1253 m 3 Mechanical monthly monitoring reports (August,/d2005)Influent total COD 783 mg/L Estimated using Table 2 and 3 dataTKN 39.2 mg/L Calculated from Table 3 dataTotal P 9.5 mg/L Table 3Nitrate 0.3 mg/L Table 3pH 7.2 - Table 3Alkalinity 300 mg CaCO 3/L DefaultCalcium 80 mg/L DefaultMagnisium 15 mg/L DefaultD.O. 0 mg/L DefaultEffluent filtered COD 29.3 mg/L Table 3Influent filtered COD 608 mg/L Table 3Influent FF COD 340 mg/L Estimated using Table 3 data and default valuesAmmonia 24.1 mg/L Table 3Acetate 12 mg/L DefaultOrtho-phosphate 5 mg/L DefaultInfluent cBOD 5 392 mg/L Estimated using COD/cBOD = 2Influent filtered cBOD 5 304 mg/L Estimated using sCOD/fcBOD = 2Influent VSS 384 mg/L Estimated from sample taken 10/20/10Influent TSS 465 mg/LMechanical monthly monitoring reports (August,2005)Figure AI.4 - Influent specifier input page for BNR-S1 mode.77

Figure AI.5 - Influent specifier fraction estimation sheet for BNR-S1 mode.Figure AI.6 - Influent specifier COD fractions page for BNR-S1 mode. Values were input into BioWin forregular mode.78

Figure AI.7 - SBR specifications screen in BioWin for BNR-S1 mode (all modes have the samespecifications).Figure AI.8 - Inffluent itinerary for regular mode79

Figure AI.9 - SBR operation page for regular modeFigure AI.10 - D.O. setpoint itinerary for regular mode80

.Figure AI.11 - Underflow rate itinerary for regular modeFigure AI.12 - Influent itinerary for BNR-S1 mode81

Figure AI.13 - SBR operation page for BNR-S1Figure AI.14 - D.O. setpoint itinerary for BNR-S1 mode82

Figure AI.15 - Underflow itinerary for BNR-S1 modeSolids Wasting CacalculationSolids Flowrate = Reactor Volume/ Target SRT/ Number of Cycles per Day/ Solids flow durationFor regular mode: Reactor Volume = 1340 m 3Target SRT = 13 daysCycles per day = 5Solids flow duration = 10 minutes per cycle (assumed value)Solids Flowrate = 1340 m 3 / 13 days/ 5 cycles per day/ 10 minutes per cycle = 2.0615 m 3 /minute83

Appendix IISelected BioWin Simulation Data84

Table AII.1- Percent deviation from effluent value at default for +60% adjustment of simulator parameter for regular modeSimulator Parameter TSS pH cBOD TN NH 3 -N NO 2 -N NO 3 - N TP COD F. CODSum of absolutevalueAerobic decay rate (NOB) 0 0 -1 0 -1 641 -7 -5 0 0 655Hetertrophic yield (OHO) 20 1 46 -30 -93 -58 -46 -94 16 14 418Aerobic decay rate (Amm) 0 0 -1 2 113 97 -2 -2 0 0 216Substrate Amm half sat. (AOB) 0 0 0 2 133 65 -2 -2 0 0 204N content in biomass (AOB & NOB) 2 0 -4 -8 -10 -6 -29 -83 1 4 147Clarification switching function (settling) 56 0 46 5 3 6 -1 2 16 0 135Substrate nitrite half sat. (ANAMMOX) 0 0 0 0 0 111 -1 -1 0 0 115Maximum Vesilind settling velocity -47 0 -38 -4 -2 -4 0 -2 -14 0 111AOB maximum specific growth rate 0 0 0 -1 -51 -24 1 1 0 0 78Anoxic hydrolysis factor (OHO) 1 0 -3 -1 -13 -14 1 -37 0 2 72NOB maximum specific growth rate 0 0 0 1 1 -50 2 2 0 0 55Hydrolysis rate (AS) 0 0 -4 0 20 18 0 1 -1 -1 46Anoxic/Anaerobic decay rate (AOB) 0 0 0 0 23 18 0 0 0 0 42Heterotrophic aerobic decay rate -1 0 4 9 -7 -8 2 1 2 3 37Sequestration rate (PAO) 1 0 -1 -1 -4 -5 0 -23 0 1 35Anoxic/Anaerobic decay rate (NOB) 0 0 0 0 0 32 -1 -1 0 0 34COD:VSS ratio (ALL) -3 0 16 2 1 2 0 1 6 0 31Substrate half sat. (OHO) 0 0 11 0 -3 -4 0 -3 2 3 27Vesilind hindered zone settling parameter (settling) 11 0 9 1 1 1 0 0 3 0 27Ammonification rate (OHO) 0 0 0 -6 -8 -7 2 2 0 0 27Yield aerobic (PAOs) 1 0 0 -1 -4 -4 -1 -13 1 2 26P in biomass (ALL+Common) 0 0 0 0 0 0 0 -23 0 0 24Frac. to end. residue (AOB & NOB) 1 0 -7 -2 -3 -2 -3 -4 0 0 23Aerobic P/PHA uptake (PAO) 1 0 -2 0 0 0 0 -17 -1 0 21Anaerobic hydrolysis factor (OHO) 0 0 -1 0 -5 -5 1 -6 0 0 19Anoxic growth factor (OHO) 0 0 -1 -2 -2 3 -3 -7 0 0 19OHO maximum specific growth rate 0 0 -8 0 2 2 0 2 -1 -2 18Hydrolysis half sat. (AS) (OHO) 0 0 2 0 -6 -5 0 -1 0 0 15P/Ac release ratio (PAO) 0 0 1 0 2 2 0 8 0 -1 14Particulate inert COD:VSS ratio (common) -1 0 6 1 0 1 0 0 2 0 11Yield of PHA sequestration * (PAO) 0 0 -1 0 -1 -1 0 -7 0 0 10Aerobic decay rate (PAO) 0 0 0 0 1 1 0 6 0 0 9Anoxic/Anaerobic decay (OHO) 0 0 -1 2 0 0 1 1 0 1 7Yield (AOB) 0 0 1 0 1 1 -1 -2 0 0 6Fermentation rate (OHO) 0 0 0 0 -1 -1 0 -2 0 0 585

Table AII.1- Percent deviation from effluent value at default for +60% adjustment of simulator parameter for regular modeSimulator Parameter TSS pH cBOD TN NH 3 -N NO 2 -N NO 3 - N TP COD F. CODAnaerobic growth facotr (OHO) 0 0 0 0 -1 -1 0 -2 0 0 5Fermentation half saturation (OHO) 0 0 0 0 1 1 0 2 0 0 4Yield (NOB) 0 0 0 0 0 2 0 -1 0 0 4Anaerobic decay rate (PAO) 0 0 0 0 0 0 0 3 0 0 4PAO max spec growth rate 0 0 0 0 0 0 0 -1 0 0 2Aerobic decay rate (PAO) 0 0 0 0 0 0 0 1 0 0 1Inert fraction of endogenous solution (PAO) 0 0 0 0 0 0 0 0 0 1 1Yield anoxic (PAOs) 0 0 0 0 0 0 0 -1 0 0 1Assim. NO2/NO3 reduction rate (OHO) 0 0 0 0 1 0 0 0 0 0 1Particulate substrate COD:VSS ratio (common) 0 0 0 0 0 0 0 0 0 0 1Anoxic P/PHA uptake (PAO) 0 0 0 0 0 0 0 -1 0 0 1Adsorption rate of colloids (OHO) 0 0 0 0 0 0 0 0 0 0 1Fraction to endogenous particulate (PAO) 0 0 0 0 0 0 0 0 0 0 1Anoxic growth factor nitrite (PAO) 0 0 0 0 0 0 0 0 0 0 1Anoxic growth factor nitrate (PAO) 0 0 0 0 0 0 0 0 0 0 0Substrate half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0Yield of low PP (PAO) 0 0 0 0 0 0 0 0 0 0 0Substrate half sat., P. limited (PAO) 0 0 0 0 0 0 0 0 0 0 0Nitrite inhibition constant KiHNO2 (AOB) 0 0 0 0 0 0 0 0 0 0 0ANAMMOX maximum specific growth rate 0 0 0 0 0 0 0 0 0 0 0Nitrite inhibition constant KiNH3 (NOB) 0 0 0 0 0 0 0 0 0 0 0Cation half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0Substrate NO2 half sat. (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Substrate NH4 half sat (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Magnesium half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0Maximum compactability constant (settling) 0 0 0 0 0 0 0 0 0 0 0Anoxic/Anaerobic decay rate (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0NO2 sensitivity constant (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Calcium half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0Specific TSS conc. for height calc. (settling) 0 0 0 0 0 0 0 0 0 0 0Hydrolysis rate (AD) (OHO) 0 0 0 0 0 0 0 0 0 0 0Hydrolysis half sat. (AD) (OHO) 0 0 0 0 0 0 0 0 0 0 0Aerobic decay rate (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Ki Nitrite (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Sum of absolutevalue86

Table AII.2- Percent deviation from effluent value at default for -60% adjustment of simulator parameterSimulator Parameter TSS pH cBOD TN NH 3 -N NO 2 -N NO 3 - N TP COD F. CODSum of absolutevalueAOB maximum specific growth rate 2 2 -1 235 13578 241 -93 -92 6 12 14263NOB maximum specific growth rate 2 0 -3 -3 -22 9791 -95 -92 2 6 10016Maximum Vesilind settling velocity 468 0 412 40 48 317 -8 15 138 0 1444OHO yield -7 -1 -28 118 15 -6 182 37 -4 -2 399OHO maximum specific growth rate 0 0 72 0 -42 -44 1 -13 12 18 202Hydrolysis rate (AS) (OHO) 5 0 37 -11 -25 -22 -15 -26 5 -1 148Clarification switching function (settling) -58 0 -47 -5 -3 -5 1 -2 -17 0 137Particulate substrate COD:VSS ratio (common) 9 7 4 8 0 0 5 6 37 26 102Substrate ammonia half saturation (AOB) 0 0 0 -1 -66 -24 1 1 0 0 93COD:VSS ratio (ALL) 49 0 -20 -2 -1 -2 0 -1 -7 0 84N content in biomass (AOB & NOB) 0 0 2 5 -17 -22 29 6 0 -1 82Ammonification rate (OHO) 0 0 -1 20 -15 -17 -8 -14 0 1 77Substrate nitrite half sat. (ANAMMOX) 0 0 0 1 0 -62 1 1 0 0 66Aerobic decay rate (AOB) 0 0 1 -1 -37 -21 0 0 0 0 60Hydrolysis half sat. (AS) 0 0 -4 0 21 18 0 1 -1 -1 47OHO aerobic decay rate 3 0 6 -12 -6 -2 -6 -8 -1 -3 47Anoxic hydrolysis factor (OHO) 0 0 3 0 13 16 -2 6 0 0 41Aerobic decay rate (NOB) 0 0 0 0 0 -31 1 1 0 0 34Particulate inert COD:VSS ratio (common) 6 0 -16 -2 -1 -2 0 -1 -6 0 33P in biomass (ALL+Common) 0 0 0 0 0 0 0 30 0 0 30Anoxic/Anaerobic decay rate (AOB) 0 0 0 0 -16 -11 0 0 0 0 27Fraction to endogenous residue (AOB & NOB) -1 0 9 2 3 3 3 4 0 0 26Substrate half saturation (OHO) 0 0 -11 0 3 3 0 3 -2 -3 25Aerobic decay rate (PAO) 0 0 -1 -1 -2 -2 0 -15 0 0 21Anoxic growth factor (OHO) 0 0 1 2 5 0 3 7 0 -1 19Anaerobic hydrolysis factor (OHO) 0 0 2 0 5 6 -1 4 0 0 18Anoxic/Anaerobic decay rate (NOB) 0 0 0 0 0 -15 0 0 0 0 16P/Ac release ratio (PAO) 0 0 -1 0 0 0 0 -11 -1 0 14Yield of low PP (PAO) 0 0 1 0 2 2 0 8 0 -1 14Yield of PHA sequestration * (PAO) 0 0 1 0 2 2 0 8 0 -1 14Aerobic P/PHA uptake (PAO) 0 0 1 0 2 2 0 8 0 -1 14Fermentation rate (OHO) 0 0 1 0 3 4 0 5 0 0 13Anaerobic growth factor (OHO) 0 0 1 0 3 4 0 5 0 0 13Sequestration rate (PAO) 0 0 1 0 2 2 0 8 0 -1 13Yield (AOB) 0 0 -1 0 -5 -4 1 2 0 0 1387

Table AII.2- Percent deviation from effluent value at default for -60% adjustment of simulator parameterSimulator Parameter TSS pH cBOD TN NH 3 -N NO 2 -N NO 3 - N TP COD F. CODSum of absolutevalueYield aerobic (PAOs) 0 0 0 0 1 2 0 7 0 -1 13Vesilind hindered zone settling parameter -5 0 -4 0 0 0 0 0 -2 0 13Anoxic/Anaerobic decay (OHO) 0 0 1 -2 -1 -1 -1 -2 0 -1 9Yield (NOB) 0 0 0 0 0 -6 0 1 0 0 9Fermentation half saturation (OHO) 0 0 0 0 -1 -1 0 -3 0 0 6Anaerobic decay rate (PAO) 0 0 0 0 0 0 0 -4 0 0 5PAO maximum specific growth rate 0 0 0 0 0 0 0 3 0 0 4Yield anoxic (PAOs) 0 0 0 0 0 0 0 2 0 0 3Adsorption rate of colloids (OHO) 0 0 0 0 -1 -1 0 0 0 0 3Aerobic decay rate (PAO) 0 0 0 0 1 1 0 -1 0 0 3Inert fraction of endogenous solution (PAO) 0 0 0 0 0 0 0 0 0 -1 1Assimil. NO2/NO3 reduction rate (OHO) 0 0 0 0 -1 0 0 0 0 0 1Anoxic P/PHA uptake (PAO) 0 0 0 0 0 0 0 1 0 0 1Nitre inhibiton constant KiHNO2 (AOB) 0 0 0 0 0 0 0 0 0 0 1Fraction to endogenous particulate (PAO) 0 0 0 0 0 0 0 0 0 0 1Anoxic growth factor nitrite (PAO) 0 0 0 0 0 0 0 0 0 0 1Maximum compactability constant (settling) 0 0 0 0 0 0 0 0 0 0 0Nitrite inhibition constant KiNH3 (NOB) 0 0 0 0 0 0 0 0 0 0 0Anoxic growth factor nitrate (PAO) 0 0 0 0 0 0 0 0 0 0 0Substrate half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0Substrate half sat., P. limited (PAO) 0 0 0 0 0 0 0 0 0 0 0Substrate NO2 half sat. (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Substrate NH4 half sat. (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Maximum specific growth rate (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Cation half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0Magnesium half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0NO2 sensitivity constant (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Aerobic decay rate (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Anoxic/Anaerobic decay rate (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 0Calcium half sat. (PAO) 0 0 0 0 0 0 0 0 0 0 0Specific TSS conc. for height calc. (settling) 0 0 0 0 0 0 0 0 0 0 0Hydrolysis rate (AD) (OHO) 0 0 0 0 0 0 0 0 0 0 0Hydrolysis half sat. (AD) (OHO) 0 0 0 0 0 0 0 0 0 0 0Ki Nitrite (ANAMMOX) 0 0 0 0 0 0 0 0 0 0 088

Sum absolute value of percent deviation from observedAOB and NOB max spec growth ratepercent adjustment from defaultTable AII.3- Combination 1. Sum absolute value of percenterror for regular modeHeterotrophic Yield (OHO) percent adjustment fromdefault-60 -50 -40 -30 -20 -10 00 280 267 296 325 329 324 357-10 270 264 293 321 321 316 309-20 213 256 277 275 272 260 291-30 224 227 219 204 174 224 284-40 445 412 360 344 361 372 378-50 1489 1440 1391 1347 1306 1264 1220-60 2109 2059 2006 1949 1890 1823 1749grey colored cells omitted from graph5004003002001000-60-50-40-30-20-10Percent adjustment OHO yield00-10-20-30-40Percent adjustmentAOB and NOBmaximum specifcgrowth rate400-500300-400200-300100-2000-100Figure AII.1- Regular mode calibration progression for combination 189

Sum absolute value of percent deviation from observedHetertrophic yield and NOB max specgrowthrate percent adjustment fromdefaultTable AII.4- Combination 2. Sum absolute value of percent errorfor regular mode.AOB max spec growth rate percent adjustment fromdefault-60 -50 -40 -30 -20 -10 0-60 2109 1489 465 284 338 356 363-50 2059 1440 418 271 323 340 346-40 2006 1196 360 245 296 311 317-30 1950 1345 307 204 251 264 269-20 1890 1302 316 224 272 290 300-10 1824 1258 335 253 299 316 3230 1750 1212 368 252 298 313 357grey colored cells omitted from graph500400300200100-30-40400-500300-400200-300100-2000-1000-60-50-40-30-200-100Percent adjustment OHO yield and NOB maimum specific grwoth rateFigure AII.2- Regular mode calibration progression for combination 2-10-20Percent adjustmentAOB maximum specificgrowth rate90

Sum absolute value of percent errorHeterotrophic yield and AOB max specgrowth rate percent adjustment fromdefaultTable AII.5- Combination 3. Sum absolute value of percent errorfor regular mode combination 3NOB max spec. growth rate percent adjustment from default-60 -50 -40 -30 -20 -10 0-60 2109 2109 2110 2111 2112 2113 2114-50 1439 1440 1443 1448 1451 1450 1448-40 370 356 360 352 383 407 416-30 244 225 218 204 229 261 268-20 322 300 263 224 272 304 309-10 365 351 317 258 281 316 3190 374 366 348 325 300 317 357grey colored cells ommited from graph5004003002001000-60-50-40-30-20-10Percent adjustment NOB maximum specific growth rate00-10-20-30-40Percent adjustmentOHO yield and AOBmaximmumspecific growth rate400-500300-400200-300100-2000-100Figure AII.3- Regular mode calibration progression for combination 391

AOB and NOB maximum specific growth rateTable AII.6- Expansion of best run from Table AII.3. Sumabsolute value of percent error for regular mode.Heterotrophic Yield (OHO)-24 -23 -22 -21 -20 -19 -18 -17 -16-26 226 224 223 221 220 218 216 213 211-27 216 214 213 211 209 207 204 202 199-28 205 204 202 200 198 195 193 190 193-29 194 192 190 188 185 183 181 185 190-30 188 185 182 178 174 173 177 181 187-31 181 177 173 170 170 174 177 181 185-32 171 167 163 166 170 173 177 181 186-33 158 157 160 164 167 171 175 180 185-34 172 174 176 179 181 184 187 191 19525020015010050-100-34500-50-320-30-24 AOB and NOB-23 -22 -28 maximum specific-21 -20 growth rate-19 -18 -26OHO Yield -17 -16Figure AII.4- Regular mode calibration progression for best run in Table AII.3200-250150-200100-15092

AOB and NOBmaximumspecific growthrateSum absolute value of percent errorsTable AII.7- Sum absolute value of percent error for BNR-S1 modeOHO yield-30 -25 -20 -15 -10 -5 0 5-20 250.3 507.4 473.1 441 378 303 250 313-25 515.8 414.7 401.2 366 303 231 251 315-30 464.3 382.0 356.5 299 219 171 251 310-35 471.3 430.5 379.2 299 232 208 248 295-40 479.7 431.7 384.9 325 255 209 226 267Grey values were omitted from plot500400-500400300-400300200-3002001000-15-10-50Percent adjustment from default OHO yield5-20-25-30-35-40Percent adjustmentfrom default AOBand NOB maximumspecific growth rate100-2000-100Figure AII.5- BNR-S1 mode calibration progression93

Appendix IIIMonthly Monitoring Reports to Iowa Department of NaturalResourcesCity of Grundy Center, Iowa94

Facility Name Grundy Center WWTFMECHANICAL PLANT FACILITY MONTHLY MONITORING REPORT Month/Year May-05Facility Number 3833001IOWA DEPARTMENT OF NATURAL RESOURCES Outfall No. 001NPDES - Operation Permit SystemINFLUENTEFFLUENTBY-FECALTOTAL RESIDUALD PASS FLOW cBOD TSS PH TEMP FLOW cBOD TSS NH3-NCOLI-CHLORINEA Y FLOWFORMpH TEMPSample Type tot calc calc grab grab tot calc calc calc calc grab grab grabSample Freq.Units MGD mg/L #/day mg/L #/day SUo F MGD mg/L #/day mg/L #/day mg/L #/day mg/L #/day n/100 mL SUo F1 0.357 0.3462 0.405 0.3933 0.391 255 832 186 607 7.39 55 0.382 6.00 19.12 9.93 31.64 0.49 1.56 6.65 544 0.364 338 1026 0.355 6.00 17.76 0.49 1.455 0.349 7.98 57 0.344 6.00 18.44 6.88 566 0.430 0.4187 0.376 0.3648 0.367 0.3549 0.434 0.419 6.62 6010 0.429 197 705 158 565 7.39 59 0.414 3.99 13.78 10.00 34.53 0.49 1.6911 0.557 182 845 0.529 5.00 22.06 0.60 2.65 6.80 5812 0.882 8.77 58 0.861 4.50 17.9213 0.941 0.93214 0.821 0.76615 0.609 0.58216 0.642 0.63017 0.663 150 829 40 221 6.71 60 0.653 6.00 32.68 6.00 32.68 0.80 4.36 6.50 5818 0.319 153 407 0.588 3.00 14.71 0.49 2.4019 0.535 7.03 59 0.525 4.50 23.69 6.63 5820 0.582 0.56121 0.519 0.49522 0.504 0.48123 0.507 0.49424 0.564 233 1096 336 1580 7.05 63 0.538 4.00 17.95 3.99 17.90 0.49 2.20 6.73 6225 0.497 173 717 0.481 2.99 11.99 0.49 1.9726 0.517 6.97 63 0.489 3.50 14.97 6.70 6327 0.466 0.45528 0.390 0.35929 0.364 0.34330 0.373 0.35031 0.500 202 842 193 805 7.11 60 0.478 3.99 15.91 3.99 15.91 0.80 3.19 6.69 61No. of Samp. 31 9 9 5 5 9 9 31 5 5 5 5 9 9 0 0 0 9 9Tot of Samp. 15.654 1883 7300 913 3778 15.38 22.5 90.9 33.9 132.65 5.14 21.46 0.00 0.00 0Monthly Avg. 0.505 209 811 183 756 0.496 4.50 18.19 6.8 26.53 0.57 2.38 N/A N/ADaily Max. 0.941 338 1096 336 1580 8.8 63 0.932 0.80 4.36 0.00 0.00 0 6.88 63Daily Min. 6.7 55 0.343 6.50 54Max. 7/Avg. 6.0 23.69 10.0 34.53Limit - Avg.Limit - Max.Limit - 7-dayOver Limit?Signature_____James L. Copeman________________________________ Certificate #1996DNR Form 35-6a (1/96)95

DAYIrrig.1000'sGPDInfluentNH3-Nmg/LInfluentNH-3_Nmg/L#'s/DayRAIN-FALLSETT.SOLIDRECYCLEFLOWType grab MEAS.FreqUnits in mL/L MGD123 0.0 0.0331 17.2 56.145 0.06 0.0 0.04667 0.308 0.25910 0.0 14.4 51.511 0.66 0.054012 0.03 0.013 1.90 0.0 0.032314 T 0.0449151617 0.04 0.0 0.1028 13.7 75.818 T19 0.26 0.020 0.0 0.03802122 0.122324 0.0 0.0638 17.3 81.42526 0.08 0.0 0.033127 0.03 0.02829 T3031 0.0 0.0785 20.7 86.3No. 10 13 10 0 5 5 0 0 0 0 0 0 0 0 0 0 0 0Total 3.67 0.0 0.5271 0 83.30 351.10 0 0 0 0 0 0 0 0 0 0 0 0Avg N/A 16.7 70.22 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/AMax 1.90 0.0 0.1028 0 20.7 86.30 0 0 0 0 0 0 0 0 0 0 0 0Min 0.03 0.0 0.0323 0 13.7 51.50 0 0 0 0 0 0 0 0 0 0 0 0L-AvgL-MaxL-MinCOMMENTS AND PRE-DRAWDOWN RESULTS:96

Facility Name Grundy Center WWTF MECHANICAL PLANT FACILITY MONTHLY MONITORING REPORT Month/Year 8/05Facility Number 3833001 IOWA DEPARTMENT OF NATURAL RESOURCES Outfall No. 001NPDES - Operation Permit SystemINFLUENTEFFLUENTDA YBY-PASSFLOWFLOW cBOD TSS PH TEMP FLOW cBOD TSS NH3-NTOTAL RESIDUALCHLORINESample Type tot calc calc grab grab tot calc calc calc calc grab grab grabSample Freq.Units MGD mg/L #/day mg/L #/day SUo F MGD mg/L #/day mg/L #/day mg/L #/day mg/L #/day n/100 mL SUo F1 0.342 0.3362 0.391 169 551 137 447 7.20 71 0.369 3.99 12.28 4.35 13.39 1.20 3.69 6.76 703 0.346 240 693 0.328 7.00 19.15 0.80 2.194 0.302 8.07 66 0.292 6.96 705 0.342 0.3096 0.278 0.2587 0.304 0.2588 0.353 0.3089 0.292 405 986 965 2350 7.18 68 0.256 4.00 8.54 6.67 14.24 3.10 6.62 6.83 7010 0.300 226 565 0.258 16.00 34.43 0.49 1.0511 0.537 6.57 70 0.513 6.86 7012 0.469 0.41713 0.370 0.33714 0.316 0.29015 0.386 0.35316 0.316 168 443 400 1054 7.36 68 0.269 3.99 8.95 4.67 10.48 0.49 1.10 6.97 7017 0.344 233 668 0.323 2.99 8.05 0.49 1.3218 0.327 7.11 71 0.292 7.00 7019 0.357 0.34120 0.275 0.25921 0.274 0.25322 0.289 7.31 68 0.284 6.90 7023 0.309 180 464 569 1466 0.300 3.99 9.98 6.00 15.01 0.70 1.7524 0.361 142 428 7.33 69 0.333 2.99 8.30 2.70 7.50 6.88 7025 0.341 0.33026 0.311 0.30227 0.272 0.25728 0.259 0.24129 0.292 0.28130 0.303 285 720 252 637 7.18 68 0.287 7.00 16.76 10.53 25.20 0.49 1.17 6.90 7131 0.299 218 544 0.283 6.00 14.16 0.60 1.42No. of Samp. 31.000 10 10 5 5 9.00 9 31.000 10.00 10.00 5.00 5.00 10 10 0 0 0 9.00 9Tot of Samp. 10.257 2266 6062 2323 5954 9.517 57.95 140.60 32.22 78.32 11.06 27.81 0.00 0.00 0Monthly Avg. 0.331 227 606 465 1191 0.307 5.80 14.06 6.44 15.66 1.11 2.78 N/A N/ADaily Max. 0.537 405 986 965 2350 8.07 71 0.513 3.10 7.50 0.00 0.00 0 7.00 71Daily Min. 6.57 66 0.241 6.76 70Max. 7/Avg. 10.00 21.48 6.67 15.01Limit - Avg.Limit - Max.Limit - 7-dayOver Limit?Signature_____________________________________ Certificate #DNR Form 35-6a (1/96)FECALCOLI-FORMpHTEMP97

D RAIN- SETT. RECYCLE Irrig.1000' Influent InfluentA Y FALL SOLID FLOW s NH3-N NH3-NType grab MEAS. mg/LFreqUnits in mL/L MGD GPD mg/L #'s/Day1 0.02 0.0 0.0491 0.03 0.0 19.5 56.34 0.0 0.05 0.0 0.0491 0.06 0.0384 0.07 38.48 0.0393 22.19 0.0 15.1 27.1 66.010 22.111 0.0 0.0378 15.112 0.0 0.0577 20.213 13.914 20.215 0.0479 15.116 0.0 20.8 22.0 58.017 15.118 0.0 21.419 0.0 0.0405 0.020 0.021 0.022 0.023 0.0189 0.0 31.1 80.124 0.0 0.0601 0.025 0.0 0.026 0.0 0.027 0.028 0.029 0.030 0.0 0.0 21.2 53.631 0.0No. 0 13 10.0000 31.0 5 5 0 0 0 0 0 0 0 0 0 0 0 0Total 0.00 0.0 0.4388 239.5 120.9 314 0 0 0 0 0 0 0 0 0 0 0 0Avg 7.7 24.18 62.8 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/AMax 0.00 0.0 0.0601 38.4 31.1 80.1 0 0 0 0 0 0 0 0 0 0 0 0Min 0.00 0.0 0.0189 0.0 19.5 53.6 0 0 0 0 0 0 0 0 0 0 0 0L-AvgL-MaxL-MinCOMMENTS AND PRE-DRAWDOWN RESULTS:98

Facility Name Grundy Center WWTF MECHANICAL PLANT FACILITY MONTHLY MONITORING REPORT Month/Year 10/05Facility Number 3833001 IOWA DEPARTMENT OF NATURAL RESOURCES Outfall No. 001NPDES - Operation Permit SystemINFLUENTEFFLUENTDA YBY-PASSFLOWFLOW cBOD TSS PH TEMP FLOW cBOD TSS NH3-NTOTAL RESIDUALCHLORINESample Type tot calc calc grab grab tot calc calc calc calc grab grab grabSample Freq.Units MGD mg/L #/day mg/L #/day SUo F MGD mg/L #/day mg/L #/day mg/L #/day mg/L #/day n/100 mL SUo F1 0.253 0.2362 0.257 0.2343 0.268 0.2394 0.286 518 1236 379 904 7.34 69 0.275 3.99 9.15 6.58 15.09 0.50 1.15 6.85 715 0.433 375 1354 0.386 5.00 16.10 0.49 1.586 0.279 9.15 68 0.238 6.95 717 0.279 0.2418 0.250 0.2089 0.245 0.20510 0.252 0.21711 0.389 233 756 400 1298 0.348 6.00 17.41 9.15 26.56 1.10 3.1912 0.282 323 760 7.37 65 0.234 2.99 5.84 0.90 1.76 6.86 6813 0.251 7.32 66 0.205 6.80 6814 0.249 0.23115 0.233 0.21116 0.229 0.20717 0.262 0.24118 0.247 443 913 371 764 8.28 67 0.231 3.99 7.69 5.30 10.21 0.49 0.94 6.75 6719 0.254 315 667 0.239 2.99 5.96 0.49 0.9820 0.265 8.65 66 0.250 6.71 6721 0.367 0.34522 0.220 0.21023 0.241 0.23024 0.246 0.20825 0.235 233 457 336 659 0.224 3.99 7.45 6.00 11.21 1.00 1.8726 0.319 383 1019 9.45 64 0.297 5.00 12.38 0.49 1.21 7.18 6527 0.261 9.58 64 0.237 6.67 6628 0.271 0.25629 0.221 0.20930 0.228 0.20531 0.226 0.216No. of Samp. 31.000 8 8 4 4 8.00 8 31.000 8.00 8.00 4.00 4.00 8 8 0 0 0 8.00 8Tot of Samp. 8.298 2823 7161 1486 3624 7.513 33.95 81.98 27.03 63.07 5.46 12.68 0.00 0.00 0Monthly Avg. 0.268 353 895 372 906 0.242 4.24 10.25 6.76 15.77 0.68 1.58 N/A N/ADaily Max. 0.433 518 1354 400 1298 9.58 69 0.386 1.10 3.19 0.00 0.00 0 7.18 71Daily Min. 7.32 64 0.205 6.67 65Max. 7/Avg. 4.50 12.62 9.15 26.56Limit - Avg.Limit - Max.Limit - 7-dayOver Limit?Signature_____________________________________ Certificate # 1996DNR Form 35-6a (1/96)FECALCOLI-FORMpHTEMP99

D RAIN- SETT. RECYCLE Irrig.1000' Influent InfluentA Y FALL SOLID FLOW s NH3-N NH3-NType grab MEAS. mg/LFreqUnits in mL/L MGD GPD mg/L #'s/Day1 0.02 0.03 0.04 0.0 0.0 24.2 57.725 0.1127 16.46 0.32 0.0 22.77 0.0 0.0258 18.38 18.39 18.310 22.711 0.0 0.0577 16.4 23.8 77.2112 22.113 T 0.0 16.414 0.0 0.015 0.016 0.017 0.05 0.018 0.0 0.0 29.3 60.3619 0.020 0.0 0.021 0.0 0.1094 0.022 0.023 0.024 0.03 0.025 0.0 0.0 28.7 56.2526 0.0785 0.027 0.0 0.028 0.0 0.0268 0.0293031No. 3 12 6.0000 28 4 4 0 0 0 0 0 0 0 0 0 0 0 0Total 0.40 0.0 0.4109 171.6 106 251.5 0 0 0 0 0 0 0 0 0 0 0 0Avg 6.13 26.5 62.89 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/AMax 0.32 0.0 0.1127 22.7 29.3 77.21 0 0 0 0 0 0 0 0 0 0 0 0Min 0.03 0.0 0.0258 0 23.8 56.25 0 0 0 0 0 0 0 0 0 0 0 0L-AvgL-MaxL-MinCOMMENTS AND PRE-DRAWDOWN RESULTS:100

Facility Name Grundy Center WWTFMECHANICAL PLANT FACILITY MONTHLY MONITORING REPORT Month/Year January-06Facility Number 3833001IOWA DEPARTMENT OF NATURAL RESOURCES Outfall No. 001NPDES - Operation Permit SystemINFLUENTEFFLUENTBY-FECALTOTAL RESIDUALD PASS FLOW cBOD TSS PH TEMP FLOW cBOD TSS NH3-NCOLI-CHLORINEA Y FLOWFORMpH TEMPSample Type tot calc calc grab grab tot calc calc calc calc grab grab grabSample Freq.Units MGD mg/L #/day mg/L #/day SUo F MGD mg/L #/day mg/L #/day mg/L #/day mg/L #/day n/100 mL SUo F1 0.259 0.2412 0.260 0.2423 0.346 210 606 300 866 7.47 59 0.326 9.00 24.5 10.67 29.01 1.10 2.99 6.53 574 0.316 338 891 0.296 8.00 19.7 1.00 2.475 0.369 7.43 59 0.349 8.50 22.11 6.73 566 0.319 0.2967 0.282 0.2598 0.265 0.2449 0.290 0.27010 0.367 233 713 295 903 8.63 57 0.340 8.00 22.7 18.67 52.94 1.10 3.12 6.70 5711 0.280 323 754 0.257 22.00 47.2 1.80 3.8612 0.313 7.40 57 0.293 15.00 34.92 6.60 5813 0.366 0.34814 0.293 0.26915 0.254 0.23816 0.312 7.44 55 0.296 6.78 5617 0.278 345 800 218 505 0.256 8.00 17.1 9.33 19.92 0.90 1.9218 0.315 413 1085 7.98 57 0.293 25.00 61.1 1.50 3.6719 0.301 0.289 16.50 39.0920 0.332 0.310 6.79 5721 0.263 0.24222 0.256 0.23023 0.377 0.35524 0.302 428 1078 303 763 7.36 56 0.274 8.00 18.3 15.52 35.47 0.70 1.60 6.68 5625 0.307 323 827 0.295 23.00 56.6 1.10 2.7126 0.345 8.12 56 0.321 15.50 37.43 6.65 5627 0.306 0.27928 0.294 0.26729 0.292 0.27030 0.312 0.28631 0.414 248 856 279 963 7.16 56 0.393 11.00 36.05 15.00 49.16 2.40 7.87 6.66 56No. of Samp. 31 9 9 5 5 9 9 31 5 5 5 5 9 9 0 0 0 9 9Tot of Samp. 9.585 2861 7610 1395 4001 8.924 66.50 169.55 69.19 186.50 11.60 30.20 0.00 0.00 0Monthly Avg. 0.309 318 846 279 800 0.288 13.30 33.91 13.84 37.30 1.29 3.36 N/A N/ADaily Max. 0.414 428 1085 303 963 8.6 59 0.393 2.40 7.87 0.00 0.00 0 6.79 58Daily Min. 7.2 55 0.230 6.53 56Max. 7/Avg. 16.50 39.09 18.67 52.94Limit - Avg.Limit - Max.Limit - 7-dayOver Limit?Signature_____James L. Copeman ________________________________ Certificate #1996DNR Form 35-6a (1/96)101

DAYIrrig.1000'sGPDInfluentNH3-Nmg/LInfluentNH-3_Nmg/L#'s/DayRAIN-FALLSETT.SOLIDRECYCLEFLOWType grab MEAS.FreqUnits in mL/L MGD1 0.02 0.08 0.03 T 0.0 0.0 19.52 56.34 T 0.05 0.0 0.0442 0.06 0.0 0.07 0.08 0.09 0.010 0.0 0.0699 0.0 13.30 40.711 0.012 0.0 0.013 0.0 0.0552 0.014 0.015 0.016 0.0344 0.017 0.0 0.0 25.62 59.418 0.0338 0.019 0.0 0.020 0.0 0.0564 0.021 0.09 0.022 0.023 0.0528 0.024 0.0 0.0 21.47 47.125 0.026 0.0 0.0442 0.027 0.0 0.028 0.029 0.33 0.030 0.05 0.031 0.0 0.1067 0.0 17.57 60.7No. 4 13 9 31 5 5 0 0 0 0 0 0 0 0 0 0 0 0Total 0.55 0.0 0.498 0 97.48 264.2 0 0 0 0 0 0 0 0 0 0 0 0Avg 0 19.50 52.84 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/AMax 0.33 0.0 0.107 0 25.62 60.7 0 0 0 0 0 0 0 0 0 0 0 0Min 0.05 0.0 0.034 0 13.3 40.7 0 0 0 0 0 0 0 0 0 0 0 0L-AvgL-MaxL-MinCOMMENTS AND PRE-DRAWDOWN RESULTS:102

Facility Name Grundy Center WWTFMECHANICAL PLANT FACILITY MONTHLY MONITORING REPORT Month/Year February-06Facility Number 3833001IOWA DEPARTMENT OF NATURAL RESOURCES Outfall No. 001NPDES - Operation Permit SystemINFLUENTEFFLUENTBY-FECALTOTAL RESIDUALD PASS FLOW cBOD TSS PH TEMP FLOW cBOD TSS NH3-NCOLI-CHLORINEA Y FLOWFORMpH TEMPSample Type tot calc calc grab grab tot calc calc calc calc grab grab grabSample Freq.Units MGD mg/L #/day mg/L #/day SUo F MGD mg/L #/day mg/L #/day mg/L #/day mg/L #/day n/100 mL SUo F1 0.290 0.262 7.00 15.30 2.80 6.122 0.305 7.41 55 0.279 6.76 573 0.340 0.3274 0.293 0.2715 0.260 0.2406 0.348 0.3277 0.260 383 830 287 622 7.74 55 0.237 6.00 11.86 5.33 10.54 3.50 6.92 6.68 558 0.277 278 642 0.254 7.00 14.83 2.20 4.669 0.272 7.56 55 0.256 6.50 13.34 6.71 5510 0.344 0.32311 0.254 0.23312 0.265 0.25113 0.319 0.29814 0.308 465 1194 1035 2659 7.50 54 0.246 9.00 18.46 12.00 24.62 5.60 11.49 6.61 5515 0.252 405 851 0.214 19.00 33.91 6.70 11.9616 0.314 7.60 53 0.291 14.00 26.19 6.70 5317 0.273 0.24718 0.276 0.25519 0.258 0.23720 0.271 0.25621 0.370 293 904 297 916 7.48 53 0.352 7.00 20.55 7.80 22.90 5.90 17.32 6.95 5322 0.296 503 1242 0.274 28.00 63.98 5.70 13.0323 0.281 7.83 54 0.255 17.50 42.27 6.90 5324 0.304 0.29225 0.301 0.28026 0.235 0.21427 0.273 0.26328 0.332 368 1019 394 1091 7.42 54 0.310 7.00 18.10 5.70 14.74 6.60 17.06 6.88 53293031No. of Samp. 28 7 7 4 4 8 8 28 5 5 4 4 8 8 0 0 0 8 8Tot of Samp. 8.171 2695 6683 2013 5288 7.544 52 115.2 31 73 39.00 88.55 0.00 0.00 0Monthly Avg. 0.292 385 955 503 1322 0.269 10.40 23.04 7.71 18.20 4.88 11.07 N/A N/ADaily Max. 0.370 503 1242 1035 2659 7.83 55 0.352 6.70 17.32 0.00 0.00 0 6.95 57Daily Min. 7.41 53 0.214 6.61 53Max. 7/Avg. 17.50 42.27 12.00 24.62Limit - Avg.Limit - Max.Limit - 7-dayOver Limit?Signature__James L. Copeman___________________________________ Certificate #1996DNR Form 35-6a (1/96)103

DAYIrrig.1000'sGPDInfluentNH3-Nmg/LInfluentNH-3_Nmg/L#'s/DayRAIN-FALLSETT.SOLIDRECYCLEFLOWType grab MEAS.FreqUnits in mL/L MGD1 0.02 0.0 0.03 0.0 0.0564 0.04 0.05 0.06 0.0503 0.07 0.0 0.0 22.45 48.688 T 0.09 0.0 0.010 0.02 0.0 0.0614 0.011 0.012 T 0.013 T 0.0356 0.014 0.0 0.0400 0.0 26.96 69.2515 0.016 0.08 0.0 0.017 0.10 0.0 0.0577 0.018 0.019 0.020 0.021 0.0 0.0687 0.0 19.76 60.9822 0.023 0.0 0.024 0.0 0.025 0.0484 0.026 0.027 0.028 0.0 0.0393 0.0 20.01 55.41293031No. 3 12 9 28 4 4 0 0 0 0 0 0 0 0 0 0 0 0Total 0.20 0.0 0.458 0 89.18 234.32 0 0 0 0 0 0 0 0 0 0 0 0Avg 0 22.30 58.58 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/AMax 0.10 0.0 0.0687 0 26.96 69.25 0 0 0 0 0 0 0 0 0 0 0 0Min 0.02 0.0 0.0356 0 19.76 48.68 0 0 0 0 0 0 0 0 0 0 0 0L-AvgL-MaxL-MinCOMMENTS AND PRE-DRAWDOWN RESULTS:104

Appendix IVBioWin Process Model FormulationFrom BioWin User Manual (EnviroSim, 2010)105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

AcknowledgementsI would like to thank all of the people who provided advice and assistance in completion of this project.Your time and efforts were greatly appreciated! I would like to give special recognition to the followingpeople: Dr. Say Kee Ong and Dr. Eric Evans (Iowa State University), Gregory L. Sindt (Bolton & Menk,Inc.), Lance Aldrich (Fox Engineering Associates, Inc.), Dan Bangasser and Brad Flater (City of GrundyCenter, Iowa), Dr. Christopher Bye (EnviroSim Associates Ltd.).137