SIMULATION FOR OPERATION AND CONTROL OF ...

SIMULATION FOR OPERATION AND CONTROL OF REJECT WATER TREATMENT PROCESSES R. M. Jones1, P.L. Dold1, I. Takács1, K. Chapman1, B. Wett2, S. Murthy3, and M. O'Shaughnessy4 1 EnviroSim Associates Ltd., 7 Innovation Drive, Suite 205, Flamborough ON L9H 7H9, Canada 2 Institute of Environmental Engineering, University of Innsbruck, A-6020 Innsbruck, Austria. 3 DCWASA, DWT, 5000 Overlook Ave., SW Washington, DC 20032, U.S.A. 4 Alexandria Sanitation Authority 1500 Eisenhower Avenue, Alexandria, VA, 22314, USA ABSTRACT The recycled nutrient load of the reject water streams from solids handling is considerable, and can amount to 15-25% of the influent nitrogen load. A number of sidestream biological processes have been developed for treating the ammonia component of the reject water before returning it to the liquid train. This paper describes a plant-wide model that includes the reactions important in sidestream processes. Calibration of the model to mainstream and sidestream conditions is illustrated. Application of the model to simulation of a sidestream process pilot plant illustrates the evaluation of different operation modes and control strategies. KEYWORDS Plant-wide model, sidestream, deammonification, anammox, process control

INTRODUCTION Typically the liquid stream from anaerobic digestion and other sludge line processes is dewatered and returned to the activated sludge process. The recycled nutrient load of the reject water stream is considerable, and can amount to 15-25% of the influent nitrogen load. This additional load increases the cost and complexity of meeting stringent effluent requirements for total nitrogen (TN). Also, the capacity of the liquid train to treat additional ammonia load may be limited by factors such as influent alkalinity, insufficient aeration capacity, or insufficient SRT. A number of sidestream biological processes have been developed for treating the ammonia component of the reject water before returning it to the liquid train. Treating a high temperature, concentrated stream is more efficient than diluting it into the main stream. These systems involve one or more of the following biological transformations, or a combination of these: • •

Nitritation mediated by ammonia oxidizing autotrophic bacteria – AOBs (i.e. conversion of ammonia to nitrite) or partial nitritation (i.e. converting a portion of the ammonia to nitrite); Nitratation mediated by nitrite oxidizing autotrophic bacteria – NOBs (i.e. conversion of nitrite to nitrate);

• • •

Denitritation mediated by heterotrophic bacteria where nitrite serves as an electron acceptor on the addition of organic substrate with production of nitrogen gas; Denitratation mediated by heterotrophic bacteria where nitrate serves as an electron acceptor on the addition of organic substrate with production of nitrite; Nitrogen removal by autotrophic anammox bacteria (ANaerobic AMMonia OXidation). The process converts ammonia directly into nitrogen gas in unaerated conditions, utilizing nitrite as an electron acceptor.

The following terminology is used to describe combinations of the basic biological transformations: • • •

Nitrification: nitritation followed by nitratation (i.e. conversion of ammonia to nitrite and then nitrate); Denitrification: denitratation followed by denitritation (i.e. conversion of nitrate to nitrite and then nitrogen gas); Deammonification: partial nitritation (i.e. converting a portion of the ammonia to nitrite) followed by the anammox reaction (i.e. conversion of ammonia and nitrite to nitrogen gas);

A range of benefits have been identified for the different side-stream treatment systems; for example: • • • • •

Seeding the activated sludge train with AOBs and NOBs grown in the side-stream stage, allowing shorter SRTs (bioaugmentation). Less carbon substrate is required to denitrify nitrite compared to nitrate. Less aeration (and alkalinity) is required to convert ammonia to nitrite rather than nitrate. In the anammox process no organic carbon is added for denitrification, and so there is no increased biosolids production or emission of CO2. Substantially less aeration energy is consumed for deammonification compared to other ammonia removal processes.

These processes have been implemented in laboratories and full-scale plants in a number of reactor configurations; for example, single and series flow-through CSTRs, reactor-clarifiers with sludge recycle, SBRs, attached growth systems. The operating conditions of sidestream biological processes are considerably different from those in the main stream process. This leads to a number of unique considerations for operation and control. • • • •

Stopping nitrification at the nitrite stage and preventing nitrate formation relies on the difference in growth rates between AOBs and NOBs, and the different temperature dependencies. High concentrations of substrate and product species such as ammonia and nitrous acid can lead to inhibitory conditions for AOBs and NOBs. In some cases successful performance depends on inhibition of certain reaction steps. Careful pH control often is required for successful operation. Anammox organisms have very low growth rates necessitating long SRTs, and long process start-up times.

For plant wide mass balancing during design, the whole plant configuration should be considered in evaluating performance of systems with liquid and solids treatment trains, and sidestream treatment of the return stream. One approach is to use separate unit process models that are available to simulate the activated sludge (AS) train, anaerobic digestion (AD), and the sidestream (SS) processes. A problem with this approach is that the different models are not based on the same set of state variables, and the issue of “passing” streams between AS, AD and SS must be addressed. For example, for oxidized N components, activated sludge models (ASM) typically only consider nitrate; anaerobic digestion models (ADM) do not consider either nitrite or nitrate; sidestream modelling (SSM) requires consideration of both nitrite and nitrate, and the relevant reactions. This transformer approach requires constructing complex model mapping interfaces (transformers) (Wett and Alex, 2003; Vannrolleghem et al., 2005; Volcke et al., 2007). An alternative approach is to apply a single, integrated (AS + AD + SS) model (incorporating all components and reactions of relevance) to the whole plant. i.e. without the need for interfacing (Seco et al., 2004). This approach (the so-called supermodel) is more widely applicable in a process sense than the transformer approach. The main difference between the supermodel and interfaced unit process models is: • a supermodel tracks the fate of components (e.g. active biomasses) in the other processes and thus can provide an estimation of active biomass transfer between processes. In the transformer approach reactions (biomass decay) are instantaneously applied to the whole population, • the supermodel directs the research emphasis towards the actual processes taking place (e.g. autotrophic decay in a digester), • the transformer approach requires detailed elemental composition of each state variable which is not readily available • the transformer approach relies on fixed composition of state variables and is not readily applicable in practice for situations when elemental compositions change, i.e. due to different lipid/carbohydrate content of influent. This paper describes an integrated model developed for whole-plant simulations, with a particular focus on simulation of autotrophic mainstream and sidestream reactions. Calibration of the model to mainstream and sidestream conditions is illustrated. Application of the model to simulation of a sidestream process pilot plant illustrates the evaluation of different operation modes and control strategies. PLANT-WIDE MODEL Table 1 summarizes key process considerations that a model for side-stream processes should include. Several models for a number of side-stream processes have been reported. For example, Wett and Rauch (2003) developed a two-step nitritation-denitritation model based on detailed data from two full-scale reject water SBR treatment processes. Koch et al. (2002) and van Hulle (2005) incorporated Anammox reactions into a two-step nitrification/denitrification model. Volcke (2006) developed a two-step nitrification and denitrification model to represent the SHARON process. The reported models have been refined and incorporated into a general

model that includes activated sludge, anaerobic digestion and physical-chemical models in the BioWin simulation platform. Table 1 - Summary of key process aspects that a model for side-stream processes must include Process Aspect Model Process Important Considerations Nitrification Different growth rates, temperature • AOB growth and decay dependencies and inhibition effects. • NOB growth and decay Heterotrophic Differences in yield must be accounted • Growth on substrate through Denitrification for. denitritation (using nitrite as an electron acceptor) • Growth on substrate through denitratation (using nitrate as an electron acceptor) Deammonification • Growth and decay of Anammox Appropriate inhibitions (i.e. nitrite (Anammox) toxicity) and limitations must be bacteria included. pH pH modeling is essential because, for • All significant equilibrium example, some inhibition effects are relationships (i.e. nitric and caused by unionized species nitrous acid, ammonia and concentrations. carbonate system) Gas-liquid Gas-liquid interactions are essential to • Stripping of certain model interactions components such as ammonia and represent pH and in some cases, to properly represent growth-limiting carbon dioxide conditions.

The process model integrated with the sidestream model in this paper is the full General Activated Sludge/Anaerobic Digestion Model (ASDM) implemented in BioWin™ (EnviroSim, 2007). This model includes reactions for activated sludge, anaerobic digestion and sidestream environments, as well as pH, gas transfer and chemical precipitation, all within the same model matrix. The model tracks over 50 components, with more than 80 processes acting on these components; in summary: • • • • • • • • • • • •

Aerobic heterotrophic growth with complex substrate, acetate, propionate and methanol Anoxic heterotrophic growth on nitrate and nitrite with complex substrate, acetate and propionate Growth of phosphorus accumulating organisms and storage of polyphosphate Assimilative nitrate and nitrite reduction Anoxic growth of methylotrophs on nitrate and nitrite Growth of ammonia and nitrite oxidizer biomasses Growth of Anammox microorganisms Growth of autotrophic and heterotrophic methanogens Decay of all nine (9) active biomasses in different environments Anaerobic fermentation of complex substrate and propionate Various hydrolysis, ammonification and colloid flocculation reactions pH estimation based on the phosphate, carbonate, ammonia, acetate and propionate systems, including strong acids and bases, plus other relevant reactions

• • •

Precipitation of various calcium, magnesium, aluminium and iron complexes (struvite, HDP, HAP, etc) Gas transfer of O2, CO2, N2, NH3, H2 and CH4 gases Inorganic suspended solids fixation during polyphosphate storage and heterotrophic growth

The stoichiometry of all these processes has been derived systematically from elemental balances. Biochemical oxidation and reduction processes have been balanced with respect to C, H, O, N, P and inorganic ions (Takacs et al., 2007). The models main kinetic and stoichiometric constants are listed in Table 2.

Table 2 - Plant-wide model – autotrophic biomass parameters Parameter Max. spec. growth rate [1/d] Arrhenius on max. spec. growth rate Substrate (NH4) half sat. [mgN/L] Substrate (NO2) half sat. [mgN/L] Aerobic decay rate [1/d] Anoxic/anaerobic decay rate [1/d] Nitrous acid inhibition constant [mmol/L] Nitrite inhibition constant [mgN/L] Nitrite toxicity constant [L / (d mgN) ] DO half sat. [mgO2/L] bicarbonate switch [mmol/L] Low pH switch High pH switch Yield [mgCOD/mgN]

AOB 0.9 1.072 0.7 0.17 0.08 0.005 0.25 0.1 5.5 9.5 0.15

NOB 0.7 1.06 0.05 0.17 0.08 0.075 0.5 0.1 5.5 9.5 0.09

Anammox 0.1 1.10 2.0 1.0 0.019 0.0095 100 0.016 0.01 4.0 5.5 9.5 0.114

This model was used for all runs presented in this paper, using the BioWin software. MODEL CALIBRATION Calibration to High F/M nitrification rate tests High F/M tests conducted to measure nitrification rates (Melcer, 2003), often result in a significant accumulation of nitrite in addition to nitrate. Therefore, these tests provided excellent data for calibrating parameters related to nitritation (i.e. conversion of ammonia to NO2) as mediated by ammonia oxidizing autotrophic bacteria (AOBs) and nitratation (i.e. conversion of nitrite to nitrate –NO3) as mediated by nitrite oxidizing autotrophic bacteria (NOBs). These tests represented a case where ammonia concentrations were relatively high. Figure 1 shows a configuration that was used to simulate a High F/M test. The results from the simulation are compared to measured data in Figure 2.

Figure 1 - Model configuration used to simulate a High F/M test for measurement of nitrification rates

Figure 2 - Simulation results and measurements from a High F/M for measurement of nitrification rates Batch Test Nitrogen Response 150 Batch Test NH3-N NH3-N Measured Batch Test NO3-N NO3-N measured Batch Test NO2-N NO2-N measured

CONC (mg/L)

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90

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Calibration to Low F/M nitrification rate tests Low F/M tests conducted to measure nitrification rates (Melcer, 2003), sometimes result in some accumulation of nitrite. In such cases, these tests provided excellent data for calibrating parameters related to nitritation (i.e. conversion of ammonia to NO2) as mediated by ammonia oxidizing autotrophic bacteria (AOBs) and nitratation (i.e. conversion of nitrite to nitrate –NO3) as mediated by nitrite oxidizing autotrophic bacteria (NOBs). These tests represented a case where ammonia concentrations were low. The model configuration used to simulate a low F/M nitrification rate test in a sequencing batch reactor is shown in Figure 3. The results from the simulation are compared to measured data in Figure 4. Figure 3 - Model configuration used to simulate a Low F/M test for measurement of nitrification rates

C ON C E N TR A TION (m g/L)

Figure 4 - Simulation results and measurements from a Low F/M test for measurement of nitrification rates Nitrogen & Ammonia - July 22, 2004 35 30 25 20 15 10 5 0 hr

2 hr

4 hr

6 hr

8 hr

10 hr

TIME SBR Ammonia N SBR NOx-N

Obs NH3-N Obs NOx-N

SBR Nitrate N

Obs NO3N

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Obs NO2N

12 hr

Calibration to Sharon Process Data Data published on a pilot-scale SHARON process (Caffaz et al., 2005) have been simulated to verify the ability of the model to predict nitritation only and the washout of NOBs under suitable conditions. The configuration used to simulate this process is shown in Figure 5.

Figure 5 - BioWin configuration used to simulate a pilot scale SHARON process

Table 3 lists the most important parameters of the system. Table 4 summarizes the characteristics of the centrate. The results from the simulation are compared to measured data in Figure 6.

Table 3 - Key design and operating information on a simulated nitritation reactor Reactor Parameter Value Nitritation Volume 7.4 m3 Dissolved Oxygen 3 mg/L Temperature 35oC

Table 4 - Characteristics of the centrate before treatment in the nitritation reactor Parameter Flow COD TKN Ammonia Total P TSS

Value 5.03 286 745 730 65 67

Units m3/d mg/L mgN/L mgN/L mgP/L mg/L

Figure 6 - Simulation results and measurements for a pilot scale SHARON process 1,000

Concentration (mg/L)

900 800

730

730

700 600 500 400 300 157

200

127

100 0

Centrate

Effluent

NH3-N (Obs)

NH3-N

1,000

Concentration (mg/L)

900 800 700 546

600

573

500 400 300 200 100

24

0

8.9

Effluent NO2-N (Obs)

NO2-N

NO3-N (Obs)

NO3-N

MODEL APPLICATION Alexandria Sanitation Authority Pilot Plant A sidestream treatment process pilot plant using a sequencing batch reactor (SBR) configuration has been constructed and started-up by the Alexandria Sanitation Authority (ASA). Initial simulations have been run with the current model to evaluate the design conditions and operating

strategies for the pilot plant. Table 5 summarizes the assumed and measured characteristics of the centrate feed. Table 5 - Characteristics of the centrate before treatment in the ASA Pilot Plant Parameter Total COD (not including methanol) Unbiodegradable particulate fraction of total COD Biodegradable soluble fraction of total COD Unbiodegradable soluble fraction of total COD Inorganic suspended solids Ammonia PO4-P Alkalinity

Value 500 0.40 0.30 0.30 50 1025 25 50

Units mg COD/L mg COD/mg COD mg COD/mg COD mg COD/mg COD mg/L mgN/L mgP/L mmole/L

Nitritation/Denitritation Mode The initial operating mode of the pilot plant was nitritation/denitritation with methanol addition. Table 6 summarizes the design and operating characteristics of the system. Wett et al. (2007) describe implementation of the DO control system utilized in this system. Aeration in this process is intermittent; when aeration is on, DO increases and ammonia is converted to nitrite, and when aeration is off, nitrite is removed through denitritation. Aeration is switched on/off based on changes in pH [see Fig. 7].

Table 6 - Design and Operating Characteristics of ASA Pilot Plant in Nitritation/Denitritation Mode Parameter Value Maximum Operating Volume (Vmax) 176 SBR Cycle Length 8 Methanol COD concentration in feed 2050 Daily Feed Volume (Vfeed) 87 Feed per cycle 29 Minimum Operating Volume (Vmin) 147 Height of Liquid at Vmax 3.59 Height of Liquid at Vmin 3.0 Reactor Cross Sectional Area 0.049 Daily Wastage Volume (Vwaste) 18 Wastage per Cycle 6 Approximate HRT 1 1.86 Approximate SRT 2 9 Dissolved oxygen (DO) setpoint 0.6 High pH setpoint (pHHi) 7.1 Low pH setpoint (pHLo) 7.3 Notes: 1 Approximate HRT = (Vmax + Vmin)/(2*Vfeed) 2 Approximate SRT = (Vmax + Vmin)/(2*Vwaste)

Units L hrs mg COD/L L L L m m m2 L L days days mg/L

Figure 7 - Simulation of pH, air supply rate and dissolved oxygen concentration in the ASA pilot plant operating in Nitritation/Denitritation mode

7.4

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1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 6:00 AM

12:00 PM

Time SBR Dissolved oxygen

The simulated organism concentrations in the SBR are shown in Figure 8. (The organism concentrations drop to low levels during the settle phase because they are being plotted at the top of the reactor). It is apparent that the nitrite oxidizing biomass has been washed out so that the reaction is proceeding from ammonia to nitrite during the aerobic phase, and from nitrite to nitrogen gas during the anoxic phase. Denitritation is being mediated by anoxic methanol utilizers. Figure 9 shows the resulting ammonia and nitrite concentrations in the SBR. There was no nitrate in the SBR. Figure 8 - Predicted organism concentrations in the ASA pilot plant operating in Nitritation/Denitritation mode. 4,000

Concentration (mg/L)

3,500 3,000 2,500 2,000 1,500 1,000 500

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Time SBR Ammonia oxidizing biomass SBR Non-polyP heterotrophs

SBR Nitrite oxidizing biomass SBR Anoxic methanol utilizers

Figure 9 - Predicted ammonia-N and nitrite-N concentrations in the ASA pilot plant operating in Nitritation/Denitritation mode. 140

Concentration (mg/L)

120 100 80 60 40 20 0 6:00 AM

12:00 PM

6:00 PM

Time SBR Ammonia N

SBR Nitrite N

12:00 AM

Deammonification Mode Subsequent to the nitritation/denitritation mode of operation at the ASA pilot plant, the methanol feed to the system was stopped and seed sludge containing Anammox organisms was added to the reactor. The seed sludge was obtained from the full-scale DEMON plant Strass in Austria. The design conditions for this operating mode were the same as for the nitritation/denitritation mode as summarized in Table 6, except for the elimination of the methanol in the feed, and an adjustment in the pH and DO control setpoints (Table 7). Table 7: Design and Operating Characteristics of ASA Pilot Plant in DEMON Operating Mode Parameter Methanol COD concentration in feed Dissolved oxygen (DO) setpoint High pH setpoint (pHHi) Low pH setpoint (pHLo)

Value 0 0.3 6.97 7.01

Units mg COD/L mg/L

When operating in the nitritation/deammonification mode, the pH control band must be narrower to prevent the accumulation of nitrite, which is toxic to the Anammox organisms. The nitrogen concentrations in the SBR when operated using the design pH control settings are shown in Figure 10. Nitrate is present in significant amounts because it is produced in the Anammox reaction. The nitrite-N concentration is controlled to less than 5 mg/L in this case. The impact of control settings on the performance of the process is shown in Figure 11. After simulating for one day with the settings shown in Table 7, the DO setpoint and the pH upper and lower setpoint were changed back to the settings used in the Nitritation/Denitritation mode (shown in Table 6). The increase in ammonia and nitrite levels, and the decrease in nitrate indicates a reduction in the activity and performance of the Anammox organisms. In addition, the increased accumulation of nitrite will lead to an irreversible inhibition of the organisms.

Figure 10 - Predicted ammonia-N, nitrate-N and nitrite-N concentrations in the ASA pilot plant operating in Nitritation/Deammonification mode 200 180

Concentration (mg/L)

160 140 120 100 80 60 40 20 0 6:00 AM

12:00 PM

6:00 PM

Time SBR Ammonia N

SBR Nitrite N

SBR Nitrate N

12:00 AM

Figure 11 - Predicted ammonia-N, nitrate-N and nitrite-N concentrations in the ASA pilot plant operating in Nitritation/Deammonification mode changing to the same controller setpoints as in the Nitritation/Denitritation mode after one day of simulation. 200 180

Concentration (mg/L)

160 140 120 100 80 60 40 20 0 06:00

12:00

18:00

00:00

06:00

12:00

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00:00

Time SBR Ammonia N

SBR Nitrite N

SBR Nitrate N

CONCLUSIONS This paper describes an integrated model developed for whole-plant simulations, with a particular focus on simulation of autotrophic mainstream and sidestream reactions. Calibration of the model to mainstream and sidestream conditions is illustrated. Application of the model to simulation of a sidestream process pilot plant illustrates the evaluation of different operation modes and control strategies. REFERENCES Caffaz, S., C. Lubello, R. Canziani, and D. Santianni (2005). “Autotrophic nitrogen removal from supernatant of Florence’s WWTP digesters”. Proc. IWA Specialized Conference Nutrient Management in Wastewater Treatment Processes and Recycle Streams, Krakow, Poland, 19-21 September, 2005. EnviroSim 2007: BioWinTM Koch G., Egli K, Van der Meer J.R. and Siegrist H. (2002) Mathematical modelling of autotrophic denitrification in a nitrifying biofilm of a rotating biological contactor. Wat. Sci. Tech., 41(4-5), 191-198. 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 characterisation in activated sludge modeling. Water Environment Research Foundation (WERF), Alexandria, VA, USA. Seco A., Ribes J., Serralta J., Ferrer J. (2004): Biological nutrient removal model No.1 (BNRM1). Water Sci Technol. 2004;50(6):69-78.

Takács, I.; Vanrolleghem, P.A.; Wett, B.; Murthy, S.: Elemental balancing-based methodology to establish reaction stoichiometry in environmental modeling. Proc. Watermatex 2007, Washington DC Van Hulle, Stijn (2005). Modelling, simulation, and optimization of autotrophic nitrogen removal processes. Ph.D. thesis, Ghent University, Belgium. Vanrolleghem P.A., Rosen C., Zaher U., Copp J., Benedetti L., Ayesa E. and Jeppsson U. (2005) Continuity-based interfacing of models for wastewater systems described by Petersen matrices. Wat. Sci. Tech., 52(1-2), 493-500. Volcke, E.I.P. (2006). Modelling, analysis and control of partial nitritation in a SHARON reactor. Ph.D. thesis, Ghent University, Belgium. Volcke E.I.P., van Loosdrecht M.C.M. and Vanrolleghem P.A. (2006) Continuity-based model interfacing for plant-wide simulation : A general approach. Wat. Res., 40, 2817-2828. Wett, B. and W. Rauch (2003). The role of inorganic carbon limitation in biological nitrogen removal of extremely ammonia concentrated wastewater. Water Research, 37, pp. 11001110. Wett, B. (2005). Solved scaling problems for implementing deammonification of rejection water. Proc. IWA Specialized Conference Nutrient Management in Wastewater Treatment Processes and Recycle Streams, Krakow, Poland, 19-21 September, 2005. Wett, B., Murthy, S., Takács, I., Hell, M., Bowden, G., Deur, A., O’Shaughnessy, M. (2007). Key Parameters for Control of Demon Deammonification Process. Nutrient Removal 2007: State of the Art. Water Environment Federation Specialty Conference Series, Baltimore, March 4-7, 2007.