Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes

ARTICLE IN PRESS

Control Engineering Practice 12 (2004) 357–373

Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes Krist V. Gernaey*, Sten B. J^rgensen CAPEC, Department of Chemical Engineering, Technical University of Denmark, Building 229, DK-2800 Lyngby, Denmark Accepted 26 March 2003

Abstract This paper describes the implementation of a simulation benchmark for studying the influence of control strategy implementations on combined nitrogen and phosphorus removal processes in a biological wastewater treatment plant. The presented simulation benchmark plant and its performance criteria are to a large extent based on the already existing nitrogen removal simulation benchmark. The paper illustrates and motivates the selection of the treatment plant lay-out, the selection of the biological process model, the development of realistic influent disturbance scenarios for dry, rain and storm weather conditions respectively, the definition of performance indexes that include the phosphorus removal processes, and the selection of a suitable operating point for the plant. Two control loops were implemented: one for dissolved oxygen control using the oxygen transfer coefficient KL a as manipulated variable, the second one for nitrate control in the anoxic zone using the internal recirculation flow rate as manipulated variable. Dynamic simulations for different dissolved oxygen set points illustrate the complex interactions in this plant, and the necessity for a continuous trade off between supplying sufficient oxygen to promote nitrification on the one hand, and the need for low dissolved oxygen concentrations on the other hand to allow sufficient development of phosphorus accumulating organisms. The potential for aeration energy savings in the plant is highlighted based on the dissolved oxygen profiles resulting from open loop simulations with a dynamic dry weather influent scenario. The influence of the dissolved oxygen set point selection on the nitrate control loop performance observed in the simulations further illustrates the need for a plant-wide optimization approach to reach optimal plant performance. r 2003 Elsevier Ltd. All rights reserved. Keywords: Benchmark examples; Performance analysis; Models; Environmental engineering; Water pollution

1. Introduction In recent years a benchmark wastewater treatment plant (WWTP) model has been developed to evaluate nitrogen (N) removal control strategies via simulations (Copp, 2002) based upon the Activated Sludge Model No 1 (ASM1; Henze, Grady, Gujer, Marais, & Matsuo 1987). The benchmark WWTP has allowed implementation and comparison of innovative control strategies for different predefined weather disturbance scenarios, corresponding to dry, rain and storm weather conditions. The control strategy performance on the benchmark WWTP is evaluated by applying several performance *Corresponding author. Tel.: +45-45-25-28-00; fax: +45-45-9329-06. E-mail addresses: [email protected] (K.V. Gernaey), [email protected] (S.B. J^rgensen). 0967-0661/02/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0967-0661(03)00080-7

criteria to the simulation output. Two system performance levels can be distinguished, the process performance and the local control loop performance level. The process performance considers (1) effluent quality, (2) effluent quality violations, and (3) operational cost. The effluent quality related performance criterion is obtained by calculating an effluent quality index that includes the major effluent pollutant concentrations (Copp, 2002). The effluent quality violations criterion reflects the number of times that the effluent concentration of a pollutant exceeded the effluent quality limits during the evaluation period (1 week of simulated data), and also includes calculation of the % of time that each of the effluent quality criteria is violated. The operational cost criterion considers excess sludge production (expressed in kg/d), aeration energy (kWh/d), and pumping energy (kWh/d). Multiplying the operational cost criteria with locally applicable cost factors for sludge production and

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Nomenclature AE ASM1 ASM2d BOD Ck CBOD CCOD CNO3 CNtot COD CPI CPtot CTKN CTSS EQ f SI fXIA ; fXIH ; fXIP IAE iN;k Ij;k iP;k IQ ISE K KL a KL a Rn Mj;k N P PE Psldg PUk

aeration energy consumption rate (kWh/ d) activated sludge model No. 1 activated sludge model No. 2d biochemical oxygen demand concentration of component k (EQ validation) total BOD concentration (EQ validation) total COD concentration (EQ validation) nitrate concentration (EQ validation) total N concentration (EQ validation) chemical oxygen demand cost performance index total P concentration (EQ validation) total organic N concentration (EQ validation) total suspended solids concentration (EQ validation) effluent quality fraction of SI from hydrolysis fraction of inert COD from decay of XA ; XH and XPAO ; respectively integral of the absolute error N fraction in organic component k (k ¼ SF ; SI ; XI ; XS ; XH ; XA or XPAO ) monod inhibition function (j ¼ XH ; XPAO ; XA ; k ¼ SO2 ) (see Table 2) P fraction in organic component k (k ¼ SF ; SI ; XI ; XS ; XH ; XA or XPAO ) influent quality index integral of the squared error proportional gain oxygen transfer coefficient KL a for reactor n (n ¼ 5; 6 or 7) monod function (j ¼ XH ; XPAO ; XA ; k ¼ SO2 ; SNO3 ; SALK ) (see Table 2) nitrogen phosphorus pumping energy consumption rate (kWh/d) sludge production rate (kg/d) pollutant load corresponding to component k

energy can be helpful in transferring the simulation results to the local situation, for example with regard to the feasibility of the implementation of a proposed control strategy. In addition, costs related to effluent fines can also be included in the performance evaluation as part of the operational costs (Vanrolleghem & Gillot, 2002).

Qe Qin Qintr Qrs Qws SA SALK SF SI SN2 SNH4 SNO3 SO2 SO2 Rn SPO4 SS t0 tf Ti Tt WWTP XA XH XI XS XMeOH XMeP XPAO XPHA XPP XTSS aj bk

effluent flow rate influent flow rate nitrate (internal) recycle flow rate return sludge flow rate waste sludge flow rate fermentation products (acetic acid) (ASM2d) bicarbonate alkalinity (ASM2d) readily biodegradable substrate (ASM2d) inert soluble organic material (ASM2d) dinitrogen (ASM2d) ammonium nitrogen (ASM2d) nitrate (plus nitrite) nitrogen (ASM2d) dissolved oxygen (ASM2d) dissolved oxygen set point in reactor n (n ¼ 5; 6 or 7) phosphate phosphorus (ASM2d) readily biodegradable soluble substrate (ASM1) start time (EQ calculation) end time (EQ calculation) integral time constant anti-windup time constant wastewater treatment plant autotrophic (nitrifying) biomass (ASM2d) heterotrophic biomass (ASM2d) inert particulate organic material (ASM2d) slowly biodegradable substrate (ASM2d) metal hydroxide (Ferric hydroxide, Fe(OH)3) (ASM2d) metal phosphate (Ferric phosphate, FePO4) (ASM2d) phosphorus accumulating organisms (ASM2d) organic storage products of XPAO (ASM2d) stored polyphosphate of XPAO (ASM2d) particulate material (ASM2d) cost factor for component j (j¼ EQ; AE, PE or Psldg ) weighting factor for component k (Ck ¼ CBOD ; CCOD ; CTKN ; CNO3 ; CPtot ; CTSS )

The present benchmark WWTP has proven useful for N removal WWTPs but does not allow evaluating the effect of potential control strategies on biological phosphorus removal processes. Optimization of phosphorus (P) removal processes is nowadays one of the key issues in many full-scale WWTPs. Indeed, biological P

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removal is often pursued in European treatment plants as an alternative to chemical P removal based on P precipitation with metal salts such as FeCl3. The purpose of this paper is therefore to demonstrate the capabilities of a combined N and P removal simulation benchmark for evaluating and comparing WWTP control strategies. The paper describes the model development, and then illustrates the simulation benchmark with a number of scenario evaluations focusing on the selection of suitable dissolved oxygen set points.

*

*

*

*

2. Materials and methods The presented model development and simulations were done using MATLAB 6.0/SIMULINK 4.0 (Mathworks, Inc) on a 1 Ghz Windows 2000 PC.

3. Benchmark development 3.1. Treatment plant configuration The ASM1 benchmark plant for N removal (Copp, 2002) consists of 5 reactors with a total volume of 5999 m3, and one sedimentation tank with a volume of 6000 m3. It was first tried to implement the Activated Sludge Model No. 2d (ASM2d; Henze et al., 1999) in this plant configuration, but the simulations showed that biological P removal was not achievable under these conditions (Gernaey, Mussati, Yuan, Nielsen, & J^rgensen, 2002). Therefore, a WWTP configuration with an anaerobic zone was preferred, to promote the development of phosphorus accumulating organisms (XPAO ). The plant lay-out is presented in Fig. 1, and consists of: * *

* *

*

7 biological tanks in series with a sedimentation tank. A total biological tank volume of 6749 m3 (see Table 1 for details). Tanks 1, 2, 3 and 4 are not aerated, but fully mixed. Aeration of tanks 5, 6 and 7 is achieved using a maximum KL a of 10 1/h. Default KL a of 10 1/h in tanks 5 and 6, and 2.5 1/h in tank 7.

*

359

Dissolved oxygen (SO2 ) saturation in tanks 5, 6 and 7 is 8 g (COD)/m3. A non-reactive sedimentation tank with a volume of 6000 m3 (area of 1500 m2 and a depth of 4 m) subdivided into 10 layers. A feed point to the sedimentation tank 2.2 m above the bottom of the sedimentation tank (the feed enters the sedimentation tank in the middle of the 6th layer, counting layers from bottom to top). Two internal recycles: * Nitrate recycle (Qintr ) from the 7th to the 3rd tank at a default flow rate of 300% of the influent flow rate (dry weather conditions: average of 55,338 m3/d). * Return sludge flow, from the underflow of the sedimentation tank to the inlet of tank 1, where it is mixed with the influent. The default return sludge flow rate (Qrs ) is 100% of the influent flow rate (Qin ) under dry weather conditions (average of 18,446 m3/d). Waste activated sludge is pumped continuously from the underflow of the sedimentation tank, at a default waste sludge flow rate (Qws ) of 400 m3/d.

Further explanation about the selection of the default plant operating conditions will be provided below, related to the steady state simulation results. 3.2. Model selection The ASM2d model (Henze et al., 1999) was selected as the basic model for describing P removal processes. The ASM2d model is based on the ASM1 model, and adds biological and chemical P removal as extra processes to it. A full model description can be found in Henze et al. (1999). The basic principles behind the biological P removal model equations in ASM2d are illustrated in Fig. 2: the P accumulating organisms (XPAO ) are modelled with cell internal structure, where all organic storage products are lumped into 1 model component (XPHA ). XPAO organisms can only grow (=form new biomass) using cell internal organic storage material (XPHA ) as a substrate with oxygen (SO2 ) or nitrate (SNO3 ) as electron acceptor. The storage process is not depending on the electron acceptor conditions,

Fig. 1. Lay-out of the benchmark plant for evaluation of control strategies on combined N and P removal processes.

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but is only possible when fermentation products such as acetate (SA ) are available. In practice, it means that the storage process will only take place in the anaerobic activated sludge tanks unless serious process upsets occur. In fact, one of the limitations of models such as ASM2d is that the simultaneous presence of considerable amounts of fermentation products and electron acceptors cannot be described accurately without model extensions (van Veldhuizen, van Loosdrecht, & Heijnen, 1999). The primary aim of this simulation benchmark is to investigate the combined effect of proposed control strategies on N and P removal. Therefore, substantial amounts of XPAO should be present in the activated sludge, to allow biological P removal to take place. One of the potential control handles in a full-scale WWTP is storage of part of the activated sludge under nonaerated conditions, for example when the influent load is low. The advantages of such plant operation are: (1) the generation of fermentation products (SA ) due to hydrolysis of slowly biodegradable substrate (XS ) and fermentation, resulting in improved biological P removal; (2) energy savings due to decreased aeration needs; (3) a lower biomass decay rate. Indeed, according to the experimental results reported in Siegrist, Brunner, Koch, Linh Con Phan, and Van Chieu Le (1999), a differentiation between aerobic, anoxic, and anaerobic autotrophic biomass (XA ) decay rates seems to be

Table 1 Volumes of the biological tanks in the combined N and P removal benchmark plant Volume (m3)

Unit Tank Tank Tank Tank Tank Tank Tank

1, 2, 3, 4, 5, 6, 7,

anaerobic tank anaerobic tank anoxic tank anoxic tank aerobic tank aerobic tank aerobic tank

500 750 750 750 1333 1333 1333

justified. Brdjanovic et al. (2000) and Rieger, Koch, Kuhni, . Gujer and Siegrist (2001) also included reduced decay rates for heterotrophs (XH ) and XPAO under anoxic and anaerobic conditions in their models for biological P removal. Therefore, to obtain more realistic simulation results, and to promote development of sufficient amounts of XPAO in the simulations, the ASM2d model equations for biomass decay were modified to make the decay process rates electron acceptor depending. The original and the modified process rates are given in Table 2. This model modification resulted in 6 extra model parameters (see Table 3). The effect of this model modification was investigated via steady state simulations, and will be discussed further below. The standard set of stoichiometric ASM2d parameters provided by Henze et al. (1999) were applied combined with kinetic parameter values corresponding to a liquid temperature of 15 C (see Table 3). The sedimentation tank model is based on the Takacs double exponential settling velocity model (Takacs, Patry, & Nolasco, 1991). The settling model parameters used are the parameters reported by Copp (2002). 3.3. Influent composition The ASM1 model, the model that has been used for the N removal simulation benchmark, has 13 components, and these do not include suspended solids (XTSS ). The ASM2d model, the model used as the basis for the combined N and P removal simulation benchmark presented in this paper, consists of 19 components (see Table 4), including XTSS : The influent for the combined N and P removal simulation benchmark WWTP was generated based on the available ASM1 influent composition (Copp, 2002). The following assumptions were made: *

The ASM1 component SS (readily biodegradable soluble substrate) consists of 40% SA (acetate, fermentation products) and 60% SF (fermentable,

ASM2d SPO4

SPO4

XPP SA

Storage Anaerobic

XPP XPHA

XPAO Growth

XPHA

SO2 Aerobic SNO3 Anoxic

Fig. 2. Illustration of the basic principles behind the biological P removal process as included in the ASM2d model.

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Table 2 ASM2d decay process rate modifications implemented in the combined N and P removal simulation benchmark to account for electron acceptor dependency of decay rates Process

Original decay rate equation

Modified decay rate equation

Decay of XH bH XH bH ½MH;O2 þ ZH;NO3 ;end IH;O2 MH;NO3 XH Decay of XPAO bPAO MP;ALK XPAO bPAO MP;ALK ½MP;O2 þ ZP;NO3 ;end IP;O2 MP;NO3 XPAO Decay of XPP bPP MP;ALK XPP bPP MP;ALK ½MP;O2 þ ZPP;NO3 ;end IP;O2 MP;NO3 XPP Decay of XPHA bPHA MP;ALK XPHA bPHA MP;ALK ½MP;O2 þ ZPHA;NO3 ;end IP;O2 MP;NO3 XPHA Decay of XA bA XA bA ½MA;O2 þ ZA;NO3 ;end IA;O2 MA;NO3 XA With: MP;ALK ¼ SALK =ðKP;ALK þ SALK Þ; MH;O2 ¼ SO2 =ðKH;O2 þ SO2 Þ;MP;O2 ¼ SO2 =ðKP;O2 þ SO2 Þ;MA;O2 ¼ SO2 =ðKA;O2 þ SO2 Þ;IH;O2 ¼ KH;O2 =ðKH;O2 þ SO2 Þ; IP;O2 ¼ KP;O2 =ðKP;O2 þ SO2 Þ;IA;O2 ¼ KA;O2 =ðKA;O2 þ SO2 Þ;MH;NO3 ¼ SNO3 =ðKH;NO3 þ SNO3 Þ; MP;NO3 ¼ SNO3 =ðKP;NO3 þ SNO3 Þ;MA;NO3 ¼ SNO3 =ðKA;NO3 þ SNO3 Þ

Table 3 Model parameters for the combined N and P removal simulation benchmark WWTP Symbol

Description

Value

Units

Kinetic parameters kH mH qfe bH qPHA qPP mPAO bPAO bPP bPHA mA bA

Hydrolysis rate constant Maximal growth rate on substrate for heterotrophic biomass XH Maximal fermentation rate Decay rate for XH Rate constant for storage of XPHA Rate constant for storage of XPP Maximum specific growth rate of phosphorus accumulating organisms (XPAO ) Decay rate of phosphorus accumulating organisms (XPAO ) Decay rate of stored polyphosphates (XPP ) Decay rate of organic storage products of XPAO (XPHA ) Maximum specific growth rate of autotrophic biomass (XA ) Decay rate of autotrophic biomass (XA )

2.46 4.23 2.11 0.28 2.46 1.23 0.82 0.14 0.14 0.14 0.61 0.09

1/d 1/d 1/d 1/d 1/d 1/d 1/d 1/d 1/d 1/d 1/d 1/d

0.50a 0.33b 0.33b 0.33b

— — — —

0.33a 0.50a

— g N/m3

Extra parameters introduced by the ASM2d model modifications described in Table 2 ZH;NO3 ;end Anoxic reduction factor for endogenous respiration of heterotrophic biomass (XH ) Anoxic reduction factor for decay of phosphorus accumulating organisms (XPAO ) ZP;NO3 ;end ZPP;NO3 ;end Anoxic reduction factor for decay of stored polyphosphates (XPP ) ZPHA;NO3 ;end Anoxic reduction factor for lysis of organic storage products of phosphorus accumulating organisms (XPHA ) ZA;NO3 ;end Anoxic reduction factor for decay of autotrophic biomass (XA ) KA;NO3 Saturation constant for SNO3

Only kinetic parameters (values for 15 C) and parameters introduced through the ASM2d decay process rate modifications (see Table 2) are given. The reference values of all other model parameters can be found in Henze et al. (1999). a Values obtained from Gujer et al. (1999). b Values obtained from Rieger et al. (2001).

*

readily biodegradable substrate), similar to the reference wastewater composition given by Henze et al. (1999). SA and SF are the two components used in the ASM2d model for soluble readily biodegradable substrate, whereas the ASM1 model only has 1 model component (SS ) for this fraction of the organic material. The concentration of the particulate COD components XI ; XS and XH in the 3 influent files for the combined N and P removal simulation benchmark plant (dry, rain and storm weather) is identical to the concentrations of these components in the influent files of the N removal benchmark plant.

*

*

*

The COD:N and COD:P ratios of organic influent components are as suggested in ASM2d. Values can be found in Henze et al. (1999). The total amount of biodegradable N (=organic N+ammonium N) in the ASM2d influent was the same as in the ASM1 influent. This means that the total amount of N in the ASM2d model influent was actually slightly higher because the ASM2d model assumptions consider that the total influent N also includes N contained in the inert (=non-biodegradable) soluble (SI ) and particulate (XI ) COD fractions. The influent orthophosphate concentration (SPO4 ) was proportional to the influent ammonium (SNH4 )

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Table 4 The Dry 1 column represents the flow weighted average dry weather influent composition assuming identical COD and biodegradable N loads as the N removal simulation benchmark dry weather influent. Dry 2, Rain 2 and Storm 2 represent flow weighted average influent compositions for dry weather, rain weather and storm weather scenarios, respectively, assuming a 30% increase of the readily biodegradable COD content Influent flow Dry 1 Dry 2 Rain 2 Storm 2 Unit rate (Qin ) 18446.33 18446.33 21319.75 19744.72 m3/d Component SO2 SF SA SI SNH4 SN2 SNO3 SPO4 SALK XI XS XH XPAO XPP XPHA XA XTSS XMeOH XMeP

0.00 41.70 27.80 30.00 40.03 0.00 0.00 9.01 7.00 51.20 202.32 28.17 0.00 0.00 0.00 0.00 215.49 0.00 0.00

0.00 54.21 36.14 30.00 39.66 0.00 0.00 8.92 7.00 51.20 202.32 28.17 0.00 0.00 0.00 0.00 215.49 0.00 0.00

0.00 46.90 31.27 25.96 34.31 0.00 0.00 7.72 7.00 44.30 175.05 24.37 0.00 0.00 0.00 0.00 186.45 0.00 0.00

0.00 50.65 33.76 28.03 37.24 0.00 0.00 8.38 7.00 51.92 193.32 27.25 0.00 0.00 0.00 0.00 208.46 0.00 0.00

g (COD)/m3 g COD/m3 g COD/m3 g COD/m3 g N/m3 g N/m3 g N/m3 g P/m3 mol HCO3/m3 g COD/m3 g COD/m3 g COD/m3 g COD/m3 g P/m3 g COD/m3 g COD/m3 g SS/m3 g SS/m3 g SS/m3

concentration. The ratio between both soluble components, 3.6 g SPO4 =16 g SNH4 was based on the reference wastewater composition of Henze et al. (1999). The flow weighted average dry weather influent composition obtained based on these assumptions is given as Dry 1 in Table 4. During steady state simulations with the combined N and P removal benchmark plant (see below), it appeared that it was difficult to establish biological P removal in the plant without increasing the concentrations of SF and SA : Therefore, the SF and SA influent load was increased with 30%. Due to the presence of a fraction of N in SF (iN;SF ¼ 0:03), the SNH4 influent concentration was also adjusted to keep the same amount of biodegradable N in the wastewater. The SPO4 concentration was also modified to keep the same SPO4 =SNH4 ratio as for the Dry 1 influent. The flow weighted average dry weather influent composition corresponding to the wastewater with the increased SF and SA load is given as Dry 2 in Table 4. Flow weighted average influent concentrations for the rain and storm weather influent scenarios generated based on the respective N removal benchmark influent scenarios, and using a similar procedure as for the Dry 2 influent, are also included in Table 4 as Rain 2 and Storm 2, respectively.

The influent flow rate (Qin ), SF ; SNH4 ; SPO4 and XTSS concentration profiles of the second week of the dynamic dry weather influent scenario with increased SF and SA load, corresponding to Dry 2 in Table 4, are given in Fig. 3. The diurnal flow rate and pollutant concentration variations are typical for a WWTP. Precisely these disturbances make it very difficult in practice to control the treatment plant. For example, in this dry weather influent scenario the maximum Qin is reached at the same moment as the maximum influent SNH4 concentration, indicating that the influent SNH4 load to the plant changes up to a factor 4 within a few hours. The Qin level and the particulate organic material concentrations (represented as XTSS in Fig. 3, where XTSS is a measure for the sum of all particulate fractions) are lower for day 13 and 14, representing a weekend effect that is related to lower industrial activity in the weekends. For the dynamic rain weather influent scenario (Fig. 4), a long rain event occurs between day 8 and 11. Besides the diurnal variations, unexpected Qin and pollutant concentration variations related to rain events are other frequently occurring disturbances for WWTPs connected to a combined sewer system transporting both wastewater and rain water to the plant. The rain event included in the rain weather influent scenario is characterized by increased Qin levels and decreased influent pollutant concentrations, as illustrated in Fig. 4. The dynamic storm weather influent scenario (data not shown), finally, includes two short storm events. The first storm event occurs on day 9, only lasts a few hours, and shows an increased Qin level and increased influent XTSS concentrations, representing the fact that the sediment in the sewers is transported to the WWTP under these suddenly increased flow rates. The second storm event starts on day 11 and lasts about half a day. This event is quite similar to the rain events in the rain weather influent scenario, i.e. only the Qin level increases, assuming that no more sediment material was available in the sewer system following the first storm event. The soluble and particulate pollutant concentrations are lower during this second storm event. 3.4. Plant performance criteria Similar to the ASM1 based N removal simulation benchmark (Copp, 2002) the performance of the combined N and P removal simulation benchmark WWTP described in this paper is evaluated based on a number of performance indexes. The effluent quality index (EQ) defined for the N removal simulation benchmark was extended to include P, as illustrated in Eqs. (1) and (2). Z tf 1 EQ ¼ PUðtÞ QeðtÞ dt; ð1Þ 1000ðtf  t0 Þ t0

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Fig. 3. Dry weather influent scenario: Influent flow rate (Qin ), SF ; SNH4 ; SPO4 and XTSS profiles.

are calculated according to Eq. (4).

PUðtÞ ¼ PUTSSðtÞ þ PUCODðtÞ þ PUBODðtÞ þ PUTKNðtÞ þ PUNO3 ðtÞ þ PUPtot ðtÞ :

ð2Þ

In Eq. (1), t0 and tf represent the start and end time of the period where the effluent quality index (EQ) is evaluated, and Qe is the effluent flow rate of the wastewater. The pollutant load PUk (kg/d) corresponding to each component k is calculated according to Eq. (3). PUk ¼ bk Ck :

ð3Þ

The factors bk in Eq. (3) are weighting factors that are attributed to each effluent component. In this paper, bTSS ¼ 2; bCOD ¼ 1; bBOD ¼ 2; bTKN ¼ 20; bNO3 ¼ 20; bPtot ¼ 20 are used as values for the weighting factors, i.e. effluent N and P are given equal weights. The bk factors for CBOD ; CCOD ; CNO3 ; CTKN and CTSS are similar to the ones proposed by Copp (2002). Furthermore, for the combined N and P removal simulation benchmark proposed in this paper, the instantaneous concentrations of the different pollutants

CTSS ¼ XTSS ; CCOD ¼ SF þ SA þ SI þ XI þ XS CBOD

þ XH þ XPAO þ XPHA þ XA ; ¼ 0:25ðSF þ SA þ ð1  fSI ÞXS þ ð1  fXIH ÞXH

þ ð1  fXIP ÞðXPAO þ XPHA Þ þ ð1  fXIA ÞXA Þ; CTKN ¼ SNH4 þ iN;SF SF þ iN;SI SI þ iN;XI XI þ iN;XS XS þ iN;BM ðXH þ XPAO þ XA Þ; CNO3 ¼ SNO3 ; CNtot ¼ CTKN þ CNO3 ; CPtot ¼ SPO4 þ iP;SF SF þ iP;SI SI þ iP;XI XI þ iP;XS XS þ iP;BM ðXH þ XPAO þ XA Þ þ XPP þ ð1=4:87ÞXMeP :

ð4Þ

An influent quality index (IQ) can be calculated in a similar way as the EQ, but by changing the BOD coefficient in Eq. (4) from 0.25 to 0.65 (Copp, 2002).

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Fig. 4. Rain weather influent scenario: Influent flow rate (Qin ), SF ; SNH4 ; SPO4 and XTSS profiles.

Similar to the N removal simulation benchmark an effluent quality violation criterion was also used for WWTP performance evaluation. This criterion reflects the number of times that the effluent concentration of a pollutant exceeded the effluent quality limits during the evaluation period (1 week of simulated data), and also includes calculation of the % of time that each of the effluent quality limits is violated. The limits used for these calculations were: CPtot ¼ 1:5 g P/m3, CNtot ¼ 18 g N/m3; SNH4 ¼ 4 g N/m3, CBOD ¼ 10 g/m3, CCOD ¼ 100 g COD/m3 and XTSS ¼ 30 g/m3. The CPtot limit is based on the Danish effluent standard for P, whereas the other values are similar to the values used for the N removal simulation benchmark. For the quantification of the plant operating cost, the operating cost performance index (CPI) as defined in Vanrolleghem and Gillot (2002) was used (Eq. (5)):

sludge production rate (kg/d). Values for AE, PE and Psldg were calculated using similar equations as the ones provided for the N removal benchmark plant (Copp, 2002). The a coefficients in Eq. (5) are the operating cost weighting factors and represent yearly operating costs that can be adapted to the local situation, if needed. The aj values suggested by Vanrolleghem and Gillot (2002) were used for the simulations reported in this paper, i.e. aEQ ¼ 50 (Euro/year)/EQ; aAE ¼ aPE ¼ 25 (Euro/year)/ (kWh/d); asldg ¼ 75 (Euro/year)/(kg TSS/d). Thus, it was assumed that a fine has to be paid for each pollutant unit discharged in the effluent, a regulation that is applied in several European countries.

CPI ¼ aEQ EQ þ aAE AE þ aPE PE þ asldg Psldg :

4.1. Simulation procedure

ð5Þ

In Eq. (5), EQ is the effluent quality index (Eq. (1)), AE and PE represent the aeration and pumping energy consumption rates (kWh/d), respectively. Psldg is the

4. Simulation results

A simulation procedure similar to the N removal simulation benchmark was applied. First, steady state simulations using a flow weighted average dry weather

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influent composition were performed. The state values obtained at the end of the steady state simulations were used as initial values for dynamic simulations, i.e. simulations with dynamic influent scenarios (see Figs. 3 and 4 for examples). Three different dynamic influent scenarios were available, corresponding to 14 days dry weather, 14 days including rain events and 14 days including storm weather. These 3 dynamic influent scenarios represent disturbance scenarios that are typical for wastewater systems with combined sewers, i.e. sewers that transport both wastewater and rain water to the treatment plant. The different scenarios were evaluated based on 28 days of simulations with dynamic influent conditions, where only the last 7 days were used for plant performance evaluation. Of these 28 days, the first 2 weeks always consisted of the dry weather scenario, followed by the dry, the rain or the storm weather scenario for the last 2 weeks. 4.2. Steady state simulations Steady state simulations are simulations where the influent to the plant consists of flow weighted average dry weather values. Steady state was obtained by simulating the open loop plant configuration of Fig. 1 for 300 days. Steady state simulations were first performed to illustrate the influence of the modifications to the ASM2d model and the influent composition, using the default values of the N removal simulation benchmark (Copp, 2002): average dry weather Qin ¼ 18; 446 m3/d; Qrs ¼ 18; 446 m3/d; Qintr ¼ 55; 338 m3/d; KL a R5 ¼ KL a R6 ¼ 10 1/h, KL a R7 ¼ 3:5 1/h. Only the Qws value was varied between 250 and 450 m3/d to simulate the effect of different sludge residence times on the WWTP behavior. Three different series of steady state simulations were performed to evaluate the influence of the ASM2d model modifications and the influent readily biodegradable substrate concentration increase that were presented earlier: (1) Simulations with dry weather influent 1 (see Table 4) and the ASM2d model. (2) Simulations with dry weather influent 1 and the modified ASM2d model (see Table 2 for details on ASM2d model modifications). (3) Simulations with dry weather influent 2 (Table 4) with increased readily biodegradable substrate concentrations, and the modified ASM2d model. The most relevant simulation results are presented in Fig. 5. In that Fig., all values correspond to concentrations obtained at the end of 300 days of steady state simulation. The modified ASM2d model, with electron acceptor depending decay rates, results in decreased effluent SNH4 concentrations compared to the ASM2d

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model (Fig. 5a). This is normal since a reduced decay of autotrophic bacteria (XA ) under anoxic and anaerobic conditions in the modified ASM2d model will result in increased XA concentrations for the same Qws value, and thus a higher nitrification rate in the reactors. The increase of the influent SF and SA load in the 3rd series of simulations does not significantly influence the effluent SNH4 concentrations (Fig. 5a). The modified ASM2d model results in increased effluent SPO4 concentrations compared to the ASM2d model (Fig. 5b), although the decay rate of the cell components involved in the biological P removal process has been reduced under anaerobic and anoxic conditions in the modified ASM2d model. The explanation for the higher effluent SPO4 concentrations for the simulations with the modified ASM2d model is the increased nitrification capacity of the plant, which results in increased production of SNO3 in the aerated reactors 5–7. SNO3 is subsequently transported from reactor 7 to the sedimentation tank, and from there to reactor 1 via the sludge recycle. The presence of higher SNO3 concentrations in tank 1 for the modified ASM2d model compared to the ASM2d model thus results in a more pronounced inhibition of the P release process, because the readily biodegradable substrates entering the WWTP with the influent are now preferentially used for denitrification with SNO3 as electron acceptor instead of using the substrate for anaerobic P release and buildup of cell internal XPHA : The increased SNO3 concentrations in reactor 1 caused by the modification of the ASM2d model are illustrated in Fig. 5c. The 30% increase of the SF and SA load results in a significant decrease of the effluent SPO4 concentrations, because there is now sufficient readily biodegradable substrate available in the influent to remove most of the SNO3 in reactor 1 (Fig. 5c) and to trigger anaerobic SPO4 release in reactors 1 and 2. The increased SPO4 release due to the increased SF and SA load can be observed from the data in Fig. 5d, representing the steady state SPO4 concentrations in reactor 2. For Qws values below 375 m3/d the original ASM2d model shows increasing SPO4 release. The simulations with the modified ASM2d model show a similar trend, but generally with lower SPO4 concentrations in reactor 2 compared to the ASM2d model. The latter is due to the higher SNO3 concentrations in reactor 1 inhibiting anaerobic SPO4 release, as mentioned before. For all Qws values that were evaluated, the simulations with the modified ASM2d model and increased influent SF and SA load result in the highest SPO4 concentrations in reactor 2. A decrease of the Qws value below 300 m3/d does not create any additional SPO4 release for this series of simulations. The reason for this is probably the loss of substantial amounts of XTSS in the effluent of the WWTP (see Fig. 5e), due to an overloaded sedimentation tank. The sedimentation tank overload is the result of a too high XTSS concentration in

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Fig. 5. Results of steady state simulations for the ASM2d model (star symbols), the modified ASM2d model (ASM2d mod1, open circle symbols) and the modified ASM2d model with increased influent SA and SF load (ASM2d mod2, +symbols). The black square represents the final operating point that was selected for the plant. (a) Effluent SNH4 ; (b) Effluent SPO4 ; (c) SNO3 in reactor 1; (d) SPO4 in reactor 2; (e) Effluent XTSS ; (f) XTSS in reactor 7.

the reactors for low Qws values. Fig. 5f illustrates the effect of Qws variations on the XTSS concentration in reactor 7, and indeed shows for the third series of simulations that XTSS concentrations in reactor 7 are more or less constant for Qws values below 300 m3/d.

The constant XTSS level for low Qws values indicates that the sedimentation tank was overloaded under these conditions and could not retain all the biomass in the plant, resulting in considerable XTSS loss to the effluent and a considerable effluent quality deterioration.

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The simulations with the ASM2d model generally result in the lowest XTSS concentrations in reactor 7. The modified ASM2d model shows slightly higher XTSS concentrations compared to the ASM2d model. This is a result of the reduced biomass decay rates under anoxic and anaerobic conditions for the modified ASM2d model, leading to higher biomass concentrations. Finally, the simulations with the modified ASM2d model and the increased influent SF and SA load result in the highest XTSS concentrations since the extra influent COD load, in addition to the effect of the reduced biomass decay rates, will also result in an increase of the amount of sludge produced. Based on these 3 series of steady state simulations it was decided to include both the modified ASM2d model with electron acceptor depending decay rates and the modified influent with increased SF and SA load in the combined N and P removal simulation benchmark plant. The steady state simulations are useful to better understand the complex biological processes that are described with the plant model. In a last phase of the steady state simulations a suitable operating point was selected for the combined N and P removal benchmark plant. A Qws value of 400 m3/d combined with a KL a set point of 2.5 1/d in tank 7 was selected as the operating point, or default plant operating conditions (see Fig. 5). The reduction of the KL a from 3.5, the value used in the three series of simulations included in Fig. 5, to 2.5 1/d resulted in a limited decrease of the nitrification capacity of the plant, but promoted the biological P removal process substantially (see Fig. 5). A Qws value of 400 m3/d ensures a low risk for XTSS washout from the plant under normal dry weather operating conditions. The values obtained for the state variables in the different reactors and the sedimentation tank after 300 days of steady state simulations with these default plant operating conditions were used as initial values for all subsequent open loop simulations with dynamic influent scenarios. Except when explicitly mentioned otherwise, the default plant operating conditions for flow rates and KL a were applied for simulations with dynamic influent scenarios. 4.3. Open loop simulations with dynamic influent scenarios After 2 weeks of open loop simulations with the dynamic dry weather influent scenario, the simulations were continued for another 2 weeks with the different weather disturbance scenarios. The plant performance was evaluated based on the last week of simulated data, and is summarized in Table 5. The simulated effluent concentration profiles for SNH4 ; CNtot ; CPtot and CCOD for the dry weather disturbance scenario are given in Fig. 6. The effluent

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Table 5 Results of open loop simulations for different weather scenarios with the combined N and P removal benchmark plant Disturbance scenario:

Dry

Average effluent concentration SNH4 (g N/m3) SNO3 (g N/m3) CNtot (g N/m3) SPO4 (g P/m3) CPtot (g P/m3) CCOD (g COD/m3) XTSS (g/m3) Plant performance IQ (kg PU/d) EQ (kg PU/d) AE (kWh/d) PE (kWh/d) Psldg (kg/d) CPI (Euro/y)

Rain

Storm

6.70 7.61 15.46 2.83 3.56 46.09 14.90

7.07 5.94 14.26 1.90 2.75 42.13 17.80

7.79 6.35 15.40 2.03 2.87 45.23 17.43

43768.09 8322.50 6229.87 2967.36 3373.30 899053.25

43768.09 10090.79 6229.87 2967.36 3308.07 982575.5

45445.32 9315.82 6229.87 2967.36 3599.60 965691.75

75.89 7.59 70.68 0.00 0.00 0.00

77.38 16.37 73.81 0.00 0.00 3.27

Effluent violations (% of time) SNH4 (g N/m3) CNtot (g N/m3) CPtot (g P/m3) CBOD (g/m3) CCOD (g COD/m3) XTSS (g/m3)

70.24 8.33 95.98 0.00 0.00 0.00

Fig. 6. Open loop simulation output: Effluent SNH4 ; CNtot ; CPtot ; and CCOD dynamics for the dry weather scenario. The horizontal lines represent the effluent discharge limits.

concentration variations are considerably less compared to the influent variations (Fig. 3), because the liquid volume in the tanks and the sedimentation tank work as a buffer volume that reduces the sharpness of any influent concentration pulse. The effluent SNH4 limit is exceeded most of the time, except during the weekend period where the SNH4 load to the plant is lower. The

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effluent CNtot limit is respected most of the time. Only on weekdays this limit is exceeded for a few hours every day, corresponding to the sharp daily influent N load peak to the plant (Fig. 3). The CPtot removal is generally better on weekdays compared to the low loaded weekend days. This is because on weekends less readily biodegradable influent COD is available to denitrify SNO3 in tank 1 and to provoke P release, resulting in a decreased P removal efficiency. For CCOD ; and also for XTSS and CBOD (dynamic simulation results not shown), the effluent limit is not violated for the dry weather scenario. The results for the dry weather disturbance scenario summarized in Table 5 confirm what could be seen already in Fig. 6. Indeed, on the average the effluent SNH4 limit of 4 g N/m3 is exceeded, whereas the CNtot norm is respected. This indicates that ideally more aeration should be provided to allow more nitrification and thus lower effluent SNH4 concentrations. The effluent CPtot limit of 1.5 g P/m3 is also exceeded. The average effluent pollutant concentrations only show slight variations for the 3 weather disturbance scenarios. In general, the main influence of the rain and the storm event seems to be a slight increase of the average effluent SNH4 and XTSS concentrations compared to the dry weather scenario. It furthermore looks like the effluent SPO4 and CPtot concentrations are lower for the rain and storm weather scenario, compared to the dry weather scenario. This phenomenon can be explained because the SNO3 concentrations in the effluent of the plant were generally lower during the rain and the storm event, compared to the dry weather scenario. Thus, less SNO3 was transported to the anaerobic reactor with the sludge recycle during the rain or storm event, which was beneficial for the biological P removal process in the simulations. The effluent XTSS limit was only exceeded shortly for both storm events, as illustrated in Fig. 7, due to a temporary overload of the sedimentation tank resulting in XTSS washout in the effluent. Table 5 also contains information on the plant operating costs, via the CPI calculation. In general, the CPI is lowest for the dry weather disturbance scenario, whereas the highest CPI is obtained for the rain weather disturbance scenario. AE and PE are identical for the 3 weather disturbance scenarios since flow rates and KL a values are constant (open loop simulations). The Psldg is highest for the storm weather scenario, which is probably related to the high influent XTSS concentrations entering the WWTP during the first storm event. The EQ contributes most to the CPI, with 46.3%, 51.3% and 48.2% for dry, rain and storm weather respectively. Thus, any deterioration of the effluent quality results in a significant operating cost penalty.

Fig. 7. Open loop simulation output: Effluent XTSS dynamics for the storm weather scenario. The arrows indicate the occurrence of a storm event.

Fig. 8. Open loop simulation output: SO2 dynamics in reactors 1, 5, 6 and 7 for the dry weather scenario.

The SO2 concentration dynamics in reactors 1, 5, 6 and 7 for the dry weather disturbance scenario are shown in Fig. 8. The SO2 concentration in reactor 1 is always very close to zero, indicating that SO2 is not inhibiting the P release process. The SO2 concentrations in reactors 5 (KL a ¼ 10 1/h), 6 (KL a ¼ 10 1/h) and 7 (KL a ¼ 2:5 1/h) follow the influent pollutant load: SO2 concentrations decrease when the load increases due to increased SO2 consumption and vice versa. The SO2 concentrations in the different reactors thus follow the diurnal and weekly load variations. The pollutant load decrease in the weekends can be observed clearly in Fig. 8 for reactors 5 and 6, since the average simulated

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SO2 concentration in these reactors is higher on days 13 and 14 compared to the normal weekdays.

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The closed loop WWTP configuration was first simulated for 300 days with flow weighted average dry weather influent data and active controllers, but no noise on the SNO3 sensor. The steady state values obtained from this simulation were then used as initial values for 2 times 14 days simulations with dynamic dry weather influent conditions with active controllers and noise on the SNO3 sensor. The last week of the simulation output was used for performance evaluation. The most significant simulation results with respect to effluent quality and WWTP operating costs are summarized in Table 6. The results of the SNO3 control loop performance evaluation can be found in Table 7. Only the scenario with a SO2 set point of 0.2 g (COD)/m3 resulted in an improved EQ compared to the open loop dry weather case (Table 5). The average effluent SNH4 concentrations for this scenario are about 1 g N/m3 higher compared to the open loop case with constant KL a R7 (2.5 1/d). As a result of the reduced nitrification efficiency in the closed loop scenario with SO2 set point=0.2 g (COD)/m3, the biological P removal process performs better compared to the open loop case, and this explains the EQ improvement. With respect to the other plant operating costs, AE is slightly lower whereas PE and Psldg are slightly higher for this scenario compared to the open loop case (Table 5). The CPI for the scenario with SO2 set point=0.2 g (COD)/ m3 is about 10,000 Euro/yr or 1.1% lower than the open loop case. An increase of the SO2 set point in reactor 7 from 0.2 to 0.4 or 0.6 g (COD)/m3 negatively influences the EQ (Table 6). As expected, the average effluent SNH4 concentrations decrease when the SO2 set point in

4.4. Simulations with the N removal benchmark control loops The purpose was to evaluate the two standard N removal simulation benchmark control loops (Copp, 2002) on the WWTP configuration shown in Fig. 1. The first loop controls the SO2 concentration in reactor 7 by manipulating KL a R7: KL a R7 is constrained to a maximum of 10 1/h and a minimum of 0 1/h, whereas the SO2 sensor in reactor 7 is assumed to be ideal, i.e. no delay or noise. Three SO2 set points were evaluated: 0.2, 0.4 and 0.6 g (COD)/m3 respectively. The second loop controls the SNO3 level in the second anoxic reactor, i.e. reactor 4 in the plant configuration of Fig. 1. The internal recycle flow rate (Qintr ) is the manipulated variable, and is constrained to a maximum of 92,239 m3/d (=5 times average dry weather Qin ) and a minimum of 0 m3/d. A SNO3 set point of 1 g N/m3 was applied for all the simulations. The SNO3 sensor in reactor 4 was assumed to have a 10 min time delay with normally distributed (standard deviation of 0.1 g/m3), zero-mean, white noise. Both controllers were PI controllers with anti-windup, and the controller tuning constants were identical to the ones reported for the MATLAB/ SIMULINK N removal benchmark implementation in Copp (2002), i.e. for the SO2 controller the proportional gain (K)=500 1/d/(g (COD)/m3), the integral time constant (Ti )=0.001 d and the anti-windup time constant (Tt )=0.0002 d; for the SNO3 controller K ¼ 15; 000 m3/d/(g N/m3), Ti ¼ 0:05 d and Tt ¼ 0:03 days.

Table 6 Result of closed loop simulations with the combined N and P removal simulation benchmark for different SO2 set points in reactor 7 SO2 set point: Average effluent concentration SNH4 (g N/m3) SNO3 (g N/m3) CNtot (g N/m3) SPO4 (g P/m3) CPtot (g P/m3) CCOD (g COD/m3) XTSS (g/m3) Plant performance IQ (kg PU/d) EQ (kg PU/d) AE (kWh/d) PE (kWh/d) Psldg (kg/d) CPI (Euro/y) Effluent violations (% of time) SNH4 (g N/m3) CNtot (g N/m3) CPtot (g P/m3)

SO2 R7 ¼ 0:2

SO2 R7 ¼ 0:4

SO2 R7 ¼ 0:6

SO2 R5 ¼SO2 R6 ¼SO2 R7 ¼ 1

7.57 6.72 15.44 2.00 2.78 46.04 15.02

5.21 8.25 14.62 4.44 5.05 46.26 14.67

4.19 9.03 14.39 5.62 6.16 46.39 14.52

4.79 8.57 14.51 5.11 5.69 46.33 14.59

43768.09 8039.70 6124.39 3050.29 3431.59 888721.25

43768.09 8555.44 6408.72 2580.18 3271.94 897890.00

43768.09 8870.60 6587.43 2367.22 3192.96 906868.25

43768.09 8744.72 5359.12 2425.97 3228.33 873988.00

78.87 11.01 91.37

62.35 5.80 100.00

53.57 4.76 100.00

60.86 5.65 100.00

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Table 7 SNO3 controller performance (controlled variable only) for the different SO2 control scenarios SO2 set point:

SO2 R7 ¼ 0:2

SO2 R7 ¼ 0:4

SO2 R7 ¼ 0:6

SO2 R5 ¼SO2 R6 ¼ SO2 R7 ¼ 1

Controlled variable (SNO3 ) IAE (g N/m3).d ISE (g N/m3)2.d Maximum absolute deviation from SNO3 set point (g N/m3) Standard deviation of error (g N/m3) Variance of error (g N/m3)2

3.19 2.30 1.34 0.57 0.32

2.60 1.66 1.26 0.49 0.24

2.39 1.37 1.03 0.44 0.20

2.43 1.42 1.04 0.45 0.20

reactor 7 increases, and as a result of the improved nitrification the average effluent SNO3 concentrations increase. The net effect of an increased SO2 set point on the CNtot effluent concentration is thus rather limited, because the lower SNH4 concentrations are compensated by higher SNO3 concentrations. Since both pollutants are equally weighted in the EQ index (see Eq. (3)) a modification of the SO2 set point in reactor 7 can hardly be noticed in the EQ calculation when only CBOD ; CCOD ; CTSS and CTKN are considered, as is the case for the N removal simulation benchmark. It is mainly the severe deterioration of the effluent CPtot concentrations from 2.78 to 5.05 and 6.16 g P/m3 for a SO2 set point in reactor 7 of 0.4 and 0.6 g (COD)/m3 respectively that contributes to a significant EQ deterioration. A correlation coefficient r equal to 0.997 was calculated between the SNO3 concentration in reactor 7 and the EQ. Again, the simulation results point out that a low SO2 set point is needed in the last aerobic reactor to avoid returning too much SO2 and SNO3 to the sedimentation tank, and thus to reactor 1 via the sludge recycle, since the presence of both SO2 and SNO3 will inhibit the P release process in reactor 1. With respect to operating costs, a higher SO2 set point in reactor 7 results in increased AE, whereas PE and Psldg decrease. The Psldg decrease can be explained by the increased biomass decay in the presence of higher SO2 concentrations. The PE reduction is due to the increased SNO3 concentrations in reactor 7, a direct consequence of the improved nitrification efficiency. For the 4 scenarios with aeration control presented in Table 6 a correlation coefficient r equal to 0.99 was calculated between the SNO3 concentration in reactor 7 and PE, thus confirming this correlation. A higher SNO3 concentration in tank 7 implies that a lower Qintr is needed to maintain the SNO3 set point in reactor 4 at 1 mg N/l. With respect to controller performance, the SO2 control loop performs well in all scenarios, and will therefore not be commented any further. The performance of the SNO3 controller for reactor 4, is not very good. The recycle of SNO3 rich mixed liquor from tank 7 to the inlet of tank 3 is often not sufficient to keep the SNO3 set point of 1 g N/m3 in tank 4, because of frequent

leakage of readily biodegradable COD to the anoxic zone on weekdays, coinciding with sudden increases of the denitrification rate and rapid SNO3 depletion in reactor 4. The simulation results in Fig. 9 illustrate this sudden appearance of SA in reactor 4, resulting in an almost immediate drop of the SNO3 concentration to 0 which cannot be compensated sufficiently by the SNO3 controller. The SNO3 controller performance is much better during the low loaded period (days 13 and 14 in Fig. 9) where little or no SA peaks occur in reactor 4. The SNO3 control performance evaluation results provided in Table 7 suggest that the SNO3 control performance is inversely correlated with the value of the SO2 set point in reactor 7. Indeed, a correlation coefficient r of 0.77 is calculated between the SO2 set point in reactor 7 (considering all 4 data points) and the integral of the absolute error (IAE) performance criterion. For the integral of the squared error (ISE) criterion this coefficient equals 0.79. However, comparing Tables 6 and 7 reveals much better correlations between the average SNO3 concentration in reactor 7 and the IAE and ISE criteria, with correlation coefficient values r of 0.99 in both cases, indicating that the SNO3 controller performance is inversely correlated with the average SNO3 concentration in reactor 7. 4.5. Simulation with three SO2 control loops Vanrolleghem and Gillot (2002) proposed that a scenario with 3 SO2 controllers, i.e. one SO2 controller in each aerated tank, should rather be the reference case for the N removal simulation benchmark instead of the scenario with one SO2 controller in the last aerobic tank. The scenario with 3 SO2 controllers was also implemented for the combined N and P removal benchmark plant. Two extra SO2 control loops, identical to the one for reactor 7, were implemented in reactors 5 and 6. A SO2 set point of 1 g (COD)/m3 was chosen for the 3 tanks, as suggested by Vanrolleghem and Gillot (2002). The simulation results are summarized in the last column of Tables 6 and 7. The average effluent SNH4 concentration resulting for this scenario is only 0.6 g N/ m3 higher than for the scenario with 1 SO2 control loop

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Fig. 9. Closed loop simulation output for SO2 set point=0.4 g (COD)/m3. SNO3 control loop results: Internal recirculation flow rate (Qintr ; manipulated variable), SA and SNO3 (controlled variable) concentrations in reactor 4.

in tank 7 with a SO2 set point of 0.6 g (COD)/m3, whereas it results in considerable AE savings. However, as expected the average effluent CPtot concentration is rather high, which is the immediate consequence of the high SO2 set point in tank 7 compared to the other scenarios. The PE is low, which is related to the rather high SNO3 concentration in reactor 7. Indeed, the higher the SNO3 concentration in reactor 7 the lower the volume of mixed liquor that needs to be pumped via the internal recycle to maintain the SNO3 set point of 1 g N/m3 in reactor 4 (SNO3 controller). In general, the scenario with the 3 SO2 controllers results in the lowest CPI for the dry weather disturbance scenario, and this CPI is about 3% lower than the CPI obtained for the open loop case. However it is clear that a more optimal selection of the SO2 set points or an alternative aeration control strategy could result in an even better plant performance.

5. Discussion 5.1. Model selection There are several reasons for the selection of the ASM2d model as the basis for this combined N and P removal simulation benchmark. First of all, the ASM2d model uses similar principles as the ASM1 model for the processes involving heterotrophic and nitrifying bacteria, which should make it easy to switch between both models. Furthermore, the ASM2d model includes the most essential biological P removal processes needed to describe full-scale P removal WWTPs. In addition, the equations for chemical P removal are also included in the model, which allows the evaluation of scenarios with combined biological and chemical P removal.

The modification to the decay process equations of the ASM2d model was justified based on experimental results (Siegrist et al., 1999) and similar developments in other recently reported models for activated sludge systems (Gujer, Henze, Mino, & van Loosdrecht, 1999; Brdjanovic et al., 2000; Rieger et al., 2001). The benefit of this model modification is that more realistic simulation results are obtained, where storage of activated sludge under anoxic or anaerobic conditions during low loaded periods now becomes an attractive alternative actuator to save aeration energy, compared to simulations with the original ASM2d model. However, it could be questioned whether the substrate storage and endogenous respiration concepts introduced by Gujer et al. (1999) should not have been included in the model of the proposed combined N and P removal benchmark plant. Including two anaerobic reactors in the model of a combined N and P removal WWTP (Fig. 1) was needed to sufficiently promote anaerobic P release, thus giving the XPAO a competitive advantage over the other bacteria. Similar approaches are applied in full-scale plants to promote biological P removal. The steady state simulations (Fig. 5) confirmed that an increase of the influent readily biodegradable substrate concentration was needed in addition to the modification of the ASM2d decay process equations for this combined N and P removal WWTP to establish sufficient biological P removal activity. Simulating the combined N and P removal WWTP configuration with identical influent readily biodegradable COD loads as for the N removal simulation benchmark would result in little or no biological P removal, as could be concluded from Fig. 5. It would thus not allow studying the combined effect of control strategy implementation on both

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biological N and P removal processes, which is precisely the aim of the combined N and P removal WWTP presented in this paper. 5.2. Simulation results In general, the proposed combined N and P removal benchmark plant has a wider scope than the N removal simulation benchmark, as simultaneous optimization of two nutrient removal processes now needs to be taken into account. The simulation results illustrate well that there is a continuous trade off between, on the one hand, providing sufficient SO2 to promote the nitrification process and thus the biological N removal processes, and on the other hand keeping low SO2 concentrations in the last aerobic reactor to return as little SO2 and SNO3 as possible to the first anaerobic reactor with the sludge recycle, thus saving on the readily biodegradable carbon source and promoting biological P removal. This trade off can also be seen from the high correlation between the SNO3 concentration in reactor 7 and the EQ, where higher SNO3 concentrations will lead to higher EQ values. At this point, one important deficiency of the model should probably be highlighted. Indeed, a general assumption in the presented simulation benchmark is that no reactions take place in the sedimentation tank, such that the concentrations of the soluble components in reactor 7 are also assumed to be the concentrations in the sludge recycle flow that is pumped from the bottom of the sedimentation tank to the first anaerobic reactor. In a full-scale plant the sludge present in the sludge blanket at the bottom of the sedimentation tank will continue the biodegradation reactions, which will result in consumption of SO2 and, if the residence time of the sludge in the sedimentation tank is sufficiently long, consumption of SNO3 and hydrolysis of slowly biodegradable substrates. Assuming that SO2 and SNO3 have been removed from the return sludge flow would violate the mass conservation laws and is therefore not an acceptable solution. Obtaining a model that is more realistic at this point would probably need the inclusion of a reactive sedimentation tank model in the plant model, i.e. the implementation of a model that combines the activated sludge model reactions with the current sedimentation tank model. Generally, the differences in average effluent pollutant concentrations are low when comparing the open loop simulation results for the different weather scenarios. This can be explained because the treatment plant performance is evaluated over 1 week, such that the temporary decrease of the effluent quality due to the occurrence of a rain or a storm event is averaged over this 1-week period. Furthermore, the liquid volume in the WWTP and the sedimentation tank works as a buffer for any pollutant load peaks. As expected, the rain weather and storm weather disturbance scenarios

result in a decrease of the WWTP performance. Probably alternative actuators such as influent flow rate distribution over several reactors under rain weather conditions could be investigated as a way to reduce the impact of rain or storm events on the plant performance. It is noteworthy that the EQ represents about 45–50% of the plant operating costs, when the CPI is calculated for the open loop simulations for different weather disturbance scenarios (Table 5). Implementation of WWTP control solutions resulting in a considerable improvement of the effluent quality will thus influence the CPI significantly. Addition of alternative actuators such as dosage of an external readily biodegradable carbon source to promote the biological P removal process or addition of chemicals to achieve P precipitation (Gernaey et al., 2002) could result in extension of the CPI calculation with extra cost factors for external carbon source and precipitation chemicals consumption. The SO2 concentration profiles of Fig. 8 illustrate the potential for aeration energy savings. However, at the same time they also indicate the system limitations corresponding to the current plant configuration. Aeration energy savings are possible during low loaded periods, corresponding to the peaks in the SO2 profiles for reactors 5, 6 and 7 in Fig. 8. Any further increase of the SO2 concentration above 1 g (COD)/m3 only results in a limited increase of the nitrification rate, because of the Monod saturation function SO2 =ðKA;O2 þ SO2 Þ that is included in the nitrification process rate equation. Indeed, for a KA;O2 value of 0.5 g (COD)/m3 used in the simulations in this paper, an increase of the SO2 set point from 1.0 to 1.5 g (COD)/m3 will only result in a 12.5% increase of the nitrification rate while resulting in a substantial increase of the AE demands. A comparison of the AE for the scenario with 3 SO2 controllers with set point 1 g (COD)/m3 (Table 6) with the open loop simulation with the dry weather scenario (Table 5) clearly illustrates the potential for AE savings. On the other hand, during high loaded periods the maximum KL a in reactor 5, for example, would be a constraint in case the SO2 set point concentration is for example selected to be 1.5 g (COD)/m3. The open loop simulations show that the maximum SO2 concentration that can be reached in reactor 5 during periods with high loads is about 1.3 g (COD)/m3. Compared to the rather simple scenarios described in this paper, several options are available to improve the EQ for this specific N and P removal plant. An influent load estimate, or a measurement of the influent pollutant load, could be used to make the SO2 set points adaptive, i.e. the SO2 set points could be increased for increasing N loads. A similar approach could be applied to the SNO3 control loop. Another alternative to improve the nitrification efficiency would be to implement intermittent aeration in tank 4, thereby using the

ARTICLE IN PRESS K.V. Gernaey, S.B. Jørgensen / Control Engineering Practice 12 (2004) 357–373

surplus aeration capacity that is available in the current WWTP. It was indeed noticed during the simulations that the maximum KL a in reactor 7 (10 1/d) was never reached for the low SO2 set points that were selected in the scenarios presented, indicating that a surplus aeration capacity is available. The simulation results show the existence of interactions between the control loops, and this was illustrated in Table 7 via the influence of the SO2 set point selection on the SNO3 control loop performance. Furthermore, the simulation results also indicate in the case of the SNO3 control loop that the control loop performance is not constant, but depends on the plant loading conditions (Fig. 9). Indeed, the SNO3 control loop performed better on low loaded weekend days compared to weekdays. The simulation results thus indicate the need for a plant-wide optimization approach, for example using model predictive control, to take into account the complex interactions between the control loops. Adding additional control loops such as external carbon source addition, return sludge control or addition of chemicals for P precipitation will only increase the complexity of the problem.

6. Conclusions A simulation benchmark for evaluation of the influence of control strategies on the combined N and P removal performance of a biological wastewater treatment plant was developed. A plant layout, a biological process model, realistic influent disturbance weather scenarios and performance indexes were defined. Two control loops were implemented for dissolved oxygen and nitrate control respectively. Dynamic simulations for different dissolved oxygen set points illustrated the complex interactions in this plant, and the necessity for a continuous trade off between supplying sufficient oxygen to promote nitrification on the one hand, and the need for low dissolved oxygen concentrations on the other hand to allow sufficient development of phosphorus accumulating organisms. The clear influence of the dissolved oxygen set point selection on the nitrate control loop performance further illustrates the need for a plant-wide optimization approach to reach optimal plant performance.

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Acknowledgements The authors wish to thank Ulf Jeppsson (IEA, Lund University of Technology, Sweden) for providing the ASM1 benchmark MATLAB/SIMULINK code. The work received support of the European Commission, in the frame of the SMAC (SMArt Control of Wastewater Systems) project (contract number EVK1-CT-200000056).

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