Dynamic modelling of nitrification in an aerated facultative lagoon

ARTICLE IN PRESS WAT E R R E S E A R C H

42 (2008) 424– 432

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Dynamic modelling of nitrification in an aerated facultative lagoon Dwight Houwelinga,, Lynda Kharounea, Antoni Escalasb, Yves Comeaua a

Department of Civil, Geological and Mining Engineering, Ecole Polytechnique of Montreal, P.O. Box 6079, Station Centre-Ville, Montreal, Quebec, Canada H3C 3A7 b Centro de Investigacio´n y Estudios de Posgrado, Facultad de Ingenierı´a, Universidad Auto´noma de San Luis Potosı´. Av. Dr. Manuel Nava No. 8, Edificio P, Zona Universitaria, C.P. 78290, San Luis Potosı´, SLP, Me´xico

ar t ic l e i n f o

abs tra ct

Article history:

Faced with the need to improve ammonia removal from lagoon wastewater treatment

Received 4 April 2007

plants (WWTPs) operated in Quebec, Canada, mechanistic modelling has been proposed as

Received in revised form

a tool for explaining the seasonal nitrification phenomenon and to evaluate optimization

26 June 2007

and upgrade scenarios. A lagoon model that includes a modified activated sludge biokinetic

Accepted 18 July 2007

model and that assumes completely mixed conditions in the water column and sediments

Available online 27 July 2007

has been applied to simulate 3 years of consecutive effluent data for a lagoon from the

Keywords: Modelling Pond systems Aerated facultative lagoon Nitrification Wastewater

Drummondville WWTP. Successful prediction of results from this plant indicates that the seasonal nitrification is determined by temperature, dissolved oxygen (DO) concentrations, hydraulic retention time (HRT) of the water column and washout driven by a well-mixed water column. Results also indicate that sediments contribute to the ammonia load in the lagoon effluent, particularly in spring and early summer. Sensitivity analyses performed with the model indicate that the nitrification period could be prolonged by increasing DO concentrations in the lagoon and that bioaugmentation would be particularly effective in spring and early summer. Limitations of the model are discussed, as well as ways to improve the hydraulic model. & 2007 Elsevier Ltd. All rights reserved.

1.

Introduction

1.1.

Problem statement

Aerated facultative lagoons and other pond systems are widely used in Canada for the treatment of municipal wastewaters with over 450 plants in the province of Quebec alone. These plants effectively remove 5-day biochemical oxygen demand (BOD5), total suspended solids (TSS) and total phosphorus (by chemical precipitation with alum). Ammonia removal, however, is unreliable due to cold winter temperatures that inhibit nitrification for much of the year and therefore only occurs during summer and fall months. This

problem has attracted increased attention since ammonia was listed as a toxic substance under the Canadian Environmental Protection Act of 1999. More recently, the Canadian government published a guideline regulating ammonia concentrations in effluents greater than 5000 m3/d (Environment Canada, 2004). In response to the need to upgrade existing lagoon plants in Quebec to meet new ammonia effluent requirements, modelling was proposed as a tool for explaining the causes of seasonal nitrification and for evaluating approaches to improving nitrifying conditions in aerated facultative lagoons (AXOR, 2005a). Empirical models based on first-order kinetics and the assumption of completely mixed conditions, which

Corresponding author. Tel.: +1 514 340 4711x4279; fax: +1 514 340 5918.

E-mail address: [email protected] (D. Houweling). 0043-1354/$ - see front matter & 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2007.07.034

ARTICLE IN PRESS WAT E R R E S E A R C H

are used to design lagoons for BOD5 removal (MENV, 2001), were shown to be unable to predict the seasonal nitrification phenomena (AXOR, 2005b). Development of a more ‘‘mechanistic’’ process model that would closely simulate the biology and hydraulics of a pond system was therefore proposed. In addition to acting as a tool for evaluating ammonia removal, further anticipated benefits included reevaluating aeration requirements, predicting best sludge management strategies as well as evaluating upgrade strategies.

1.2.

42 (2008) 424 – 432

425

model no. 2d (ASM2d) in which conversion rates are calculated for ammonia and nitrifying bacteria based on temperature, ammonia and DO concentrations as well as nutrient availability (Henze et al., 1999). Little justification for the ‘‘correctness’’ of the model was given other than simulation results, which closely followed measured ammonia effluent data over a 5-year period. Moreover, the advantage of the 2-CSTR model as opposed to a simple 1-CSTR model was not presented beyond its ability to simulate solids accumulation.

Pond process modelling

One approach to modelling pond systems is to model mass transport using simple hydraulic models based on continuous stirred tank reactors (CSTRs) and a more complex model to account for biochemical activity. This approach is already well established for activated sludge systems and commercial software exists to allow easy implementation of activated sludge process models. For pond systems, this approach has previously been used for modelling processes such as highrate algal ponds (Jupsin et al., 2003) and waste stabilization ponds (Dochain et al., 2003). However, the difficulty in modelling pond systems is that, unlike activated sludge systems, hydraulic behavior is characterized by non-ideal flow (Dorego and Leduc, 1996; Name`che and Vasel, 1998) as well as accumulation of a sediment layer from which nutrients and organic matter can be released (Chabir et al., 2000). Modelling of pond hydraulics based on CSTRs is therefore not as straightforward as it is for activated sludge processes and requires a unique approach. Murphy and Wilson (1974) proposed methods for calibrating hydraulic models that could simulate non-ideal flow and the presence of dead zones using interconnected CSTRs. Calibration of these types of models depends on results from tracer studies and results are best in cases where hydraulic behavior does not vary much due to factors such as changing wind conditions or influent flow. Indeed, Jupsin et al. (2003) modelled the high-rate algal pond system as a ring of equalvolume CSTRs with a high recycle flow. Dochain et al. (2003), however, modelled flow through a single completely mixed reactor for a 1 m deep waste stabilization pond but then modelled the sediments in a separate but connected completely mixed reactor. Other researchers have proposed more sophisticated approaches to modelling pond hydraulics such as models based on computational fluid dynamics (CFD). An advantage of CFD models is that they can predict the presence of dead zones and density currents from first principles and have been used with some success in modelling pond systems (Baleo et al., 2001; Shilton and Harrison, 2003). An important disadvantage of CFD-based models, however, is that they require much more computing power to simulate. Moreover, the CFD models proposed by Baleo et al. and Shilton and Harrisson do not account for the presence of accumulation of sediments. Houweling et al. (2005) showed in a brief paper that it is possible to predict the dynamics of seasonal nitrification based on a complex biokinetic model and a hydraulic model that divides the total volume between two separate CSTRs: one for the water column and one for the sediment layer. The biokinetic model was adapted from the activated sludge

1.3.

Current objectives

Simulating a complex biokinetic model in a simple CSTRbased hydraulic model offers the attraction of sufficient complexity to model the wide range of biological activity found in ponds with the numerical simplicity of simulating reactions under completely mixed conditions. However, the validity of assuming that a pond’s water column and sediments behave as separate CSTRs and whether exchanges between the two zones can be modelled through a single recycle flow must be examined. Given that the hydraulics and biology of pond systems varies so much as a function of HRT, depth, mixing, climate and other factors, the validity of assuming completely mixed conditions will likely vary depending on the type of pond system being modelled. The purpose of this paper is to evaluate the potential of the CSTR-based model proposed by Houweling et al. (2005) for modelling seasonal nitrification in an aerated facultative lagoon. The usefulness of the model as a tool for process optimization is discussed as well as limitations of the model. Finally, evaluation of the current model is used to suggest characteristics of an improved lagoon model.

2.

Study site

The study site was a wastewater treatment plant (WWTP) in Drummondville, Canada, consisting of two parallel trains of four aerated facultative lagoons. This plant was chosen because, owing to its relatively important size, it is one of the rare lagoon plants to have weekly effluent characterization at the exit of individual lagoons. Also, tracer studies performed in the first and final lagoon at this site indicate that hydraulic regime in the water column is close to completely mixed (Houweling, 2006). Design parameters for the plant, presented in Table 1, include a total hydraulic retention time (V/Q) of 46.4 d per train and 11.6 d per lagoon. The plant has been in operation since the fall of 1997 and sampling and measurements of the accumulated sludge blanket have been conducted annually since 1999. Modelling results are presented for only the first lagoon in this paper because this is where nitrification is primarily observed to take place.

3.

Model description

3.1.

Hydraulic model

The 2-CSTR model used by Houweling et al. (2005) includes a ‘‘top’’ aerated completely mixed reactor for the water column

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42 (2008) 424– 432

Table 1 – Design parameters of Drummondville municipal WWTP lagoons Parameter

Volume HRT Surface area Water depth Aeration intensity

Units

3

m d m2 m W/m3

Lagoon No.

Total

1A, 1B

2A, 2B

3A, 3B

4A, 4B

384,000 11.6 68,800 6.5 0.47–1.23

384,000 11.6 68,800 6.5 0.14–0.36

384,000 11.6 49,700 6.5 0.08–0.20

384,000 11.6 49,700 6.5 0.06–0.15

3,070,000 46.4 474,000 – –

Solids separator

3.2.

Aeration Mixing

Aeration Mixing

1-CSTR model

Recycle fraction (R)

Mixing 2-CSTR model

Fig. 1 – 1-CSTR and 2-CSTR models of an aerated facultative lagoons including water column and bottom sludge layer.

0.005

Recycle fraction (R)

and a ‘‘bottom’’ un-aerated completely mixed reactor for the sediment layer. The 2-CSTR model is presented in Fig. 1 along with a simple 1-CSTR model for purposes of comparison. The volume of the sediment reactor is set to the average volume of the sediment layer measured at the plant for the years 2001–2003. The volume of the water column reactor was calculated as the difference between the total volume and the volume of the bottom reactor. Effluent from the top reactor flows through a solids-separator object that performs a mass balance limiting effluent TSS to a user-defined value in the effluent and recycling the remaining solids into the sediment reactor. Flow between the sediment reactor and the water column reactor is determined by the recycle flow fraction ‘‘R’’, which passes through the solids-separator object. The recycle flow fraction ‘‘R’’ was varied during simulations as a function of measured aeration flow in the lagoon as shown in Fig. 2. This variation was implemented using a dynamic input file whose sole purpose was to better simulate the effect of airflow through the diffusers on mixing in the sediments. The relationship in Fig. 2 was introduced to the model in order to account for the observation that higher aeration rates in lagoons could lead to more ammonia release from sediments. Indeed, model simulations showed that varying the slope and the intercept of the curve in Fig. 2 affects ammonia load in the lagoon but not the beginning nor the end of the nitrification period.

0.004 0.003 0.002 0.001 0.000 0.000 0.005

0.010

0.015

0.020

0.025

Air flow (m³/m³/hr) Fig. 2 – Recycle fraction ‘‘R’’ simulated as a linear function of airflow to lagoon.

Biokinetic model

The biokinetic model used by Houweling et al. (2005) was based on ASM2d and is presented in Table 2. In this model, nitrification is modelled as a single step and nutrient uptake of ammonia is taken into account through the growth of BOD5-degrading heterotrophic biomass. Process reactions included in ASM2d for phosphorus accumulating organisms have been eliminated; however, the fermentation reaction of soluble BOD5 into volatile fatty acids was retained. The purpose of this was to enable modelling of volatile fatty acid transformation into CH4 by methanogenic organisms, which are known to be active in lagoon sediments. The growth rate of methanogenic organisms is based on the model proposed by Andrews (1969). Decay of biomass is modelled according to the ‘‘death-regeneration’’ model in which cellular lysis produces biodegradable and ‘‘inert’’ organic matter as well as ammonia and PO4. Decay is not dependant on the presence of

an electron acceptor in the death-regeneration model. Only default biokinetic model parameters were retained from the ASM2d and Andrews model except for the specific growth rate of nitrifying biomass, which was adjusted from 1.00 to 0.95 d1. It would be possible, however, to achieve similar calibration by adjusting the active volume of the water column or the temperature dependency coefficient of the growth rate. An additional first-order reaction has been included in the model to account for the transformation of particulate ‘‘inert’’ organic matter into biodegradable organic matter, which can take place in pond systems (Marin, 1987). The empirical decay constant for this reaction ‘‘ki’’ was calibrated based on simulation results, presented in Fig. 3, which show solids accumulation in the 2-CSTR model over a 5-year period.

ARTICLE IN PRESS

Table 2 – Biological model used to simulate aerated facultative lagoon No.

Process

Hydrolysis 1 Aerobic hydrolysis 2 Anoxic hydrolysis 3 Anaerobic hydrolysis 4 Hydrolysis of inert organic matter Heterotrophic organisms 5 Growth on fermentable substrate, SF 6 Growth on fermentation products, SA 7 Denitrification with fermentable substrates, SF 8 Denitrification with fermentation products, SA 9 Fermentation 10 Lysis Nitrifying organisms 11 Aerobic growth of XNIT 12 Lysis of XNIT Methanogenic organisms 13 Growth of XMTH with fermentation products 14 Lysis of XMTH

Simplified formula XS-SF XS-SF XS-SF XI-XS SF-XH SA-XH

SF-SA XH-XS+XI SNH+SALK-XNIT XNIT-XS+XI SA-XMTH+SCH4 XMTH-XS+XI

Simultaneous precipitation of phosphorus 15 Precipitation

XMEOH+SPO4 -

16

SALK+XMEPO4 -

XMEPO4 +SALK Redissolution

XMEOH+SPO4 Note: Eqs. (1)––(12), (15) and (16) from ASM2d (1999); Eq. (4) is a firstorder decay reaction; Eqs. (13) and (14) from Andrews (1969). Symbols: SA: volatile fatty acids; SALK: alkalinity; SCH4 : methane; SF: fermentable substrate; SNH: ammonia; SPO4 : ortho-phosphates; XH: heterotrophic biomass; XI: inert organic matter; XMEOH: ‘ferrichydroxide’; XMEPO4 : ‘ferric-phosphate’; XMTH: methanogenic biomass; XNIT: nitrifying biomass; XS: slowly biodegradable organic matter.

3.3.

7

Influent fractions

Influent fractions for the model’s state variables were determined based on measured chemical oxygen demand (COD), BOD5, TSS, volatile suspended solids (VSS), total Kjeldahl nitrogen (TKN), ammonia and phosphorus from the influent as well as soluble COD from the plant effluent. Since organic matter in the model is expressed in COD units, equivalence between COD and TSS was calculated based on calibrated particulate COD to VSS ratios and measured VSS to TSS ratios. The soluble unbiodegradable fraction of the COD was estimated based on the measured soluble COD from the plant effluent, which was assumed to be entirely composed of unbiodegradable soluble COD from the influent. Fractions of biodegradable and ‘‘inert’’ particulate COD were estimated by comparing influent total COD with influent BOD5 and assuming a BOD5 to ultimate BOD ratio of 1.5. A standard

Measured Modelled TVS with ki =0 Modelled TVS with ki =0.001

6 5 4 3 2 1 0 1997

SF-XH SA-XH

427

42 (2008) 424 – 432

Total volatile solids accumulation (kg/m³)

WAT E R R E S E A R C H

1998

1999

2000 Year

2001

2002

2003

Fig. 3 – Calibration of the constant ‘‘ki’’ for degradation of inert organic material using solids accumulation data in the 2-CSTR model.

fraction of organic nitrogen was assumed for each of the organic state variables and influent ammonia was assigned to the difference between influent TKN and the sum of the organic nitrogen.

3.4.

Model simulation

Measured variations of influent loads and plant operating conditions were taken into account during model simulations using input files. Input files were read dynamically by the simulation software and included data for varying flows, TSS, BOD5 and TKN loads as well as lagoon temperatures and DO concentrations for the years 2001, 2002 and 2003. DO concentrations in the lagoon’s water column were simulated using an input file that assigned the DO state variable to measured values. Although the model has the capability to predict DO concentrations based on oxygen transfer and biomass respiration rates, these predictions were not found to be reliable mainly because removal of organic matter, through biological activity and settling, was not calibrated in the model. An input file was used to assign DO concentrations to measured values during model simulations in order to improve ammonia removal predictions. As a consequence, the effect of biokinetic reactions presented in Table 2 is to simulate the assimilation of ammonia through growth processes and the release of ammonia through death and hydrolysis processes but not competition for DO. Competition between heterotrophic bacteria and nitrifying bacteria for DO is simulated indirectly, however, because periods of lower DO concentrations were measured and simulated by means of the input file. Because of the gradual accumulation of sediments over many years, which occurs in pond systems, it is not possible to simulate steady-state conditions. Initial conditions in the water column were therefore determined by simulating the model for at least a full year prior to the calibration period. Initial conditions in the sediment reactor of the 2-CSTR model

ARTICLE IN PRESS

Results

4.1.

Predicting nitrification period

Simulation results for the 1- and 2-CSTR models compared with measured results in the first lagoon at Drummondville are presented in Figs. 4, 5 and 6, respectively, for the years 2001, 2002 and 2003. Figs. 4A, 5A and 6A show the varying temperatures and DO concentrations measured in the lagoon,

Table 3 – Range of values for simulation parameters Parameter

Influent Q BOD5 TN

Nitrification kinetics mNIT Koa Kna

Reactors Vaerated Vnonaerated Temperature Lagoon temperature Solids separator Recycle fraction (R) through the nonaerated reactor Effluent TSS Effluent VSS

Units

Range of values

m3/d mg/L mg N/ L

12,500–47,000 55–230 9–26

d1 mg/L mg N/ L

0.95 0.5 2.0

m3 m3

281,000 24,500

1C

– mg/L mg/L

0.5–24

0.001–0.003 40 35

10 8

30

A

Temp

25 20

6

15 4

10 DO DO corrected

2

5

0

Temperature (oC)

4.

which were used as a dynamic input to the 1- and 2-CSTR models. Measured effluent ammonia concentrations, presented in Figs. 4B, 5B and 6B, show a nitrification period beginning in July for 2001 and August for 2002 and 2003 at lagoon temperatures around 23–25 1C. Loss of nitrification was observed to occur gradually during the months of October and November at lagoon temperatures between 20 and 10 1C. Significant dips in the ammonia effluent concentrations are observed in the month of April 2001 and over a longer period in the spring of 2003. These dips are not due to nitrification but rather the result of dilution of the influent caused by spring snowmelt. For the year 2001, simulation results for both the 1- and 2-CSTR models are able to predict the beginning of the nitrification period as well as loss of nitrification beginning in October. Simulated loss of nitrification is more rapid than was observed, however. Greater loss of nitrification is simulated in the model due to limiting DO concentrations at the end of September (see the dotted line in Fig. 4A). Since no effect on nitrification rates is observed in the measured data, this suggests that the measured values were not representative of average DO concentrations in the lagoon at this time. A corrected DO profile was therefore developed and simulation results based on this new profile show good agreement with the observation of gradual loss of nitrification during

Dissolved oxygen (mg O2 /L)

were determined by simulating solids accumulation from the date of plant start-up. It was found that, due to the very low concentrations of biomass in the lagoon water column, simulation results were sensitive to the influent ‘‘seed’’ concentration of nitrifying biomass. A constant concentration of 104 mg/L of nitrifying biomass was simulated in this paper based on results of nitrification tests carried out using the high F:M method (WERF, 2003). Sensitivity analyses showed that simulation results would be unaffected by varying the influent biomass concentration over a range of 103–106 mg/L. Calibration of the model for the 2001 data set was based on adjusting the nitrifying biomass maximum specific growth rate and the relationship between recycle flow rate through the sediment reactor and the measured aeration flows (for the 2-CSTR model). Model simulations for the 2002 and 2003 data used the same parameter values as for the 2001 data set, presented in Table 3, and thus served to validate the calibrated model. All model simulations were run using the GPS-X 4.1 software (Hydromantis, 2001).

42 (2008) 424– 432

Effluent NH4 (mg N/L)

WA T E R R E S E A R C H

0

B

1-CSTR model ; DOcorr. 2-CSTR model 2-CSTR model ; DOcorr.

15

10

5 Measured

Nitrifying biomass (mg/L) Effluent NO2+3 (mg N/L)

428

0 15

C Measured 2-CSTR model; DOcorr.

10 5 0 10

D 2-CSTR model; DOcorr.

1 0.1 0.01 1E-3 1E-4

Jan Feb Mar Apr May Jun

Jul

Aug Sep Oct Nov Dec

Fig. 4 – Calibration of 1- and 2-CSTR models with lagoon effluent data from 2001.

ARTICLE IN PRESS

20

6

15 4

10 DO DOcorrected

2

5

0

0

B 10

5

1-CSTR model; DOcorr. 2-CSTR model 2-CSTR model; DOcorr.

0 15

C Measured 2-CSTR model; DOcorr.

10 5 0 10 1

8

D 2-CSTR model; DOcorr.

0.1 0.01 1E-3 1E-4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

A

Temp

25 20

6

15

4

10

2

5

DO

0

0

B

Measured

15

30

10

Temperature (oC)

25

Dissolved oxygen (mg O2 /L)

Temp

Effluent NH4 (mg N/L)

8

A

Temperature (oC)

30

10

429

42 (2008) 424 – 432

Nitrifying biomass (mg/L) Effluent NO2+3 (mg N/L)

Nitrifying biomass (mg/L)

Effluent NO2+3 (mg N/L)

Effluent NH4 (mg N/L)

Dissolved oxygen (mg O2 /L)

WAT E R R E S E A R C H

15

10

5

Measured 1-CSTR model 2-CSTR model

0 15

C Measured 2-CSTR model

10 5 0 10 1

D 2-CSTR model

0.1 0.01 1E-3 1E-4 Jan Feb Mar Apr May Jun Jul

Aug Sep Oct Nov Dec

Fig. 5 – Validation of 1- and 2-CSTR models with lagoon effluent data from 2002.

Fig. 6 – Validation of 1- and 2-CSTR models using lagoon effluent data from 2003.

October and November. Model results thus indicate that loss of nitrification in 2001 is uniquely a function of decreasing growth rates of nitrifying biomass, due to decreasing lagoon temperatures, which do not keep up with the loss of nitrifying biomass in the lagoon effluent. In other words, loss of nitrification is attributed to washout. Effluent nitrate concentrations are less accurately predicted by the model because they are determined by denitrification processes (nitrate removal) as well as nitrification (nitrate production). Denitrification could be more accurately simulated by calibrating organic matter removal in the model. Nonetheless, agreement between dips in both the measured and the simulated nitrate profile indicates that the model was able to simulate denitrifying activity in the lagoon. The simulated nitrifying biomass concentration presented in Fig. 4D provides interesting insights into the relationship between biomass concentrations in the lagoon and seasonal nitrification. Fig. 4D shows nitrifying biomass concentrations reaching a maximum of about 1 mg/L during summer months and then gradually decreasing in fall and winter. Concentrations continue to decrease in spring, reaching a minimum of 0.7 mg/L at the beginning of May, when lagoon temperatures are at 14.5 1C, after which they begin to increase along with increasing lagoon temperatures. Two dips occur in the growth curve, in mid-May and mid-June, which can be attributed

to low DO concentrations during these periods. When the nitrification period begins in July, nitrifying biomass concentrations are at 0.2 mg/L and the lagoon temperature is 21 1C. The nitrification period ends in November when biomass concentrations are at 0.3 mg/L and the temperature is about 10 1C. For the year 2002, simulation results presented in Fig. 5B show that both the 1- and 2-CSTR models predict nitrification a full month in advance of what was observed; however, both models correctly predict the loss of nitrification in the lagoon in fall. Also of note is that both models over-predict ammonia removal during the months of July and August when nitrification is observed to be occurring only partially. Partial nitrification is predicted correctly, however, in the month of September and the model predicts this due to DO limitation. This suggests that DO limitation is also the cause of partial nitrification in July and August and that measured DO concentrations used in simulations were not representative of average DO concentrations in the lagoon during this period. As was done for 2001, a corrected DO profile was developed for the 2002 data set and this allows the models to more closely simulate the beginning of the nitrification period. A large correction of the DO profile was required in this case as can be seen by comparing the solid and the dotted lines in Fig. 5A. This could suggest that some factor other than

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unrepresentative DO measurements could be affecting nitrification rates in the lagoon, such as pH or toxic inhibition. For the year 2003, simulation results presented in Fig. 6B show that both the 1- and 2-CSTR models correctly predict the beginning and the end of the nitrification period within less than a week without any need to adjust the DO profile. Both models over-predict the ammonia removal immediately after the onset of nitrification at the end of July but then correctly predict partial ammonia removal in August and this due to limiting DO concentrations in the water column. Simulated concentrations of nitrifying biomass in the water column for 2002 and 2003, presented in Fig. 5D and 6D, are similar to those for 2001 (Fig. 4D). As for 2001, simulated nitrifying biomass concentrations reach a maximum concentration of about 1 mg/L at the beginning of the nitrification period in July 2002 and 2003. The 2002 profile resembles that observed in 2000, with biomass concentrations dropping to 0.5 mg/L (compared with 0.7 mg/L in May 2001) before beginning to slowly increase. Fig. 5D shows that the reason nitrification is delayed a month in 2002 is that nitrifying biomass fails to increase, due to limiting DO, in the lagoon until July. The same phenomenon is observed in 2003, presented in Fig. 6D, when biomass concentrations reached their minimum as late as July 18 despite favorably high temperatures. This highlights the role limiting DO plays in determining the nitrification at this plant. Both the 1- and 2-CSTR models correctly predict the slope of the ammonia curve during loss of nitrification in the months of October and November for the three data sets presented in Figs. 4B, 5B and 6B. This serves to validate the assumption of completely mixed conditions in the lagoon’s water column as far as modelling biomass concentrations. By comparison, a plug-flow hydraulic model would have simulated very rapid loss of nitrification in fall. However, the need to correct the DO concentrations indicates that this assumption may not be appropriate for modelling DO concentrations in the lagoon. Indeed, measurements made in the first lagoon at the Drummondville WWTP indicate that, although temperatures in the water column are close to homogeneous in summer months, considerable error is induced by this assumption for lagoon temperatures in winter and DO concentrations year-round.

42 (2008) 424– 432

summer is that aeration rates are increased at the plant during this period in order to compensate for higher oxygen demand. Since aerators are subsurface, increasing the aeration rate can increase mixing and re-suspension of sediments. This was taken into account in the model by varying the flow rate from the sediment reactor to the water column in accordance with the relationship presented in Fig. 2. The success obtained in simulating increased ammonia concentrations in the effluent based on this approach indicates that aeration rate in the lagoon is likely an important factor in determining rates of release of ammonia from the lagoon sediments.

4.3.

Optimization scenarios

Two optimization scenarios were studied to explore the possibilities of using the model as a predictive tool to evaluate lagoon optimization or upgrade scenarios. The effect of increasing DO concentrations in the lagoon to 2 and 10 mgO2/L was simulated and compared with results simulated using measured effluent DO values for the 2001 data set. Results from the DO sensitivity analysis, presented in Fig. 7, indicate that increasing DO concentration to 2 mg O2/L could speed up the start of nitrification by about 3 weeks. Increasing the DO concentrations in the lagoon to 10 mg O2/L, a DO concentration close to saturation, could speed up the onset of nitrification by an additional 2 weeks. The effects of increased DO in the fall are to extend nitrification by about 2 weeks for DO ¼ 2 mg O2/L and 3 weeks for DO ¼ 10 mg O2/L. The effect of increasing the concentration of nitrifying biomass in the influent, known as bioaugmentation, was evaluated by comparing simulation results using influent concentrations of 0.01 and 1 mg/L with 0.0001 mg/L that was used in the calibrated model. Results of the bioaugmentation sensitivity analysis, presented in Fig. 8, indicate that increasing influent concentration to 0.01 mg/L could speed up the onset of the nitrification period by about 2 weeks but would have no effect on extending the period of nitrification in the fall. Increasing the influent concentration to 1 mg/L, however,

20

Comparison of 1- and 2-CSTR models

A comparison of model predictions for ammonia removal using the 1- and 2-CSTR models indicates that the effect of sediments on ammonia removal is to increase ammonia concentrations in the effluent. A comparison of Figs. 4B, 5B and 6B indicates that increased ammonia load from the sediments occurs year-round but particularly during the spring and early summer. This is due to the fact that little conversion of particulate organic matter into soluble organic matter and nutrients (processes 1–4 in Table 2) occurs at cold temperatures. High rates of release of ammonia as well as PO4 and soluble organic matter then occur in spring and summer when higher temperatures allow higher rates of hydrolysis of particulate organic matter accumulated over the winter. An additional factor that could explain higher rates of ammonia release from the sediments in spring and early

Effluent NH4 (mg N/L)

4.2.

15

DOcorrected DO=2 mg O2/L DO=10 mg O2/L

10

5

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fig. 7 – Simulated effect of increased concentrations on effluent NH4 for 2001.

lagoon

DO

ARTICLE IN PRESS WAT E R R E S E A R C H

Effluent NH4 (mg N/L)

20

15

XNIT=0.0001 mg/L XNIT=0.01 mg/L XNIT=1 mg/L

10

5

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fig. 8 – Simulated effect of bioaugmentation on effluent NH4 for 2001.

would result in effluent ammonia concentrations below 5 mg N/L from mid-April to late October in addition to lowering ammonia concentrations year-round. Likely dosing scenarios would therefore involve additions of large quantities of highly concentrated nitrifying biomass in the spring to early summer period. Continual addition of high concentrations of nitrifying bacteria on the order of 1 mg/L of influent flow would likely be infeasible, however.

5.

Discussion

Comparing simulation results using the 1- and 2-CSTR models indicates that good predictions of the beginning and end of the nitrification period can be obtained without taking into account interactions with the sediment layer. This result indicates that the loss of nitrification in fall is uniquely the product of washout in the effluent and not settling of biomass into the sediments. This conclusion may not be applicable in systems where aeration (mixing) intensities are below the range of 0.47–1.23 W/m3 of the lagoon studied in this paper. Further comparison of results from the 1- and 2-CSTR models shows the role of sediments in accumulating organic matter and, through hydrolysis processes, producing ammonia that can be released into the water column. The ability of the 2-CSTR model to accurately simulate the increased ammonia load from the sediments, particularly in spring and early summer, indicates that modelling interactions between the water column and lagoon sediments through a small recycle flow is a valid modelling approach. Improved model calibration achieved by linking the magnitude of the recycle flow to aeration intensity in the lagoon further indicates that aeration intensity is a significant factor in interactions between the water column and sediments. One benefit of modelling the effect of sediments on ammonia effluent concentrations is that the effect of removing sediments can consequently be simulated. The 2-CSTR model has the same potential to simulate the organic matter load from the sediments. Calibration of CH4 gas production processes in

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the model would first be necessary, however, to properly close the COD mass balance in the model. The need to develop ‘‘corrected’’ DO profiles in order to accurately model the end of the nitrification period in 2001 (Fig. 4) and the beginning of the nitrification period in 2002 (Fig. 5) suggests that the assumption of a homogeneous DO concentrations in the lagoon studied is a major limitation in the model. Indeed, onsite measurements indicate that DO concentrations vary from minimum levels at the beginning of the lagoon (where organic loading is highest) to maximum levels near the exit of lagoon. On the other hand, good accuracy in simulating the gradual loss of nitrification in 2001, 2002 and 2003 indicates that assuming completely mixed conditions in the water column in regard to biomass is a valid assumption. An improved hydraulic model for the water column could include separate CSTRs to account for different zones of DO concentration; however, back-mixing between the different zones would be necessary to account for overall mixing of biomass throughout the water column. An improved hydraulic model could thus be modelled as a network of 1- or 2-CSTR models. In any case, residence time distributions developed from tracer studies would be helpful in calibrating the hydraulic model (Murphy and Wilson, 1974). Despite limitations to the 1- and 2-CSTR models regarding assuming completely mixed conditions in the water column, good predictions were obtained of the effects of increasing DO concentrations in the water column as well as bioaugmentation. Firstly, the models are of benefit for identifying the major factors that determine the extent of the nitrification period (temperature, DO and washout) as well as the contribution in ammonia load from the sediments. The model simulator then serves as a calculator that can take into account the dynamic effects of increasing DO or bioaugmentation. This allows the model to predict that there is much greater advantage to bioaugmentation in spring and early summer than there is in fall. Such dynamic effects could not be hand-calculated because of the necessity of assuming steady-state conditions. A comparison of simulation results of the 1- and 2-CSTR modelling approach help to identify the strengths and limitations of the 2-CSTR model proposed by (Houweling et al., 2005). Moreover, this paper allows an evaluation of applying the ‘‘simple hydraulic model—complex biokinetic model’’ to aerated facultative lagoons. This approach has previously been applied to HRAP and waste stabilization pond systems (Dochain et al., 2003; Jupsin et al., 2003). In contrast to the Drummondville aerated facultative lagoons, the systems studied by Jupsin et al. and Dochain et al. were un-aerated, shallow (1 m) systems and simulations were run on a diurnal cycle. It is important that proposed pond models, or modelling approaches, be tested against a wide range of pond systems in order to evaluate their potential and robustness. This will lead to greater consensus about how to best model pond systems and greater use of pond process models as predictive tools.

6.

Conclusions

Seasonal nitrification observed in an aerated facultative lagoon is successfully simulated using a 1-CSTR model that assumes a

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WA T E R R E S E A R C H

completely mixed water column and a complex biokinetic model that accounts for nitrogen and organic matter removal. Interactions with the sediments are ignored in the 1-CSTR model. Results from these simulations indicate that the beginning and end of the nitrification period primarily is dependant on temperature, DO concentrations, HRT of the water column and washout driven by a well-mixed water column. Inaccuracies in predictions of the beginning and end of the nitrification period are attributed to the assumption of homogeneous DO concentrations in the water column, which is inherent in the 1-CSTR model. Recommended improvements to the model are therefore to separate the water column into several CSTRs to account for a DO gradient across the lagoon. Recycle flows would need to be included in such a model in order to account for back-mixing of biomass in the water column. A 2-CSTR model that simulates exchanges between sediments and the water column was simulated and results were compared with those from the 1-CSTR model. This comparison indicates that the advantage of the 2-CSTR model is to predict the ammonia load released from the sediments. Accuracy of model results also indicates that modelling exchanges between the water column and the sediments through a small recycle flow is a satisfactory approach. Prediction of the organic matter load from sediments would be possible with the 2-CSTR model but would first require a calibration of the methane production. In addition to helping identify the important factors governing the seasonal nitrification period (1-CSTR model) as well as the contribution of the sediments to ammonia load (2-CSTR model), the 2-CSTR model was used to perform sensitivity analyses of DO and bioaugmentation. Two advantages of using the model to perform these analyses are (a) the model can account for the interactions of several biokinetic processes and (b) the model can take into account dynamic effects. Model results show that the nitrification period could be extended by as much as 3 weeks in both fall and spring by increasing DO concentrations in the lagoon studied. Model results also indicate that bioaugmentation would have its greatest impact in advancing the onset of nitrification in spring and early summer. Prolonging the period of nitrification in fall and winter, however, would be difficult.

Acknowledgments We thank Marc-Andre´ Desjardins, Gino Be´langer and Alain Rousseau of AXOR Experts-Conseils, Janick Lemay of the Ministry of Municipal Affairs, Sports and Recreation and Francois Chabot of the municipality of Drummondville for their assistance in obtaining data and the realization of this project.

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R E F E R E N C E S

Andrews, J.F., 1969. Dynamic model of the anaerobic digestion process. J. Sanit. Eng. Div. ASCE 95 (1), 95–116. AXOR, 2005a. Research and development project—removal of ammonia from aerated lagoons. Report prepared for the Re´gie d’assainissement des eaux use´es Terrebonne/Mascouche, 132pp.+appendices. Montreal, Canada (in French). AXOR, 2005b. Modelling of ammonia removal in aerated lagoons. Vecteur Environ. 38 (2), 30–40 (in French). Baleo, J.-N., Humeau, P., Le Cloirec, P., 2001. Numerical and experimental hydrodynamic studies of a lagoon pilot. Water Res. 35 (9), 2268–2276. Chabir, D., Ouarghi, H.E., Brostaux, Y., Vasel, J.L., 2000. Some influences of sediments in aerated lagoons and waste stabilization ponds. Water Sci. Technol. 42 (10/11), 237–246. Dochain, D., Gre´goire, S., Pauss, S., Schaegger, M., 2003. Dynamical modelling of a waste stabilisation pond. Bioprocess Biosyst. Eng. 26 (1), 19–26. Dorego, N.C., Leduc, R., 1996. Characterization of hydraulic flow patterns in facultative aerated lagoons. Water Sci. Technol. 34 (11), 99–106. Environment Canada, 2004. Guideline for the release of ammonia dissolved in water found in wastewater effluents. Canada Gazette, Part I, December 4, 2004 138 (49), 3489–3497. Henze, M., Grady, C.P.L., Gujer, W., Marais, G.V.R., Matsuo, T., 1999. Activated sludge model no. 2D ASM2D. Water Sci. Technol. 39 (1), 165–182. Houweling, C.D., 2006. Modelling the removal of ammonia nitrogen in aerated facultative lagoons. Ph.D. Thesis. Department of Civil, Geological and Mining Engineering, Ecole Polytechnique of Montreal, 159pp.+appendices (in French) Houweling, C.D., Kharoune, L., Escalas, A., Comeau, Y., 2005. Modeling ammonia removal in aerated facultative lagoons. Water Sci. Technol. 51 (12), 139–142. Hydromantis, Inc., 2001. GPS-X 4.1. Hamilton, Canada. Jupsin, H., Praet, E., Vasel, J.-L., 2003. Dynamic mathematical model of high rate algal ponds (HRAP). Water Sci. Technol. 48 (2), 197–204. Marin, M., 1987. Biological solids accumulation in aerated facultative lagoons. Sci. Tech. de l’Eau 20 (4), 303–308 (in French). MENV, 2001. Lagoon systems. Guide for the Study of Conventional Technologies for Treatment of Wastewaters of Domestic Origin. Ministe`re de l’environnement du Que´bec, Government of Quebec, Canada, 30pp. (Chapter 6, in French). Murphy, K.L., Wilson, A.W., 1974. Characterization of mixing in aerated lagoons. J. Environ. Eng. Div., ASCE October, 1105–1117. Name`che, T., Vasel, J.L., 1998. Hydrodynamic studies and modelization for aerated lagoons and waste stabilization ponds. Water Res. 32 (10), 3039–3045. Shilton, A., Harrison, J., 2003. Integration of coliform decay within a CFD (computational fluid dynamic) model of a waste stabilisation pond. Water Sci. Technol. 48 (2), 205–210. WERF, 2003. Methods for wastewater characterization in activated sludge modeling. WERF Stock No. 99WWF3. Virginia, USA, 596pp.