4) The nutrient content in the biomass, endogenous residues and other particulate compounds were decreased to 0.004 (for phosphorus compounds) and 0.025 (for nitrogen) to describe not only the low nutrient assimilation observed in the sludge of the pulp mill but also the low effluent ammonia and orthophosphate concentrations.

Readily biodegradable COD Fbs gCOD/gCODtot 0.64 0.27 0.64 Acetate Fac gCOD/gRBCOD 0.15 0.15 0.21 Non‐colloidal slowly biodegradable COD Fxsp gCOD/gSBCOD 0.50 0.50 0.50 Unbiodegradable soluble COD Fus gCOD/gCODtot 0.14 0.08 0.14 Unbiodegradable particulate Fup gCOD/gCODtot 0.03 0.08 0.16 Ammonia Fna gNH3‐N/gTKN 0.02 0.75 0.02 Particulate organic nitrogen Fnox gN/g OrgN 0.23 0.25 0.11 Soluble unbiodegradable TKN Fnus gN/gTKN 0.05 0.02 0.04 N/COD for unbiodegradable particulate COD FupN gN/gCOD 0.04 0.04 0.02 Phosphate Fpo4 gPO4‐P/gTP 0.58 0.75 0.58 P/COD for unbiodegradable particulate COD FupP gP/gCOD 0.01 0.01 0.00

AOB: Aerobic decay rate bA 1/d 0.04 0.17 1.029 OHOs: Aerobic decay bH 1/d 0.24 0.62 1.029 Switches: Aerobic den. DO half saturation kO,H mgO2/L 0.35 0.05 ‐

Common: N in endogenous residue mgN/mgCOD 0.025 0.07 Common: P in endogenous residue mgP/mgCOD 0.004 0.022 Common: Endogenous residue COD/VSS ratio mgCOD/mgVSS 1.48 1.42 Common: Particulate substrate COD/VSS ratio mgCOD/mgVSS 1.48 1.6 Common: Particulate inert COD/VSS ratio mgCOD/mgVSS 1.48 1.6 OHOs: N in biomass mgN/mgCOD 0.025 0.07 OHOs: P in biomass mgP/mgCOD 0.004 0.022 OHOs: Fraction to endogenous residue fH ‐ 0.2 0.08

**value**

**value**

**Biowin default value**

**Biowin default value**

**Wastewater characterization value**

**Arrhenius**

Table 16.8 displays a summary of the values adjusted during the model calibration.

**Table 16.8 Summary of parameters adjusted during the calibration of the model.**

**Kinetic and stoichiometric parameters Symbol Units Calibrated**

**Wastewater characteristics Symbol Unit Calibrated**

respectively) were adjusted to 0.18 and 0.03, respectively, to match the MLSS

respectively, to describe the MLVSS and ammonia concentrations in the aerobic

,

,

**16.3.5 Model calibration**

reactors.

Stoichiometric parameters

(2002) and Meijer and Brdjanovic (2012):

concentration of 5.3 gTSS/L of the plant.

dissolved oxygen of OHO (KOH) to 0.35.

#### **16.3.7 Assessment of plant improvement and upgrading scenarios**

Using BioWin, eight different configurations and operational alternatives were assessed to evaluate the robustness of the plant and potential for improvement. The assessed scenarios were evaluated from a theoretical perspective. Even though the conclusions drawn below were based on a calibrated/validated model describing the current operation of the WWTP at the evaluated pulp mill, additional experimental *in situ* validation would be desirable to fully confirm the applicability of the proposed alternatives. For instance, it would be necessary to demonstrate the appropriateness of every single alternative described below by conducting experimental pilot scale research at the pulp mill facility.

#### **<IP_ADDRESS> Implementation of chemical phosphorus removal**

A jar test evaluation was carried out at the evaluated pulp mill laboratory facilities to define an adequate coagulant dose. Table 16.10 displays the results of the jar test experiments. A low soluble phosphorus concentration of approximately 0.26 mgP/L can be achieved with 100 mgFeCl3.6H2O/L (experiment E2A, beaker No. 2). An even lower concentration can be reached if 270 mgFeCl3.6H2O are dosed (experiment E2A, beaker No.4), resulting in a phosphorus concentration of 0.11 mgP/L. However, the coagulant requirements are considerably higher (270 mg/L *versus* 100 mg/L) for a relatively marginal removal improvement (from 0.26 to 0.11 mgP/L). Thus, the addition of 100 mgFeCl3.6H2O/L (experiment E2A, beaker No. 2) was selected as the best alternative. This also suggested that the coagulant addition could take place in the end or after the aerobic tank, following a sweep coagulation mechanism, at the regular pH observed at the plant (pH 7.2) without requiring any pH adjustment. The coagulant concentration and addition point were selected and implemented in Biowin (Figure 16.9), leading to a similar outcome of about 0.1 mgP/L in the effluent of the plant.

**Figure 16.9 Modified layout of WWTP with chemical P removal.**

Assuming an average current effluent TP concentration of 0.62 mgP/L (as observed during the sampling campaign) and an effluent flow of 58,925 m3 /d, the estimated phosphorus load is 36.5 kgP/d. Then, if chemical phosphorus removal is applied and an effluent TP concentration of 0.1 mgP/L can be achieved, the effluent phosphorus load will be reduced to 6.5 kgP/d, which is far even lower than the discharge limit set by the local environmental agency of 74 kgP/d. However, the volumes of excess sludge produced per day are estimated to increase from 321 tonTSS/d to 326 tonTSS/d. Or, an increase of 9.8 tonTSS/d if the two treatment lines are considered as parallel lines.

on 15 December 2021

**429**

**Figure 16.10 Soluble phosphorus concentrations in the effluent of the plant as a function of the coagulant**

Beaker 4 270 mg/L

Beaker 3 180 mg/L

FeCL3 x 6H20

Beaker 2 100 mg/L

**Table 16.10 Results of the jar‐tests executed on activated sludge and mixed liquor from the pulp mill wastewater plant of study to assess the chemical phosphorus removal potential under different pH and**

> **E1A E1B** Mixed liquor AS from end of aeration basins

> **E1C E1D**

**E2A E2B** Mixed liquor AS from end of aeration basins

**E2C E2D**

**P total** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ **P soluble (0.2 μm pore size filter)** 0.85 0.85 0.78 0.7 1.49 1.5 1.46 1.53

Effluent from secondary clarifier

**P total** 0.76 0.72 0.74 0.7 0.69 0.67 0.68 0.69 **P soluble (0.2 μm pore size filter)** 0.62 0.61 0.64 0.51 0.63 0.63 0.65 0.61

**P total** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ **P soluble (0.2 μm pore size filter)** 0.7 0.26 **0.23 0.11** 0.96 0.35 **0.16 0.17**

Effluent from secondary clarifier

**P total** 0.66 0.68 0.66 0.66 0.67 0.69 0.68 0.67 **P soluble (0.2 μm pore size filter)** 0.40 **0.23 0.18 0.16** 0.44 **0.22 0.19 0.12**

Default pH 7.4 pH adjusted to 5.5

Default pH 7.7 pH adjusted to 5.5

Default pH 7.2 pH adjusted to 6

Default pH 7.9 pH adjusted to 6

Beaker 1 Beaker 2 Beaker 3 Beaker 4 Beaker 1 Beaker 2 Beaker 3 Beaker 4

Beaker 1 Beaker 2 Beaker 3 Beaker 4 Beaker 1 Beaker 2 Beaker 3 Beaker 4

Beaker 1 Beaker 2 Beaker 3 Beaker 4 Beaker 1 Beaker 2 Beaker 3 Beaker 4

Beaker 1 Beaker 2 Beaker 3 Beaker 4 Beaker 1 Beaker 2 Beaker 3 Beaker 4

Coagulant costs (€/ear)

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

0

Effluent soluble P Coagulant costs

**coagulant doses.**

**Initial effluent:**  Ptotal=0.72 mgP/L Psoluble=0.68 mgP/L

**Initial Mixed liquor AS:**  Psoluble=0.88 mgP/L

**Initial effluent:**  Ptotal=0.72 mgP/L Psoluble=0.68 mgP/L

> 0.1 0.2 0.3 0.4 0.5 0.6

> > 0

Effluent soluble P (mg/L)

0.8 0.7

**dose and costs of coagulant per year.**

Beaker 1 20 mg/L

**Initial Mixed liquor AS:**  Psoluble=0.88 mgP/L

#### **Table 16.10 Results of the jar‐tests executed on activated sludge and mixed liquor from the pulp mill wastewater plant of study to assess the chemical phosphorus removal potential under different pH and coagulant doses.**

**Figure 16.10 Soluble phosphorus concentrations in the effluent of the plant as a function of the coagulant dose and costs of coagulant per year.**

on 15 December 2021

**428**

Assuming an average current effluent TP concentration of 0.62 mgP/L (as observed during the

kgP/d. Then, if chemical phosphorus removal is applied and an effluent TP concentration of 0.1 mgP/L can be achieved, the effluent phosphorus load will be reduced to 6.5 kgP/d, which is far even lower than the discharge limit set by the local environmental agency of 74 kgP/d. However, the volumes of excess sludge produced per day are estimated to increase from 321 tonTSS/d to 326 tonTSS/d. Or, an increase of 9.8 tonTSS/d if the two treatment lines are considered as

/d, the estimated phosphorus load is 36.5

**16.3.7 Assessment of plant improvement and upgrading scenarios**

experimental pilot scale research at the pulp mill facility.

**<IP_ADDRESS> Implementation of chemical phosphorus removal**

outcome of about 0.1 mgP/L in the effluent of the plant.

**Figure 16.9 Modified layout of WWTP with chemical P removal.**

sampling campaign) and an effluent flow of 58,925 m3

parallel lines.

Using BioWin, eight different configurations and operational alternatives were assessed to evaluate the robustness of the plant and potential for improvement. The assessed scenarios were evaluated from a theoretical perspective. Even though the conclusions drawn below were based on a calibrated/validated model describing the current operation of the WWTP at the evaluated pulp mill, additional experimental *in situ* validation would be desirable to fully confirm the applicability of the proposed alternatives. For instance, it would be necessary to demonstrate the appropriateness of every single alternative described below by conducting

A jar test evaluation was carried out at the evaluated pulp mill laboratory facilities to define an adequate coagulant dose. Table 16.10 displays the results of the jar test experiments. A low soluble phosphorus concentration of approximately 0.26 mgP/L can be achieved with 100 mgFeCl3.6H2O/L (experiment E2A, beaker No. 2). An even lower concentration can be reached if 270 mgFeCl3.6H2O are dosed (experiment E2A, beaker No.4), resulting in a phosphorus concentration of 0.11 mgP/L. However, the coagulant requirements are considerably higher (270 mg/L *versus* 100 mg/L) for a relatively marginal removal improvement (from 0.26 to 0.11 mgP/L). Thus, the addition of 100 mgFeCl3.6H2O/L (experiment E2A, beaker No. 2) was selected as the best alternative. This also suggested that the coagulant addition could take place in the end or after the aerobic tank, following a sweep coagulation mechanism, at the regular pH observed at the plant (pH 7.2) without requiring any pH adjustment. The coagulant concentration and addition point were selected and implemented in Biowin (Figure 16.9), leading to a similar

Based on the results from the jar test experiments (Table 16.10, E2A) and the operational conditions at the plant, the actual coagulant dose required and their associated costs were estimated. Figure 16.10 shows the effluent P soluble concentrations as a function of the coagulant dose and costs of coagulant per year. As observed, the addition of 100 mg FeCl3/L appears to be a satisfactory option to decrease and secure a low P concentration in the plant at an estimated annual cost of € 1.159.459.

#### **<IP_ADDRESS> Implementation of enhanced biological phosphorus removal (EBPR)**

By converting the former selector (35,000 m3 ) located at the beginning of the aeration basin to an unaerated zone, EBPR can be achieved at the evaluated pulp mill WWTP. The modelling results predicted that the TP concentration in the effluent can be decreased from 0.62 to 0.26 mgP/L (Figure 16.11). For EBPR to develop, anaerobic conditions must be created and a different group of heterotrophic microorganisms (known as the poly‐phosphate accumulating organisms, PAOs) shall proliferate to enhance the biological P‐uptake.

**Figure 16.11 Modified layout of WWTP with un‐aerated reactor zone.**

The proposed configuration may be a good option to further P removal during the biological treatment by only converting the former selector into an anaerobic selector. This option can be easily achieved by switching off the aeration in the beginning of the basins. However, further research activities, such as the execution of pilot‐tests at the plant facilities, need to be conducted to validate the technical feasibility of this configuration.

#### **<IP_ADDRESS> Assessment of higher influent flows**

The impact of higher influent flows caused by a potential increase in the production of the pulp mill industry was evaluated. The pulp mill WWTP was able to cope with influent flows from 20 up to 100% higher, mostly, because the WWTP is overdesigned. Nevertheless, to keep a MLSS concentration between 4,215 and 4,750 mg/L, the SRT needs to be proportionally reduced from 32 to 26, 23, 20, 18 and 16 days, as the influent flow increases from 0 to 20, 40, 60, 80 and 100%.

However, even though the WWTP would still meet the discharge limits, the effluent TP concentration would increase from 0.62 mgP/L to 0.87 mgP/L. This option needs to be carefully addressed and perhaps combined with another alternative (such as chemical P‐removal and EBPR implementation) to secure the compliance of the discharge standards if higher flows need to be treated.

on 15 December 2021

**431**

**Figure 16.12 Modified pulp mill WWTP layout with only one treatment line in operation.**

**<IP_ADDRESS> Discharge and co‐treatment of municipal wastewater**

the treated effluent of approximately 0.11 mgP/L.

**<IP_ADDRESS> Effects of shorter SRT on plant performance**

**Bentos at the evaluated pulp mill WWTP. Parameter Unit Pulp mill**

Flow m3

3,417 m3

If the municipal wastewater from the city of Fray Bentos (with estimated flow of approximately

indicate that the total effluent P concentrations would considerably increase (up to approximately 0.8 mgTP/L) (Table 5.11). However, if EBPR is implemented (by introducing an un‐ aerated or anaerobic zone, as previously described) the evaluated pulp mill WWTP may have the capacity to satisfactorily treat both effluents while still achieving a final total P concentration in

**Table 16.11 Effects of the discharge and co‐treatment of municipal wastewater from the City of Fray**

COD mgCOD/L 1,588 750 1542 260 TSS mgTSS/L 167 7.6 TN mgTN/L 15.8 60 18.2 1.9 TP mgTP/L 3.7 15 4.3 0.1 MLVSS mgVSS/L 4,236 MLSS mgTSS/L 5,269 SRT days 31

According to model predictions, decreasing the SRT from 32 d to 25, 20, 15 and 10 d would lead to an increase in the effluent phosphorus concentrations from 0.6 mgP/L to 0.7, 0.9, 1.0 and 1.2mgP/L, respectively. A decrease in the SRT may decrease the concentration of total suspended solids in the reactor. Therefore, less biomass would be present and the adsorption of colloidal phosphorus to particulate organic phosphorus would also decrease. This apparently leads to the increase in the effluent TP concentration. This alternative cannot be implemented alone but, if needed, it will need to be combined with another operating strategy such as the implementation of chemical P precipitation or EBPR. Thus, decreasing the SRT may contribute to

If due to maintenance purposes the plant needs to operate with only one wastewater treatment line (Figure 16.12), the model‐based results indicate that this will increase the effluent TP concentration up to 0.8 mgP/L. This will be close to the maximum permissible discharge limit. Thus, in principle, this practice could be applied, but it is not recommendable for long term

**Municipal influent**

/d 58,925 3,416 62,341 59,926

**Total influent** **Effluent Aerobic**

**reactor**

**influent**

reduce the aeration costs but a higher sludge generation will be observed.

periods, only for maintenance purposes and in case of contingencies.

**<IP_ADDRESS> Operation of the plant with only one treatment line**

/d) would be co‐treated at the evaluated pulp mill WWTP, the modelling studies

#### **<IP_ADDRESS> Discharge and co‐treatment of municipal wastewater**

If the municipal wastewater from the city of Fray Bentos (with estimated flow of approximately 3,417 m3 /d) would be co‐treated at the evaluated pulp mill WWTP, the modelling studies indicate that the total effluent P concentrations would considerably increase (up to approximately 0.8 mgTP/L) (Table 5.11). However, if EBPR is implemented (by introducing an un‐ aerated or anaerobic zone, as previously described) the evaluated pulp mill WWTP may have the capacity to satisfactorily treat both effluents while still achieving a final total P concentration in the treated effluent of approximately 0.11 mgP/L.

**Table 16.11 Effects of the discharge and co‐treatment of municipal wastewater from the City of Fray Bentos at the evaluated pulp mill WWTP.**

#### **<IP_ADDRESS> Effects of shorter SRT on plant performance**

According to model predictions, decreasing the SRT from 32 d to 25, 20, 15 and 10 d would lead to an increase in the effluent phosphorus concentrations from 0.6 mgP/L to 0.7, 0.9, 1.0 and 1.2mgP/L, respectively. A decrease in the SRT may decrease the concentration of total suspended solids in the reactor. Therefore, less biomass would be present and the adsorption of colloidal phosphorus to particulate organic phosphorus would also decrease. This apparently leads to the increase in the effluent TP concentration. This alternative cannot be implemented alone but, if needed, it will need to be combined with another operating strategy such as the implementation of chemical P precipitation or EBPR. Thus, decreasing the SRT may contribute to reduce the aeration costs but a higher sludge generation will be observed.

#### **<IP_ADDRESS> Operation of the plant with only one treatment line**

If due to maintenance purposes the plant needs to operate with only one wastewater treatment line (Figure 16.12), the model‐based results indicate that this will increase the effluent TP concentration up to 0.8 mgP/L. This will be close to the maximum permissible discharge limit. Thus, in principle, this practice could be applied, but it is not recommendable for long term periods, only for maintenance purposes and in case of contingencies.

on 15 December 2021

**430**

Based on the results from the jar test experiments (Table 16.10, E2A) and the operational conditions at the plant, the actual coagulant dose required and their associated costs were estimated. Figure 16.10 shows the effluent P soluble concentrations as a function of the coagulant dose and costs of coagulant per year. As observed, the addition of 100 mg FeCl3/L appears to be a satisfactory option to decrease and secure a low P concentration in the plant at

an unaerated zone, EBPR can be achieved at the evaluated pulp mill WWTP. The modelling results predicted that the TP concentration in the effluent can be decreased from 0.62 to 0.26 mgP/L (Figure 16.11). For EBPR to develop, anaerobic conditions must be created and a different group of heterotrophic microorganisms (known as the poly‐phosphate accumulating organisms,

The proposed configuration may be a good option to further P removal during the biological treatment by only converting the former selector into an anaerobic selector. This option can be easily achieved by switching off the aeration in the beginning of the basins. However, further research activities, such as the execution of pilot‐tests at the plant facilities, need to be

The impact of higher influent flows caused by a potential increase in the production of the pulp mill industry was evaluated. The pulp mill WWTP was able to cope with influent flows from 20 up to 100% higher, mostly, because the WWTP is overdesigned. Nevertheless, to keep a MLSS concentration between 4,215 and 4,750 mg/L, the SRT needs to be proportionally reduced from 32 to 26, 23, 20, 18 and 16 days, as the influent flow increases from 0 to 20, 40, 60, 80 and 100%.

However, even though the WWTP would still meet the discharge limits, the effluent TP concentration would increase from 0.62 mgP/L to 0.87 mgP/L. This option needs to be carefully addressed and perhaps combined with another alternative (such as chemical P‐removal and EBPR implementation) to secure the compliance of the discharge standards if higher flows need

) located at the beginning of the aeration basin to

**<IP_ADDRESS> Implementation of enhanced biological phosphorus removal (EBPR)**

an estimated annual cost of € 1.159.459.

By converting the former selector (35,000 m3

PAOs) shall proliferate to enhance the biological P‐uptake.

**Figure 16.11 Modified layout of WWTP with un‐aerated reactor zone.**

conducted to validate the technical feasibility of this configuration.

**<IP_ADDRESS> Assessment of higher influent flows**

to be treated.

#### **<IP_ADDRESS> Implementation of an anaerobic digester for sludge waste treatment**

The incorporation of an anaerobic digester will allow the WWTP to produce energy due to the potential biogas generation. However, the addition of the anaerobic digester would also promote the release of P in the anaerobic digester. This can lead to an increase in the effluent TP concentration due to the internal recirculation flows from the sludge handling facilities to the main treatment lines. Nevertheless, the incorporation of an anaerobic digester, together with the introduction of an unaerated zone (for EBPR implementation as previously discussed), may alleviate that increase in the effluent TP concentration. In addition, by shortening the SRT, and increasing the production of a less stabilized sludge, higher biogas productions could be reached. Figure 16.13 shows the modified layout of the pulp WWTP after the introduction of an anaerobic digester.

**Figure 16.13 Pulp mill WWTP modified layout after the introduction of an anaerobic sludge digester.**

The anaerobic sludge digester considered in this study was designed with a retention time of 21 d. The potential methane generated at the plant by the anaerobic digester was estimated as a function of the SRT applied in the mainstream activated sludge treatment line, which could be shortened to maximize the generation of sludge as shown in Figure 16.14.

**433**

**Figure 16.15 Potential energy savings at the pulp mill as a function of the applied SRT after the potential**

0

40

50

Net energy savings (%)

Energy consumption

Energy production

Net energy savings Net energy consumption

20

10

30

80

90

70

100

60

Further improvements can be incorporated to the previously described scenario. The nutrients (nitrogen and phosphorus) that would be present in the supernatant of the anaerobic sludge digester may be recovered through the addition of magnesium for their precipitation as magnesium‐ammonium‐phosphate (MAP), also known as struvite. The modified layout indicating the required changes and additions to the WWTP to achieve that goal are shown in Figure 16.16. Table 16.12 shows the design parameters for the precipitation of struvite in the

If the SRT is shortened to 5 days, this may lead to an increase of 300% in the methane

implementation of EBPR and the introduction of the anaerobic digester will not affect the effluent quality. The effluent concentrations will remain practically unchanged. For instance, the effluent COD and TP concentrations would remain around 260‐280 mg/L and 0.1 mg/L,

The energy production by the methane produced in the anaerobic digester, the energy consumption (only considering aeration requirements) at the activated sludge process, and the potential net energy savings at the evaluated pulp mill WWTP were estimated after the implementation of the previously discussed scenario. The y‐axis at the left on Figure 16.15 shows the energy production (red line), energy consumption (blue line), and net energy consumption (green line) as a function of the SRT. The y‐axis at the right shows the net energy savings expressed in percentage as a function of the SRT. 16The potential net energy savings may increase from 24% to even 88% of the aeration requirements when implementing the proposed scenario decreasing the SRT from 32 to 5 d. Overall, this alternative looks rather promising and deserves to be thoroughly analyzed due to the potentially remarkable benefits. However, further research activities such as the execution of pilot‐tests at the plant facilities

/d (at 5 d SRT). According to the model simulations, the combined

/d (at 32 d

production. This suggests that the methane production would increase from 1,736 m3

need to be conducted to validate the technical feasibility of this configuration.

**<IP_ADDRESS> Nutrients recovery in the supernatant of the anaerobic digester**

20 15 10 5

days

reactor including the chemical addition dose and the effluent composition.

SRT) to 5,568 m3

respectively, regardless the applied SRT.

**implementation of an anaerobic digester.**

25

0

2,000

1,000

4,000

5,000

3,000

Energy (MJe/h)

32

If the SRT is shortened to 5 days, this may lead to an increase of 300% in the methane production. This suggests that the methane production would increase from 1,736 m3 /d (at 32 d SRT) to 5,568 m3 /d (at 5 d SRT). According to the model simulations, the combined implementation of EBPR and the introduction of the anaerobic digester will not affect the effluent quality. The effluent concentrations will remain practically unchanged. For instance, the effluent COD and TP concentrations would remain around 260‐280 mg/L and 0.1 mg/L, respectively, regardless the applied SRT.

The energy production by the methane produced in the anaerobic digester, the energy consumption (only considering aeration requirements) at the activated sludge process, and the potential net energy savings at the evaluated pulp mill WWTP were estimated after the implementation of the previously discussed scenario. The y‐axis at the left on Figure 16.15 shows the energy production (red line), energy consumption (blue line), and net energy consumption (green line) as a function of the SRT. The y‐axis at the right shows the net energy savings expressed in percentage as a function of the SRT. 16The potential net energy savings may increase from 24% to even 88% of the aeration requirements when implementing the proposed scenario decreasing the SRT from 32 to 5 d. Overall, this alternative looks rather promising and deserves to be thoroughly analyzed due to the potentially remarkable benefits. However, further research activities such as the execution of pilot‐tests at the plant facilities need to be conducted to validate the technical feasibility of this configuration.

**Figure 16.15 Potential energy savings at the pulp mill as a function of the applied SRT after the potential implementation of an anaerobic digester.**

#### **<IP_ADDRESS> Nutrients recovery in the supernatant of the anaerobic digester**

Further improvements can be incorporated to the previously described scenario. The nutrients (nitrogen and phosphorus) that would be present in the supernatant of the anaerobic sludge digester may be recovered through the addition of magnesium for their precipitation as magnesium‐ammonium‐phosphate (MAP), also known as struvite. The modified layout indicating the required changes and additions to the WWTP to achieve that goal are shown in Figure 16.16. Table 16.12 shows the design parameters for the precipitation of struvite in the reactor including the chemical addition dose and the effluent composition.

on 15 December 2021

**432**

**Figure 16.14 Potential methane production at the evaluated pulp mill as a function of the SRT after the**

**<IP_ADDRESS> Implementation of an anaerobic digester for sludge waste treatment**

digester.

The incorporation of an anaerobic digester will allow the WWTP to produce energy due to the potential biogas generation. However, the addition of the anaerobic digester would also promote the release of P in the anaerobic digester. This can lead to an increase in the effluent TP concentration due to the internal recirculation flows from the sludge handling facilities to the main treatment lines. Nevertheless, the incorporation of an anaerobic digester, together with the introduction of an unaerated zone (for EBPR implementation as previously discussed), may alleviate that increase in the effluent TP concentration. In addition, by shortening the SRT, and increasing the production of a less stabilized sludge, higher biogas productions could be reached. Figure 16.13 shows the modified layout of the pulp WWTP after the introduction of an anaerobic

**Figure 16.13 Pulp mill WWTP modified layout after the introduction of an anaerobic sludge digester.**

shortened to maximize the generation of sludge as shown in Figure 16.14.

**potential implementation of an anaerobic digester.**

SRT (days)

20 15 10 5

25

0 32

2,000

1,000

4,000

3,000

Methane (m3CH4/d)

6,000

5,000

The anaerobic sludge digester considered in this study was designed with a retention time of 21 d. The potential methane generated at the plant by the anaerobic digester was estimated as a function of the SRT applied in the mainstream activated sludge treatment line, which could be

**Figure 16.16 Modified layout of the pulp mill WWTP with a side‐stream struvite recovery reactor.**

**Figure 16.17 Modified layout of pulp mill WWTP in accordance to current practices implemented in Amsterdam West WWTP for phosphate recovery.**

on 15 December 2021

**435**

The authors would like to express their gratitude to the staff of the pulp mill in Uruguay where this research was conducted. Moreover, the authors would like to acknowledge the financial support provided by both the Agencia Nacional de Investigación e Innovación (ANII) in Uruguay and the Dutch government through

The proposed configuration may lead to the production of up to 346 kg of struvite per day. Recent research carried out at the Amsterdam West WWTP has shown that by precipitating struvite in the digested sludge (before dewatering) the performance of the sludge dewatering centrifuge can increase (van Nieuwenhuijzen *et al.,* 2009; Bergmans, 2011). The amounts of struvite recovered may be higher; eventually, reaching a concentration of approximately 1,611 mg/L corresponding to a load of 432.5 kgISS/d as shown in Table 16.12. The plant layout

Though this case‐study was carried out using the general activated sludge and anaerobic digestion model (ASDM) from Biowin, it is one more example of the needs to improve the coupling of ASM models (Henze *et al.*, 2000) with ADM1 (Batstone *et al.*, 2002), particularly because of the anaerobic digestion fate of EBPR sludge (Ikumi *et al.*, 2014). This can contribute towards the development of an integrated resource recovery strategy, while complying with the corresponding discharge limits. Further research, like the execution of pilot‐tests at the plant facilities, the anaerobic digestion of the (EBPR) sludge and the nutrient recovery, needs to be conducted to validate the technical feasibility of this configuration. In this regard, the model developed by Ikumi *et al.* (2011) could be tested and compared against the outcomes obtained

The pulp mill WWTP evaluated in this study operates efficiently and therefore meets the effluent water quality standards established by the local environmental regulations (DINAMA, Decree 253/79). The model‐based assessment indicates that its robustness may even allow the pulp mill WWTP to treat higher flows and organic loads while still meeting the discharge standards. Interestingly, the plant exhibited extremely low nutrient requirements (fP of 0.012 mgP/mgVSS and fN of 0.037 mgN/mgVSS) compared to the average requirements reported for municipal wastewater (fp: 0.025 mgP/mgVSS and fn: 0.10 mgN/mgVSS). This may reflect the selection of microbial populations with low nutrient requirements due to their relatively low availability in the influent. Although the ASDM model from BioWin (developed by EnviroSim) was specifically designed for modelling municipal wastewater treatment plants, it was capable of predicting the performance of the pulp mill WWTP. Only minor adjustments were needed that included the adjustment of certain kinetic and stoichiometric parameters like the OHOs and AOB aerobic decay rates and the N and P fractions in the biomass. Based on both the model predictions and laboratory experiments, the effluent TP concentrations can be further decreased by incorporating chemical sweep coagulation. If EBPR is implemented in the system, through the introduction of an unaerated section equivalent to 25% of the total volume, the total P concentration in the treated effluent may be further decreased. This could allow the pulp mill WWTP to co‐treat also the municipal wastewater from the city of Fray Bentos (together with the pulp mill wastewater). The performance of the WWTP may be probably further improved. The effluent TP concentration could probably decrease and a high biogas production and energy savings may be simultaneously achieved by introducing the following modifications/additions: (i) incorporating an anaerobic digester; (ii) converting the actual selector zone of the aeration basin into an unaerated zone; and (iii) decreasing the SRT. Further research, like the execution of pilot‐ tests at the plant facilities, the anaerobic digestion of the (EBPR) sludge and the nutrient recovery, needs to be conducted to validate the technical feasibility of the new proposed

describing this alternative is described in Figure 16.17.

from Biowin.

configurations.

**Acknowledgements**

**16.4. Conclusions**

The proposed configuration may lead to the production of up to 346 kg of struvite per day. Recent research carried out at the Amsterdam West WWTP has shown that by precipitating struvite in the digested sludge (before dewatering) the performance of the sludge dewatering centrifuge can increase (van Nieuwenhuijzen *et al.,* 2009; Bergmans, 2011). The amounts of struvite recovered may be higher; eventually, reaching a concentration of approximately 1,611 mg/L corresponding to a load of 432.5 kgISS/d as shown in Table 16.12. The plant layout describing this alternative is described in Figure 16.17.

Though this case‐study was carried out using the general activated sludge and anaerobic digestion model (ASDM) from Biowin, it is one more example of the needs to improve the coupling of ASM models (Henze *et al.*, 2000) with ADM1 (Batstone *et al.*, 2002), particularly because of the anaerobic digestion fate of EBPR sludge (Ikumi *et al.*, 2014). This can contribute towards the development of an integrated resource recovery strategy, while complying with the corresponding discharge limits. Further research, like the execution of pilot‐tests at the plant facilities, the anaerobic digestion of the (EBPR) sludge and the nutrient recovery, needs to be conducted to validate the technical feasibility of this configuration. In this regard, the model developed by Ikumi *et al.* (2011) could be tested and compared against the outcomes obtained from Biowin.

# **16.4. Conclusions**

The pulp mill WWTP evaluated in this study operates efficiently and therefore meets the effluent water quality standards established by the local environmental regulations (DINAMA, Decree 253/79). The model‐based assessment indicates that its robustness may even allow the pulp mill WWTP to treat higher flows and organic loads while still meeting the discharge standards. Interestingly, the plant exhibited extremely low nutrient requirements (fP of 0.012 mgP/mgVSS and fN of 0.037 mgN/mgVSS) compared to the average requirements reported for municipal wastewater (fp: 0.025 mgP/mgVSS and fn: 0.10 mgN/mgVSS). This may reflect the selection of microbial populations with low nutrient requirements due to their relatively low availability in the influent. Although the ASDM model from BioWin (developed by EnviroSim) was specifically designed for modelling municipal wastewater treatment plants, it was capable of predicting the performance of the pulp mill WWTP. Only minor adjustments were needed that included the adjustment of certain kinetic and stoichiometric parameters like the OHOs and AOB aerobic decay rates and the N and P fractions in the biomass. Based on both the model predictions and laboratory experiments, the effluent TP concentrations can be further decreased by incorporating chemical sweep coagulation. If EBPR is implemented in the system, through the introduction of an unaerated section equivalent to 25% of the total volume, the total P concentration in the treated effluent may be further decreased. This could allow the pulp mill WWTP to co‐treat also the municipal wastewater from the city of Fray Bentos (together with the pulp mill wastewater). The performance of the WWTP may be probably further improved. The effluent TP concentration could probably decrease and a high biogas production and energy savings may be simultaneously achieved by introducing the following modifications/additions: (i) incorporating an anaerobic digester; (ii) converting the actual selector zone of the aeration basin into an unaerated zone; and (iii) decreasing the SRT. Further research, like the execution of pilot‐ tests at the plant facilities, the anaerobic digestion of the (EBPR) sludge and the nutrient recovery, needs to be conducted to validate the technical feasibility of the new proposed configurations.

### **Acknowledgements**

The authors would like to express their gratitude to the staff of the pulp mill in Uruguay where this research was conducted. Moreover, the authors would like to acknowledge the financial support provided by both the Agencia Nacional de Investigación e Innovación (ANII) in Uruguay and the Dutch government through

on 15 December 2021

**434**

**Figure 16.17 Modified layout of pulp mill WWTP in accordance to current practices implemented in**

**Table 16.12 Design and operating parameters for the recovery of nutrients as struvite in the sludge**

Flow 58,925 58,604 m3

COD 1,588 266 mgCOD/L TSS 167 6.4 mgTSS/L TN 15.8 1.5 mgTN/L TP 3.7 0.2 mgTP/L MLVSS 3,451 mgVSS/L MLSS 4,244 mgTSS/L Mg flow 10 m3

Mg concentration 6,670/8,000 mgMg/L Struvite precipitation 1,430/1,611 mgISS/L Struvite production 346/432 kg/d

**reactor**

**Unit**

/d

/d

**Parameter Influent Effluent Reactor Struvite**

**Amsterdam West WWTP for phosphate recovery.**

**treatment line.**

**Figure 16.16 Modified layout of the pulp mill WWTP with a side‐stream struvite recovery reactor.**

awarding an MSc scholarship to the first author of this work. In addition, the authors would like to acknowledge the financial contribution provided by the Universidad Católica del Uruguay (UCUDAL) to carry out almost all the analytical work involved in this research, and to cover some transportation and accommodation costs.

# **References**

**437**

**Chapter 17**

**van Loosdrecht M.C.M.**

**17.2 Historical overview**

**17.1 Introduction**

modelling.

**The past, present and future of**

**wastewater treatment modeling**

**Lopez‐Vazquez C.M., Hooijmans C.M., Meijer S.C.F., Brdjanovic D. and**

Activated sludge systems have been applied for 100 years. In the course of the years, researchers have developed various models to describe the activated sludge processes. The main aim has been to get a better understanding of the conditions that favour the conversions of carbon, nitrogen and phosphorus present in wastewater, and associated oxygen consumption and sludge production. The current chapter presents reflection on the historical developments, state‐of‐the‐art of activated sludge modelling and future trends supported by a literature inventory and a statistical analysis of publications dealing with the activated sludge models and

Before the 80's, several research groups worked independently from each other on developing models of activated sludge. Each group developed and applied their own approach and notation, first in steady‐state models, and later on, in dynamic models. In the early 80's, Poul Harremoës, President of IAWPRC (The International Association of Water Pollution, Research and Control, later IAWQ, International Association of Water Quality; nowadays, IWA, International Water Association) initiated the idea to combine the most relevant and applied models and to work together at an international level to accelerate development of a common, unified model. As a consequence, in 1982, the "Task group on mathematical modelling for design and operation of biological wastewater treatment" was established with Gerrit Marais (University of Cape Town), Leslie Grady (Clemson University), Willy Gujer (EAWAG), Tomonori Matsuo (Tokyo University) and Mogens Henze (Technical University of Denmark) as chairman. This joint activity resulted in the development of the first dynamic Activated Sludge Model, called in short ASM1 (Henze *et al.,* 1987). The ASM1 model is a structured model based on Monod kinetics that predicts the processes of biological (bacteriological) reactions. The ASM1 models COD and N removal, oxygen consumption and sludge production. Wastewater is characterized in terms of 7 dissolved and 6 particulate components that are used to describe 2 biomass groups, 7 fractions of COD (organic material) and 4 fractions of nitrogen (Henze *et al*., 1987; Gujer and Henze, 1991). Dissolved oxygen concentration and alkalinity are also included as part of the wastewater characteristics. From the 8 processes of the model, 3 are related to the growth of heterotrophic and autotrophic organisms, 2 describe the biomass decay (death‐regeneration theory, Dold *et al.,* 1980), and 3 related to hydrolysis. The model is presented in a matrix format, also known as the Petersen matrix or Gujer matrix (Petersen, 1965; Takács *et al*., 2007). This matrix contains stoichiometric coefficients and a kinetic vector. All state variables involved in a process are displayed in columns, and all processes where a state variable is involved are presented in the rows of the

# **Chapter 17**

# **The past, present and future of wastewater treatment modeling**

**Lopez‐Vazquez C.M., Hooijmans C.M., Meijer S.C.F., Brdjanovic D. and van Loosdrecht M.C.M.**

# **17.1 Introduction**

Activated sludge systems have been applied for 100 years. In the course of the years, researchers have developed various models to describe the activated sludge processes. The main aim has been to get a better understanding of the conditions that favour the conversions of carbon, nitrogen and phosphorus present in wastewater, and associated oxygen consumption and sludge production. The current chapter presents reflection on the historical developments, state‐of‐the‐art of activated sludge modelling and future trends supported by a literature inventory and a statistical analysis of publications dealing with the activated sludge models and modelling.

# **17.2 Historical overview**

Before the 80's, several research groups worked independently from each other on developing models of activated sludge. Each group developed and applied their own approach and notation, first in steady‐state models, and later on, in dynamic models. In the early 80's, Poul Harremoës, President of IAWPRC (The International Association of Water Pollution, Research and Control, later IAWQ, International Association of Water Quality; nowadays, IWA, International Water Association) initiated the idea to combine the most relevant and applied models and to work together at an international level to accelerate development of a common, unified model. As a consequence, in 1982, the "Task group on mathematical modelling for design and operation of biological wastewater treatment" was established with Gerrit Marais (University of Cape Town), Leslie Grady (Clemson University), Willy Gujer (EAWAG), Tomonori Matsuo (Tokyo University) and Mogens Henze (Technical University of Denmark) as chairman. This joint activity resulted in the development of the first dynamic Activated Sludge Model, called in short ASM1 (Henze *et al.,* 1987). The ASM1 model is a structured model based on Monod kinetics that predicts the processes of biological (bacteriological) reactions. The ASM1 models COD and N removal, oxygen consumption and sludge production. Wastewater is characterized in terms of 7 dissolved and 6 particulate components that are used to describe 2 biomass groups, 7 fractions of COD (organic material) and 4 fractions of nitrogen (Henze *et al*., 1987; Gujer and Henze, 1991). Dissolved oxygen concentration and alkalinity are also included as part of the wastewater characteristics. From the 8 processes of the model, 3 are related to the growth of heterotrophic and autotrophic organisms, 2 describe the biomass decay (death‐regeneration theory, Dold *et al.,* 1980), and 3 related to hydrolysis. The model is presented in a matrix format, also known as the Petersen matrix or Gujer matrix (Petersen, 1965; Takács *et al*., 2007). This matrix contains stoichiometric coefficients and a kinetic vector. All state variables involved in a process are displayed in columns, and all processes where a state variable is involved are presented in the rows of the

on 15 December 2021

**436**

awarding an MSc scholarship to the first author of this work. In addition, the authors would like to acknowledge the financial contribution provided by the Universidad Católica del Uruguay (UCUDAL) to carry out almost all the analytical work involved in this research, and to cover some transportation and

Barañao P.A, Hall E.R. (2004) Modelling carbon oxidation in pulp mill activated sludge systems:

Bergmans B. (2011) Struvite recovery from digested sludge at WWTP West. MSc Thesis. Delft University

Ekama G.A., Wentzel M.C. (2008) Organic Material Removal. In: Biological Wastewater Treatment:

Faroppa C., Annala K (2004) UPM Fray Bentos. Informe Ambiental ‐ Resumen. (*In Spanish*). http://www.upm.com/uy/planta/medio‐ambiente/estudios/estudios‐por‐la‐ empresa/Documents/Evaluaci%C3%B3n%20de%20Impacto%20Ambiental%20‐

Management (Watermatex2011). San Sebastian, Spain, June 20‐22th, 2011.

Bastone D.J.. Keller J., Angelidaki I., Kalyuzhyi S.V., Pavlostathis S.G., Rozzi A., Sanders W.T.M., Siegrist H., Vavillin V.A. (2002) The IWA Anaerobic digestion model No. 1 (ADM1). Wat Sci Tech, 45(10):65‐73. BRACELPA (2011) http://www.bracelpa.org.br/eng/estatisticas/pdf/booklet/booklet.pdf (accessed August

Gujer W., Henze M., Mino T. and van Loosdrecht M.C.M. (1999) Activated Sludge Model No. 3. Water Sci.

Henze M., Gujer W., Mino T., van Loosdrecht M.C.M. (2000) Activated sludge models ASM1, ASM2,

Henze M., van Loosdrecht M.C.M., Ekama G.A., Brdjanovic D (2008) Biological Wastewater Treatment: Principles, Modelling and Design. IWA Publishing. London, U.K. ISBN:<PHONE_NUMBER>.

Ikumi D.S., Brouckaert C.J. Ekama G.A. (2011) A 3‐phase anaerobic digestion model. In: Proceedings of the 8th IWA International Symposium on Systems Analysis and Integrated Assessment in Water

Ikumi DS, Harding TH, Vogts M, Lakay MT, Mafungwa H, Brouckaert CJ, Ekama GA (2014) Mass balances modelling over wastewater treatment plants III. Final Report. Water Research Comission. South

Hulsbeek J.J.W., Kruit J., Roeleveld P.J. and van Loosdrecht M.C.M. (2002) A practical protocol for dynamic modelling of activated sludge systems. Water Sci. Tech., 45, 127‐136. Keskitalo J, Leiviskä K (2012) Evaluating the Potential for Process Control in Pulp Mill Wastewater Treatment Plant by Simulation. Mathematical Modelling. Oulu: University of Oulu. Keskitalo J, Leiviskä K (2010) Mechanistic modelling of pulp and paper mill wastewater treatment plants.

41, Oulu: University of Oulu, Control Engineering Laboratory. Lawler D.F., Benjamin M.M. (2006) Particle treatment processes: Common elements. By: Desmond F.

Meijer S.C.F. and Brdjanovic D. (2012) A Practical Guide to Activated Sludge Modelling. UNESCO‐IHE

Paul E., Laval M.L., Sperandio M. (2001) Excess sludge production and costs due to phosphorus removal.

Saarela S., Garcia E., Eluen I., Fernandez V., Kovasin K. (2008) "Celso Foelkel". The first year of operation

foelkel.com.br/artigos/outros/Botnia‐Saarela‐First%20year%20operation.pdf (accessed July 2013). Takács I. (2008) Experiments in Activated Sludge Modelling. PhD Thesis, Ghent University. Belgium. Thompson G., Swain J., Kay M., Forster C.F. (2001) The treatment of pulp and paper mill effluent: a

Portal de noticias TIEMPO. July 9th, 2013. http://www.tiempo.com.uy/forestales/1833‐la‐verdad‐sobre‐

van Nieuwenhuijzen A.F, Havekes M., Reitsma B.A,, de Jong P. (2009) Wastewater treatment plant Amsterdam West: new, large, high‐tech and sustainable. Water Practice Tech, 4(1),

of the BOTNIA Fray Bentos pulp mill in Uruguay. 2008. http://www.celso‐

Principles, Modelling and Design. eds. Henze M., van Loosdrecht M.C.M., Ekama G.A., Brdjanovic D.

Calibration of Activated Sludge Model No 3. Wat Sci Tech, 50(3): 1‐10.

of Technology. Delft, The Netherlands.

%20UPM%20Fray%20Bentos.pdf (accessed July 2013).

ASM2d and ASM3. IWA publishing. London, U.K.

Lawler and Mark M. Benjamin, Chapter 1, pp 1‐82.

Lecture Notes, 280 p. ISBN:<PHONE_NUMBER>.

review." Biores Technol 77(3): 275‐286.

Environ Technol, 22(11): 1363‐1371.

upm (accessed May 1st, 2014).

doi:10.2166/wpt.2009.006.

IWA Publishing. London, U.K.

Technol., 3, 183‐193.

accommodation costs.

**References**

2013).

Africa.

Report A No

matrix. Already in use in chemical modelling (Petersen, 1965), this representation helped to present the model in a condensed form. It facilitated its publication, interpretation and comparison not only between models, but also between processes and compounds. Certain major limitations of ASM1 are, for example, that it only describes heterotrophic and autotrophic reactions under aerobic and anoxic conditions (in which, for instance, ordinary heterotrophs consume carbonaceous substrates and autotrophic nitrifying organisms oxidize ammonia to nitrate), but it does not include enhanced biological phosphorus removal (EBPR) processes (Gujer and Henze, 1991). Despite the fact that to a great extent knowledge of EBPR processes was already available when ASM1 was developed (van Loosdrecht *et al.*, 1997), EBPR was not included in ASM1 since most of the wastewater treatment plants at that time did not incorporate biologically enhanced (or chemical) phosphorus removal (Fenu *et al.,* 2010).

Throughout the years, several research groups started to work on the description of EBPR for its incorporation in the dynamic activated sludge models, mostly based on directly measurable soluble compounds. From the mid 80's to the mid 90's, the EBPR process grew in popularity and the understanding of the underlying bio‐chemical mechanisms increased (Henze, 2000). In the meantime, in 1990, the composition of the Task Group changed, when Leslie Grady left and Takashi Mino (Tokyo University) and Mark Wentzel (University of Cape Town) joined the task group. The knowledge acquired on EBPR led to the publication of the Activated Sludge Model No. 2 (ASM2) (Henze *et al.,* 1995), which included the EBPR processes. In particular, ASM2 includes Phosphate Accumulating Organisms (PAO), growing only under aerobic conditions, with the correspondingly associated anaerobic, anoxic, and aerobic reactions. ASM2 was a compromise between complexity and simplicity, and between different points of view on how the correct model should look like to be used as a conceptual platform for further model development (Henze *et al.,* 2000). In 1996, Mark van Loosdrecht (Delft University of Technology) became member of the Task Group, following the departure of Tomonori Matsuo, Mark Wentzel and Gerrit Marais. Because the occurrence of denitrifying EBPR was well‐established (e.g. Kuba *et al.,* 1997; Murnleitner *et al.*, 1997) the ASM2 model was expanded in 1999 by the inclusion of denitrifying PAO (DPAO). This version of the model was denoted as ASM2d (Henze *et al.,* 1999). Both ASM2 and ASM2d are similar to ASM1 by assuming the cell to be a black box, as opposed to using the metabolic approach to modelling the processes that take place inside the cell. Parallel to these developments, in 1994, an increasing knowledge of the cell‐internal biochemistry of PAO resulted in the development of a metabolic model (TUD bio‐P model; Smolders *et al.,* 1994a, b; Murnleitner *et al.,* 1997) describing the anaerobic and aerobic phases of EBPR based on intracellular storage compounds. This model was later fully integrated with ASM by Meijer (2004).

At the same time as the ASM2d model was presented, the Task Group also developed the ASM3 model to correct some of the shortcomings of ASM1. ASM3 was proposed to become the new standard for ASM‐based modelling. ASM3 replaced the death‐regeneration process for heterotrophic organisms by an endogenous respiration process and also introduced the role of storage of organic substrates (Gujer *et al.,* 1999). In 2000, the Task Group presented the overview of the ASM models 1 to 3 (Henze *et al.,* 2000).

Within the context of model development it is also important to mention the role of hardware. The development of the models and computer capacity (CPU) grew hand to hand (Gujer, 2006). From a technical perspective, it became feasible to work with models that contained a large number of process descriptions and variables. In the 90's, models were increasingly used by researchers, but also mathematical modelling became popular between practitioners. Today, mathematical models are commonly used in the USA, Australia and many countries in Europe (Hauduc *et al.*, 2009). To facilitate its application, software has been developed to assist in

**439**

On the website of the GMP‐TG (https://iwa‐gmp‐tg.irstea.fr/) adjusted Gujer matrices for 7 published models can be downloaded as an MS Excel spreadsheet: (1) ASM1 (Henze *et al*., 1987, 2000a); (2) ASM2d (Henze *et al.,* 1999); (3) ASM3 (Gujer *et al.,* 1999); (4) ASM3+BioP (Rieger *et*

Besides sending out the questionnaire several workshops, meetings, and courses on activated sludge modelling were organised, such as the wastewater treatment modelling seminars. The GMP‐TG was involved in the development of a new IWA Model Notation System (Corominas *et al*., 2010), and interviewed several distinguished modellers (Peter Dold, George Ekama, Willi Gujer, Mogens Henze, Mark van Loosdrecht, among others). One of the advices was to suggest typical values for regularly used ratios, variables, and parameters in wastewater modelling. The feedback was compiled in the IWA book "Guidelines for Using Activated Sludge Models",

Scientific and Technical Report No. 22 (Rieger *et al.*, 2012).

design, optimisation, operation and training. Modelling simulators provide a better understanding of wastewater treatment plants since they allow users to view the response of the treatment systems to changes in a number of different variables, and are also used to optimize wastewater treatment plants and to train plant operators. Examples of commercial packages are GPS‐X, SIMBA, STOAT, WEST, BioWin etc. For research and training SSSP, ASIM, AQUASIM and even Microsoft Excel are regularly used. Significant benefits are associated with the use of simulators in the analysis, design, and operation of wastewater treatment systems (Meijer and Brdjanovic, 2012). More information on development of activated sludge modelling

Several guidelines on how to model a wastewater treatment plant, and protocols on how to characterize the wastewater/sewage and sludge, have been developed during the last years around the world. In 2004, at the 4th IWA World Water Congress in Marrakech, groups that developed various protocols (Hochschulgruppe, STOWA, BIOMATH and WERF) came together to develop plans to synthesize the best modeling practices available. A new IWA Good Modelling Practice Task Group (GMP‐TG) was formed on the use of Activated Sludge Models, parallel to and with the full support from the Task group on mathematical modelling for design and operation of biological wastewater treatment. The GMP‐TG consists of an international team of modellers collecting experience and knowledge on activated sludge modelling to provide guidance to practitioners (Rieger *et al.,* 2012). One of the aims of GMP‐TG was to prepare a scientific and technical report to propose simple and effective procedures for the use of ASM‐ type models (Rieger *et al.,* 2012). In preparation of this report, the GMP‐TG developed and sent out a questionnaire in 2007 to benchmark and collect relevant information on the practical use of modelling. The objectives were to better define the profile of ASM users, to identify the tools/procedures that are used (models, guidelines, protocols) and to highlight the main limitations encountered while building and using ASM‐type models (Hauduc *et al.,* 2009). The outcome of the questionnaire, filled in by 96 respondents, showed that models are used by researchers for optimisation purposes, as well as by modellers employed by private companies to carry out design studies. Modelling is seen as an engineering tool, needing relevant training that is often lacking. The most used biokinetic models were ASM1 (57%) and ASM2d (32%), followed by ASM3, other (non‐specified), ASDM (BioWin), Mantis (GPSX) and TUD model. The study also revealed that models are sometimes not properly applied, which might be due to a lack of knowledge and standardised procedures. The development of standardised modelling procedures and better knowledge transfer by making available some practical case studies was mentioned as a key instrument to address certain obstacles like the complexity of the model theories and procedures, the time consuming steps and finally the reliability of the models.

is provided in Chapter 1 of this book and by Ekama and Takács (2014).

**17.2 Recent developments**

design, optimisation, operation and training. Modelling simulators provide a better understanding of wastewater treatment plants since they allow users to view the response of the treatment systems to changes in a number of different variables, and are also used to optimize wastewater treatment plants and to train plant operators. Examples of commercial packages are GPS‐X, SIMBA, STOAT, WEST, BioWin etc. For research and training SSSP, ASIM, AQUASIM and even Microsoft Excel are regularly used. Significant benefits are associated with the use of simulators in the analysis, design, and operation of wastewater treatment systems (Meijer and Brdjanovic, 2012). More information on development of activated sludge modelling is provided in Chapter 1 of this book and by Ekama and Takács (2014).

# **17.2 Recent developments**

Several guidelines on how to model a wastewater treatment plant, and protocols on how to characterize the wastewater/sewage and sludge, have been developed during the last years around the world. In 2004, at the 4th IWA World Water Congress in Marrakech, groups that developed various protocols (Hochschulgruppe, STOWA, BIOMATH and WERF) came together to develop plans to synthesize the best modeling practices available. A new IWA Good Modelling Practice Task Group (GMP‐TG) was formed on the use of Activated Sludge Models, parallel to and with the full support from the Task group on mathematical modelling for design and operation of biological wastewater treatment. The GMP‐TG consists of an international team of modellers collecting experience and knowledge on activated sludge modelling to provide guidance to practitioners (Rieger *et al.,* 2012). One of the aims of GMP‐TG was to prepare a scientific and technical report to propose simple and effective procedures for the use of ASM‐ type models (Rieger *et al.,* 2012). In preparation of this report, the GMP‐TG developed and sent out a questionnaire in 2007 to benchmark and collect relevant information on the practical use of modelling. The objectives were to better define the profile of ASM users, to identify the tools/procedures that are used (models, guidelines, protocols) and to highlight the main limitations encountered while building and using ASM‐type models (Hauduc *et al.,* 2009). The outcome of the questionnaire, filled in by 96 respondents, showed that models are used by researchers for optimisation purposes, as well as by modellers employed by private companies to carry out design studies. Modelling is seen as an engineering tool, needing relevant training that is often lacking. The most used biokinetic models were ASM1 (57%) and ASM2d (32%), followed by ASM3, other (non‐specified), ASDM (BioWin), Mantis (GPSX) and TUD model. The study also revealed that models are sometimes not properly applied, which might be due to a lack of knowledge and standardised procedures. The development of standardised modelling procedures and better knowledge transfer by making available some practical case studies was mentioned as a key instrument to address certain obstacles like the complexity of the model theories and procedures, the time consuming steps and finally the reliability of the models.

Besides sending out the questionnaire several workshops, meetings, and courses on activated sludge modelling were organised, such as the wastewater treatment modelling seminars. The GMP‐TG was involved in the development of a new IWA Model Notation System (Corominas *et al*., 2010), and interviewed several distinguished modellers (Peter Dold, George Ekama, Willi Gujer, Mogens Henze, Mark van Loosdrecht, among others). One of the advices was to suggest typical values for regularly used ratios, variables, and parameters in wastewater modelling. The feedback was compiled in the IWA book "Guidelines for Using Activated Sludge Models", Scientific and Technical Report No. 22 (Rieger *et al.*, 2012).

On the website of the GMP‐TG (https://iwa‐gmp‐tg.irstea.fr/) adjusted Gujer matrices for 7 published models can be downloaded as an MS Excel spreadsheet: (1) ASM1 (Henze *et al*., 1987, 2000a); (2) ASM2d (Henze *et al.,* 1999); (3) ASM3 (Gujer *et al.,* 1999); (4) ASM3+BioP (Rieger *et*

on 15 December 2021

**438**

At the same time as the ASM2d model was presented, the Task Group also developed the ASM3 model to correct some of the shortcomings of ASM1. ASM3 was proposed to become the new standard for ASM‐based modelling. ASM3 replaced the death‐regeneration process for heterotrophic organisms by an endogenous respiration process and also introduced the role of storage of organic substrates (Gujer *et al.,* 1999). In 2000, the Task Group presented the

Within the context of model development it is also important to mention the role of hardware. The development of the models and computer capacity (CPU) grew hand to hand (Gujer, 2006). From a technical perspective, it became feasible to work with models that contained a large number of process descriptions and variables. In the 90's, models were increasingly used by researchers, but also mathematical modelling became popular between practitioners. Today, mathematical models are commonly used in the USA, Australia and many countries in Europe (Hauduc *et al.*, 2009). To facilitate its application, software has been developed to assist in

overview of the ASM models 1 to 3 (Henze *et al.,* 2000).

(2004).

matrix. Already in use in chemical modelling (Petersen, 1965), this representation helped to present the model in a condensed form. It facilitated its publication, interpretation and comparison not only between models, but also between processes and compounds. Certain major limitations of ASM1 are, for example, that it only describes heterotrophic and autotrophic reactions under aerobic and anoxic conditions (in which, for instance, ordinary heterotrophs consume carbonaceous substrates and autotrophic nitrifying organisms oxidize ammonia to nitrate), but it does not include enhanced biological phosphorus removal (EBPR) processes (Gujer and Henze, 1991). Despite the fact that to a great extent knowledge of EBPR processes was already available when ASM1 was developed (van Loosdrecht *et al.*, 1997), EBPR was not included in ASM1 since most of the wastewater treatment plants at that time did not incorporate biologically enhanced (or chemical) phosphorus removal (Fenu *et al.,* 2010).

Throughout the years, several research groups started to work on the description of EBPR for its incorporation in the dynamic activated sludge models, mostly based on directly measurable soluble compounds. From the mid 80's to the mid 90's, the EBPR process grew in popularity and the understanding of the underlying bio‐chemical mechanisms increased (Henze, 2000). In the meantime, in 1990, the composition of the Task Group changed, when Leslie Grady left and Takashi Mino (Tokyo University) and Mark Wentzel (University of Cape Town) joined the task group. The knowledge acquired on EBPR led to the publication of the Activated Sludge Model No. 2 (ASM2) (Henze *et al.,* 1995), which included the EBPR processes. In particular, ASM2 includes Phosphate Accumulating Organisms (PAO), growing only under aerobic conditions, with the correspondingly associated anaerobic, anoxic, and aerobic reactions. ASM2 was a compromise between complexity and simplicity, and between different points of view on how the correct model should look like to be used as a conceptual platform for further model development (Henze *et al.,* 2000). In 1996, Mark van Loosdrecht (Delft University of Technology) became member of the Task Group, following the departure of Tomonori Matsuo, Mark Wentzel and Gerrit Marais. Because the occurrence of denitrifying EBPR was well‐established (e.g. Kuba *et al.,* 1997; Murnleitner *et al.*, 1997) the ASM2 model was expanded in 1999 by the inclusion of denitrifying PAO (DPAO). This version of the model was denoted as ASM2d (Henze *et al.,* 1999). Both ASM2 and ASM2d are similar to ASM1 by assuming the cell to be a black box, as opposed to using the metabolic approach to modelling the processes that take place inside the cell. Parallel to these developments, in 1994, an increasing knowledge of the cell‐internal biochemistry of PAO resulted in the development of a metabolic model (TUD bio‐P model; Smolders *et al.,* 1994a, b; Murnleitner *et al.,* 1997) describing the anaerobic and aerobic phases of EBPR based on intracellular storage compounds. This model was later fully integrated with ASM by Meijer *al.,* 2001); (5) ASM2d+TUD (Meijer, 2004); (6) Barker & Dold model (Barker and Dold, 1997); and (7) UCTPHO+ (Hu *et al.,* 2007). In the same website, a comparison of different parameter‐ naming rules including the new IWA Model Notation System (Corominas *et al.,* 2010) can also be found.

Recently, among other initiatives to improve the knowledge transfer by facilitating and making available some practical case‐studies (Hauduc *et al.,* 2009), UNESCO‐IHE published the book "A Practical Guide to Activated Sludge Modelling" (Meijer and Brdjanovic, 2012) which is used in the modelling course delivered every year at UNESCO‐IHE in cooperation with Delft University of Technology (http://www.unesco‐ihe.org/modelling‐wastewater‐treatment‐processes‐and‐ plants). With a very practical focus, it presents all the steps performed as a part of a modelling project in Croatia where five WWTPs were subject to upgrade to EU effluent discharge standards. Besides the general modelling protocols and guidelines for wastewater characterization and fractionation, methods for quantitative influent assessment, addressing different components of the urban wastewater chain and introducing a methodology for quantification of sewage components are presented. Guidelines for plant flows and measurement points necessary for the preparation of an additional sampling program are also given, as well as an inventory of all the regular day‐to‐day sampling to be used in a modelling project, a methodology for activated sludge plant assessment and methods to evaluate raw plant data and to filter out possible errors which affect the model reliability and results. Practical methods for wastewater data evaluation were developed by Meijer *et al.*, 2002. New developments on data evaluation are presented in chapter 15 of this book in relation to a well‐ documented case study performed on WWTP Houtrust in the Netherlands in 2010. A methodology for secondary settlers' design and assessment is described, including the five most commonly used settler design procedures. The last part of the book elaborates on the methodology applied in model calibration and the main steps thereof.

# **17.3 Statistics on ASM literature**

As a mean to assess the development and historical impact of mathematical modelling on the wastewater treatment field, a statistical analysis of the scientific literature was carried out, excluding the technical reports. Google Scholar was used as web search engine. The Google Scholar index includes most peer‐reviewed online journals, plus scholarly books and other non‐ peer reviewed journals. In addition, such a search engine was selected since, contrary to other databases and scientific search engines, it could provide an estimation of the citations and references of and from documents dated back to the 1980's and even older. Thus, in March 2014, the top 50 most cited papers were identified. It is interesting to notice that an abbreviated report of ASM1 (Henze *et al.,* 1987), ASM2 (Gujer *et al.,* 1995), ASM2d (Henze *et al.,* 1999) and ASM3 (Gujer *et al.,* 1999) were published as papers in addition to their availability as technical reports. The results of the search are presented in Figure 17.1. The papers were classified in five groups concerning their focus and contents (as indicated by the colour of bars): (i) model development and model presentation; (ii) characterization and modelling protocols; (iii) metabolic modelling; (iii) applied modelling; and, (iv) others. As seen in Figure 17.1, the top‐50 list is dominated by papers on model development and metabolic modelling, followed by those on protocols for characterization and modelling and thereafter by the applied modelling papers. Interestingly, in the top 5, four papers on model development and presentation and one on metabolic modelling can be found. The two by far most cited papers are the ASM3 paper from Gujer *et al.* (1999) and a paper by Takács *et al.* (1991) on the clarification‐thickening process. To find the paper on settling as the second most cited paper is remarkable, as it deals with modelling of the activated sludge system, but unlike the ASM models, not with the biokinetics. The third most cited is the ASM2d paper by Henze *et al.* (1999).

**441**

0

**Figure 17.1 The top 50 most cited papers on mathematical modelling (excluding technical reports).**

300

**No.**

200

100

**Source: Google Scholar (March 2014).**

700

**To 50 Most Cited**

Characterization

Metabolic

Applied

Others

modelling

modelling

 and

Model

development and

**Papers**

modelling protocols

presentation

758 724

800

500

600

393 393

400

**Citations**

342

294

83

**Figure 17.1 The top 50 most cited papers on mathematical modelling (excluding technical reports). Source: Google Scholar (March 2014).**

**440**

*al.,* 2001); (5) ASM2d+TUD (Meijer, 2004); (6) Barker & Dold model (Barker and Dold, 1997); and (7) UCTPHO+ (Hu *et al.,* 2007). In the same website, a comparison of different parameter‐ naming rules including the new IWA Model Notation System (Corominas *et al.,* 2010) can also be

Recently, among other initiatives to improve the knowledge transfer by facilitating and making available some practical case‐studies (Hauduc *et al.,* 2009), UNESCO‐IHE published the book "A Practical Guide to Activated Sludge Modelling" (Meijer and Brdjanovic, 2012) which is used in the modelling course delivered every year at UNESCO‐IHE in cooperation with Delft University of Technology (http://www.unesco‐ihe.org/modelling‐wastewater‐treatment‐processes‐and‐ plants). With a very practical focus, it presents all the steps performed as a part of a modelling project in Croatia where five WWTPs were subject to upgrade to EU effluent discharge standards. Besides the general modelling protocols and guidelines for wastewater characterization and fractionation, methods for quantitative influent assessment, addressing different components of the urban wastewater chain and introducing a methodology for quantification of sewage components are presented. Guidelines for plant flows and measurement points necessary for the preparation of an additional sampling program are also given, as well as an inventory of all the regular day‐to‐day sampling to be used in a modelling project, a methodology for activated sludge plant assessment and methods to evaluate raw plant data and to filter out possible errors which affect the model reliability and results. Practical methods for wastewater data evaluation were developed by Meijer *et al.*, 2002. New developments on data evaluation are presented in chapter 15 of this book in relation to a well‐ documented case study performed on WWTP Houtrust in the Netherlands in 2010. A methodology for secondary settlers' design and assessment is described, including the five most commonly used settler design procedures. The last part of the book elaborates on the

As a mean to assess the development and historical impact of mathematical modelling on the wastewater treatment field, a statistical analysis of the scientific literature was carried out, excluding the technical reports. Google Scholar was used as web search engine. The Google Scholar index includes most peer‐reviewed online journals, plus scholarly books and other non‐ peer reviewed journals. In addition, such a search engine was selected since, contrary to other databases and scientific search engines, it could provide an estimation of the citations and references of and from documents dated back to the 1980's and even older. Thus, in March 2014, the top 50 most cited papers were identified. It is interesting to notice that an abbreviated report of ASM1 (Henze *et al.,* 1987), ASM2 (Gujer *et al.,* 1995), ASM2d (Henze *et al.,* 1999) and ASM3 (Gujer *et al.,* 1999) were published as papers in addition to their availability as technical reports. The results of the search are presented in Figure 17.1. The papers were classified in five groups concerning their focus and contents (as indicated by the colour of bars): (i) model development and model presentation; (ii) characterization and modelling protocols; (iii) metabolic modelling; (iii) applied modelling; and, (iv) others. As seen in Figure 17.1, the top‐50 list is dominated by papers on model development and metabolic modelling, followed by those on protocols for characterization and modelling and thereafter by the applied modelling papers. Interestingly, in the top 5, four papers on model development and presentation and one on metabolic modelling can be found. The two by far most cited papers are the ASM3 paper from Gujer *et al.* (1999) and a paper by Takács *et al.* (1991) on the clarification‐thickening process. To find the paper on settling as the second most cited paper is remarkable, as it deals with modelling of the activated sludge system, but unlike the ASM models, not with the biokinetics.

methodology applied in model calibration and the main steps thereof.

The third most cited is the ASM2d paper by Henze *et al.* (1999).

**17.3 Statistics on ASM literature**

found.

The results of the literature search are also presented in the form of a pie chart, displaying as percentage the number of papers that belong to the top 50 most cited papers when grouped per focus and contents (Figure 17.2a). As observed, most of the top 50 most cited papers deal with protocols for characterization and modelling closely followed by papers on modelling development and metabolic modelling, but by far by the applied modelling papers. Nevertheless, when looking at the cumulative number of citations of the top 50 most cited papers (Figure 17.2b) the papers on model development and presentation have the highest number of citations. Such a distribution can suggest that there has been an increasing need to use well‐established protocols to understand and implement the mathematical models but, logically, they cannot outnumber the basic modelling papers since they are indeed the subject of study and application. Remarkably, the papers on metabolic modelling rank third close to the first two groups. This is a clear indication that metabolic modelling has been widely accepted particularly for its contribution to modelling the EBPR process (e.g. Comeau *et al.,* 1986; Smolders *et al.,* 1994). It should be underlined that applied modelling papers received the lowest number of citations. This can be a reflection of the findings of the GMP‐TG regarding the need to make available more practical case‐studies (Hauduc *et al.,* 2009). In practice, possibly the modelling studies are so case‐specific that they offer a limited interest to a broad scientific audience, which limits the potential number of citations that an applied modelling paper could get. But also, it cannot be discarded that the present degree of maturity and reliability of the models reduces the chances to find a novelty in the field. Thereby in any case, the historical links between academia and industry have been a key catalyser and should continue to work hand‐to‐ hand for the future development and establishment of mathematical modelling in the wastewater treatment field.

In parallel, a search was done on the citations of the five publications on the different ASM models. This included the technical reports on ASM1, ASM2, ASM1‐ASM3, and papers on ASM2d and ASM3 (which are only published in papers and not as a technical report). As seen in Figures 17.3a and 17.3b, between 1998 and 2000, more than 10 years after the publication of ASM1, there was a boost in the number of citations to the ASM models. Simulation tools were already available from the early 90's (like GPS‐X and SIMBA), indicating that the software capabilities were not a factor that delayed their implementation (and possibly the hardware was not either). Interestingly, the ASM started to have more citations after the publications of the most cited papers on characterization and modelling protocols (like the papers of Kappeler and Gujer, 1992;

**443**

**Figure 17.3 ASM reports/papers: number of yearly citations (upper figure), and cumulative number of**

ASM2d Henze *et al.* (1999) 1,500

In an effort to get an estimation of the individual potential application of the ASM models, an additional search was carried out, looking at the yearly number of publications that refer to each model (using as exact phrase in Google Scholar anywhere in the article or document: "Activated sludge model No. ASM X"). Compared to the previous search, the difference is that the focus is

**yearly citations (lower figure). Source: Google Scholar (March 2014).**

1986 1985 1990 1995 2000 2005 2010 2014 Year

1980 1985 1990 1995 2000 2005 2010 2015 Year

80

120

140

100

Number of citations per year

160

180

200

40

20

0

1,000 500

0

2,000 2,500 3,000

Number of citations per year

60

Mamais *et al.,* 1993; Spanjers and Vanrolleghem, 1999; Vanrolleghem *et al.,* 1995; Vanrolleghem *et al.,* 1999; and Roeleveld and van Loosdrecht, 2002) (Figure 17.1). Possibly, the lack of understanding and confidence to apply the ASM models delayed their implementation, which was overcome by the availability of reliable characterization and modelling protocols. Furthermore, the publication of the ASM1‐ASM3 technical report (Henze *et al.,* 2000), which consolidated in a single publication the contents and features of all ASM models, could be another important driver that helped to promote the ASM models, increasing the number of citations after 2000. Due to the popularity of the ASM1‐ASM3 technical report (Figure 17.3a) practically all the latest publications refer to it. This makes difficult to clearly distinguish which model was used for a particular study, possibly overestimating the popularity or application of some ASM models and underestimating the others. Importantly, the yearly number of citations to ASM reports is stabilizing or even decreasing (Figure 17.3). Apparently, the number of citations per year has passed its exponential growth and started to level off since 2009.

> ASM1 Henze *et al.* (1987) ASM2 Henze *et al.* (1995)

ASM3 Gujer *et al.* (1999) ASM1-ASM3 Henze *et al.* (2000)

ASM2d Henze *et al.* (1999)

ASM1-ASM3 Henze *et al.* (2000) ASM3 Gujer *et al.* (1999)

ASM2 Henze *et al.* (1995) ASM1 Henze *et al.* (1987)

Mamais *et al.,* 1993; Spanjers and Vanrolleghem, 1999; Vanrolleghem *et al.,* 1995; Vanrolleghem *et al.,* 1999; and Roeleveld and van Loosdrecht, 2002) (Figure 17.1). Possibly, the lack of understanding and confidence to apply the ASM models delayed their implementation, which was overcome by the availability of reliable characterization and modelling protocols. Furthermore, the publication of the ASM1‐ASM3 technical report (Henze *et al.,* 2000), which consolidated in a single publication the contents and features of all ASM models, could be another important driver that helped to promote the ASM models, increasing the number of citations after 2000. Due to the popularity of the ASM1‐ASM3 technical report (Figure 17.3a) practically all the latest publications refer to it. This makes difficult to clearly distinguish which model was used for a particular study, possibly overestimating the popularity or application of some ASM models and underestimating the others. Importantly, the yearly number of citations to ASM reports is stabilizing or even decreasing (Figure 17.3). Apparently, the number of citations per year has passed its exponential growth and started to level off since 2009.

**Figure 17.3 ASM reports/papers: number of yearly citations (upper figure), and cumulative number of yearly citations (lower figure). Source: Google Scholar (March 2014).**

In an effort to get an estimation of the individual potential application of the ASM models, an additional search was carried out, looking at the yearly number of publications that refer to each model (using as exact phrase in Google Scholar anywhere in the article or document: "Activated sludge model No. ASM X"). Compared to the previous search, the difference is that the focus is

on 15 December 2021

**442**

**Figure 17.2 The top 50 most cited papers on mathematical modelling (excluding technical reports): percentage per group within the top 50 papers (right) and per cumulative number of citations (left).**

**24%**

**12%**

**8%**

In parallel, a search was done on the citations of the five publications on the different ASM models. This included the technical reports on ASM1, ASM2, ASM1‐ASM3, and papers on ASM2d and ASM3 (which are only published in papers and not as a technical report). As seen in Figures 17.3a and 17.3b, between 1998 and 2000, more than 10 years after the publication of ASM1, there was a boost in the number of citations to the ASM models. Simulation tools were already available from the early 90's (like GPS‐X and SIMBA), indicating that the software capabilities were not a factor that delayed their implementation (and possibly the hardware was not either). Interestingly, the ASM started to have more citations after the publications of the most cited papers on characterization and modelling protocols (like the papers of Kappeler and Gujer, 1992;

Model development and presentation

Characterisation Applied modelling Others

**27%**

**29%**

Metabolic modelling

The results of the literature search are also presented in the form of a pie chart, displaying as percentage the number of papers that belong to the top 50 most cited papers when grouped per focus and contents (Figure 17.2a). As observed, most of the top 50 most cited papers deal with protocols for characterization and modelling closely followed by papers on modelling development and metabolic modelling, but by far by the applied modelling papers. Nevertheless, when looking at the cumulative number of citations of the top 50 most cited papers (Figure 17.2b) the papers on model development and presentation have the highest number of citations. Such a distribution can suggest that there has been an increasing need to use well‐established protocols to understand and implement the mathematical models but, logically, they cannot outnumber the basic modelling papers since they are indeed the subject of study and application. Remarkably, the papers on metabolic modelling rank third close to the first two groups. This is a clear indication that metabolic modelling has been widely accepted particularly for its contribution to modelling the EBPR process (e.g. Comeau *et al.,* 1986; Smolders *et al.,* 1994). It should be underlined that applied modelling papers received the lowest number of citations. This can be a reflection of the findings of the GMP‐TG regarding the need to make available more practical case‐studies (Hauduc *et al.,* 2009). In practice, possibly the modelling studies are so case‐specific that they offer a limited interest to a broad scientific audience, which limits the potential number of citations that an applied modelling paper could get. But also, it cannot be discarded that the present degree of maturity and reliability of the models reduces the chances to find a novelty in the field. Thereby in any case, the historical links between academia and industry have been a key catalyser and should continue to work hand‐to‐ hand for the future development and establishment of mathematical modelling in the

wastewater treatment field.

**8%**

**Source: Google Scholar (March 2014).**

**23%**

**23%**

**7%**

**39%**

not on the technical reports, but on the number of yearly citations to the models themselves. The results are depicted in Figures 17.4a and 17.4b. From Figure 17.4a, it can be seen that ASM1 is still the model of reference, followed by ASM3, ASM2 and ASM2d. Its simplicity and flexibility to apply it, not only to municipal activated sludge systems, but also to industrial conditions (Petersen *et al.,* 2002) and even to other wastewater treatment technologies (e.g. biofilm systems and wetlands) (Wanner *et al.,* 2006; Langergraber *et al.,* 2009) may have contributed to its popularity.

**Figure 17.4 Publications referring to ASM models: number of yearly publications (upper figure); and, (b) cumulative number of publications (lower figure). Source: Google Scholar (March 2014).**

Similar to Figures 17.3a and 17.3b, the maximum number of citations was reached around the years 2009‐2010. Thereafter, a decrease in the number of citations is observed. A similar trend observed by Gujer (2006) when searching the Web of Science. The fact that the number of citations to the ASM models decreases does not automatically mean that the number of studies performed has progressively reduced. As previously discussed (Gujer, 2006), it could likely be a consequence of the fact that mathematical modelling is mature, standardized and well‐ established and that publications on modelling are not easy to publish in peer‐reviewed journals due to a lack or insufficiency of scientific novelty.

**445**

It is too soon to assess whether the efforts of the IWA GMP‐TG, in the form of the standardized procedures, available Gujer matrices for all models, and the publication of the Guidelines on Good Modelling Practice in 2012 will lead to an increased work on modelling and increased number of publications. As previously discussed, the number of citations to the ASM models has decreased likely because mathematical modelling is becoming a standardized and mature practice in developed countries. On the opposite, case‐studies and publications from developing countries are limited, which might be partly caused by the fact that activated sludge treatment systems are mainly found in developed countries. Likewise, the questionnaire prepared by the GMP‐TG (Hauduc *et al.,* 2009) also showed that among the 96 responses, 65% came from European countries and 20% from North America. Other continents (South America, Africa, Asia and Australia) were under‐represented. Nevertheless, this book includes some pioneering and (very) recent studies on activated sludge modelling in India (Brdjanovic *et al.*, 2007; Lopez‐ Vazquez *et al.*, 2013), Bosnia and Herzegovina (Hodzic *et al.*, 2011, Price and Vojinovic, 2010),

**17.4 Calibration and model accuracy related to ASM development** A recurring topic in ASM publications is the issue of model calibration and related model accuracy. Early in the ASM development, the need was recognized for the development of better model calibration procedures. This led to the development of practical modelling protocols (Hulsbeek *et al.*, 2002, Rieger *et al.*, 2012). In these protocols, methods are proposed to structure the modelling procedure by addressing specific parameters for calibrating the different biological processes with the goal to obtain more reliable and reproducible model results. Related to model calibration, methods were developed to directly measure kinetic rates of the different bioconversions during full scale practice. They were adopted from lab‐scale ASM research and applied in full‐scale operation such as the use of activated sludge batch‐tests and respirometry (Spanjers *et al.* (1996 and 1999), Vanrolleghem *et al.* (1999)). These tests result in

kinetic data which can be directly be applied in the model calibration process.

in chapter 15 of this book by Meijer *et al.*.

is only possible when the plant data are accurate.

**17.5 The future**

In 1995, Barker and Dold related model inaccuracy to the general lack of reliable wastewater treatment data. They recognized that in full‐scale practice the poor quality of wastewater treatment data affects the model calibration results and reduces the overall accuracy of the model. This important conclusion has led to a new line of research on data accuracy in wastewater treatment, which is developing parallel to the research on ASM models and their practical application. Several publications are available on this topic; Meijer *et al.* (2002), Puig *et al.* (2008), Rieger *et al.* (2011), Spindler *et al.* (2012 and 2014) and more recent work published

Techniques for data evaluation and quality improvement (error detection and reconciliation) are available from process industry, however, not yet widely applied in wastewater treatment. The proposed methods that can be used to improve wastewater treatment data rely on a combination of statistical and mass‐balancing techniques. It is possible to detect gross errors in wastewater treatment data and also to correct and improve on the data before using them in a modeling study. Several successful examples are available demonstrating the effectiveness of data evaluation in wastewater treatment and also the usefulness for reliable modeling. Important conclusions of the parallel research on data accuracy are (i) most full‐scale wastewater treatment data contain (large) measurement errors and misinterpretations, (ii) when reliable model results are required, also some kind of data evaluation needs to be performed and (iii), the inaccuracy of the measured treatment data is often larger than the inaccuracy of the model and calibrated parameters. This means that effective model calibration

# **17.4 Calibration and model accuracy related to ASM development**

A recurring topic in ASM publications is the issue of model calibration and related model accuracy. Early in the ASM development, the need was recognized for the development of better model calibration procedures. This led to the development of practical modelling protocols (Hulsbeek *et al.*, 2002, Rieger *et al.*, 2012). In these protocols, methods are proposed to structure the modelling procedure by addressing specific parameters for calibrating the different biological processes with the goal to obtain more reliable and reproducible model results. Related to model calibration, methods were developed to directly measure kinetic rates of the different bioconversions during full scale practice. They were adopted from lab‐scale ASM research and applied in full‐scale operation such as the use of activated sludge batch‐tests and respirometry (Spanjers *et al.* (1996 and 1999), Vanrolleghem *et al.* (1999)). These tests result in kinetic data which can be directly be applied in the model calibration process.

In 1995, Barker and Dold related model inaccuracy to the general lack of reliable wastewater treatment data. They recognized that in full‐scale practice the poor quality of wastewater treatment data affects the model calibration results and reduces the overall accuracy of the model. This important conclusion has led to a new line of research on data accuracy in wastewater treatment, which is developing parallel to the research on ASM models and their practical application. Several publications are available on this topic; Meijer *et al.* (2002), Puig *et al.* (2008), Rieger *et al.* (2011), Spindler *et al.* (2012 and 2014) and more recent work published in chapter 15 of this book by Meijer *et al.*.

Techniques for data evaluation and quality improvement (error detection and reconciliation) are available from process industry, however, not yet widely applied in wastewater treatment. The proposed methods that can be used to improve wastewater treatment data rely on a combination of statistical and mass‐balancing techniques. It is possible to detect gross errors in wastewater treatment data and also to correct and improve on the data before using them in a modeling study. Several successful examples are available demonstrating the effectiveness of data evaluation in wastewater treatment and also the usefulness for reliable modeling. Important conclusions of the parallel research on data accuracy are (i) most full‐scale wastewater treatment data contain (large) measurement errors and misinterpretations, (ii) when reliable model results are required, also some kind of data evaluation needs to be performed and (iii), the inaccuracy of the measured treatment data is often larger than the inaccuracy of the model and calibrated parameters. This means that effective model calibration is only possible when the plant data are accurate.

# **17.5 The future**

It is too soon to assess whether the efforts of the IWA GMP‐TG, in the form of the standardized procedures, available Gujer matrices for all models, and the publication of the Guidelines on Good Modelling Practice in 2012 will lead to an increased work on modelling and increased number of publications. As previously discussed, the number of citations to the ASM models has decreased likely because mathematical modelling is becoming a standardized and mature practice in developed countries. On the opposite, case‐studies and publications from developing countries are limited, which might be partly caused by the fact that activated sludge treatment systems are mainly found in developed countries. Likewise, the questionnaire prepared by the GMP‐TG (Hauduc *et al.,* 2009) also showed that among the 96 responses, 65% came from European countries and 20% from North America. Other continents (South America, Africa, Asia and Australia) were under‐represented. Nevertheless, this book includes some pioneering and (very) recent studies on activated sludge modelling in India (Brdjanovic *et al.*, 2007; Lopez‐ Vazquez *et al.*, 2013), Bosnia and Herzegovina (Hodzic *et al.*, 2011, Price and Vojinovic, 2010),

on 15 December 2021

**444**

**Figure 17.4 Publications referring to ASM models: number of yearly publications (upper figure); and, (b)**

Similar to Figures 17.3a and 17.3b, the maximum number of citations was reached around the years 2009‐2010. Thereafter, a decrease in the number of citations is observed. A similar trend observed by Gujer (2006) when searching the Web of Science. The fact that the number of citations to the ASM models decreases does not automatically mean that the number of studies performed has progressively reduced. As previously discussed (Gujer, 2006), it could likely be a consequence of the fact that mathematical modelling is mature, standardized and well‐ established and that publications on modelling are not easy to publish in peer‐reviewed journals

**cumulative number of publications (lower figure). Source: Google Scholar (March 2014).**

1986 1985 1990 1995 2000 2005 2010 2014 Year

1980 1985 1990 1995 2000 2005 2010 2015 Year

due to a lack or insufficiency of scientific novelty.

not on the technical reports, but on the number of yearly citations to the models themselves. The results are depicted in Figures 17.4a and 17.4b. From Figure 17.4a, it can be seen that ASM1 is still the model of reference, followed by ASM3, ASM2 and ASM2d. Its simplicity and flexibility to apply it, not only to municipal activated sludge systems, but also to industrial conditions (Petersen *et al.,* 2002) and even to other wastewater treatment technologies (e.g. biofilm systems and wetlands) (Wanner *et al.,* 2006; Langergraber *et al.,* 2009) may have contributed to

> ASM1 Henze *et al.* (1987) ASM2 Henze *et al.* (1995)

ASM3 Gujer *et al.* (1999) ASM2d Henze *et al.* (1999)

ASM3 Gujer *et al.* (1999)

ASM2 Henze *et al.* (1995) ASM1 Henze *et al.* (1987)

ASM2d Henze *et al.* (1999)

its popularity.

80

120

140

100

Number of publications per year

160

180

200

40

20

0

1,500 1,000 500

0

2,000 2,500 3,000 3,500 4,000

Number of publications per year

60

Mexico (Fall *et al.,* 2012), Croatia (Meijer and Brdjanovic, 2012) and Uruguay (Betancur, 2014) (Chapters 8, 10, 12, 13, and 15). This demonstrates that efforts are undertaken also in developing countries to apply the models to existing activated sludge systems (mostly for optimization or upgrade).

Concerning the future development of activated sludge modelling, it is important to underline and take into consideration the current and future needs and developments. In this regard, the trends regarding existing wastewater process technologies will likely need to further focus on providing a better description of the nutrient removal processes, not only for the sake of the nutrients removal itself, but also to reduce the associated energy costs and environmental impact. Modelling the description of nitrous oxides emissions in nutrient removal plants (Ni *et al.,* 2013) as well as achieving a satisfactory description of the actual (anaerobic, aerobic and anoxic) metabolic activities of the relevant EBPR populations (e.g. *Accumulibacter* type I and type II) and their interactions with "new" bacterial populations (such as *GAO*) (Lopez‐Vazquez *et al.,* 2009; Oehmen *et al.,* 2010) can be some examples of the required developments. Regarding innovative wastewater treatment technologies, a stronger focus can be expected on modelling the bioprocesses and hydrodynamics involved in the aerobic granular sludge (de Kreuk *et al.,* 2007), on the implementation of the Anammox process and related N‐removal processes in the mainstream treatment line (Kartal *et al.,* 2010; Wett *et al.,* 2013; Lackner *et al.,* 2014), and even on the intracellular storage processes of organics for bioplastic production (Bengtsson *et al,.* 2008; Jian *et al.,* 2012). While the aerobic granular technology offers significant benefits for WWTP optimization on both resources and footprint, the last two open new doors towards the conversion of existing WWTP from 'removal‐type systems' towards 'resource‐recovery systems' and 'energy‐factories' (Kartal *et al.,* 2010; van Loosdrecht and Brdjanovic, 2014). Also, the application of the sulphur‐cycle processes to wastewater treatment will likely continue to attract attention for the treatment of sulphur contaminated waters resulting from saline water intrusion, use of seawater for sanitation to alleviate water scarcity, cooling purposes, and industrial activities (Wang *et al.,* 2009; Hao *et al.*, 2014). Undoubtedly, mathematical modelling will be a strong tool to get a better understanding of the factors affecting these processes and facilitate their implementation. Such efforts may imply the need to incorporate and take into account more complex biokinetic models (Nielsen *et al.,* 2010; Oehmen *et al.*, 2010), elemental balance approaches (Takács *et al.*, 2007; Lu *et al.*, 2009), and likely also to establish stronger links with genomics, molecular techniques and metabolome analyses (Fiehn, 2001) as well as to develop the required experimental methods to determine and understand the microbial activities involved. The incorporation of the new processes (e.g. Anammox, and sulphur conversions) will likely follow the IWA GMP‐TG concepts (Rieger *et al.*, 2012) and certainly most of the developments could be expected on the implementation of Anammox processes. The increased complexity of these combined biological processes require development of effective process controls in which application of dynamic ASMs could become crucial for successful full‐ scale application.

As a consequence of the interaction between the existing but also for the implementation of new technologies, plant‐wide modelling will acquire and deserve special attention to: (i) develop an optimal WWTP control strategy, (ii) increase the efficiency of the WWTP removal processes, (iii) reduce the operating costs, (iv) maximize energy recovery through biogas production, and (v) maximize the removal and recovery of nutrients in the side‐stream processes.

As a plant‐wide modelling starting point, the mathematical description of the separation processes in the primary settling tanks (PST) affecting the COD fractions needs to improve (Vanrolleghem *et al.,* 2014). This will contribute to maximize the recovery of energy via the anaerobic digestion of organics and favour the role of WWTP as energy factories (Kartal *et al.*,

on 15 December 2021

**447**

Nevertheless, to apply plant‐wide modelling the biggest challenges can be found when coupling ASM models with anaerobic digestion models (like the ADM1) (Batstone *et al*., 2002). Those challenges are related not only to the description of the sludge digestion processes that take place in sludge thickeners and anaerobic sludge digesters but also to the physicochemical processes occurring within these systems. One of the first challenges is the different sets of state variables used by ASM models and ADM1. Overall, there are two different ways to deal with this issue: (1) the 'super model' approach where a complete set of variables valid for both aerobic and anaerobic environments is defined (Grau *et al.,* 2009), which is also available in e.g. the BioWim simulator developed by EnviroSim (REF) and (2) the use of established interlinked models by applying a set of algebraic transformation equations ('transformers') based on a Gujer matrix description of the two models (Vanrolleghem *et al.,* 2005; Volcke *et al.,* 2006; Nopens *et al.,* 2009). A pioneering and successful attempt of plant‐wide modelling and coupling ASM and ADM models using a designed interface/transformer was demonstrated on the WWTP Anjana in India (Brdjanovic *et al.*, 2007), arguably the first published application of activated sludge modelling in a developing country (Chapter 8). Overall, the previous approaches can satisfactorily help out to apply a plant‐wide model, the supermodel having the biggest potential. However, they cannot be directly applied to the plant‐wide model description of a system performing EBPR. The latter because the anaerobic digestion fate of EBPR sludge (highly rich in phosphorus and intracellular compounds) is not included in ADM1 (Ikumi *et al.*, 2014a,b). Thus, the anaerobic endogenous processes at which an enriched EBPR sludge is exposed in anaerobic digesters cannot be currently described by ADM1. This is also directly linked with the strong

2010). Currently, most of PST separation processes are still modelled as black boxes with lumped and gross removal coefficients assigned to all particulate organics, whereas unbiodegradable particulate organics have shown to be subject to higher removal efficiencies than the

Another important aspect in plant‐wide modelling is the coupling of the state variables (Volcke *et al.*, 2006) from the activated sludge process tanks and those from secondary settling tanks (SST) (Bürger *et al.,* 2011; Torfs *et al.,* 2013). Models for clarifiers use total suspended solids as a state variable, which is not explicitly used in ASM models and need to be calculated as a composite variable of the activated sludge processes. In addition, due to the different redox conditions created, the bottom of a clarifier needs to be dynamically modelled in a similar way like a bioreactor to take into account the potential redox effects on the active biological processes. Among them, rising sludge due to denitrification under anoxic conditions in nitrogen removal plants, secondary P‐release under anaerobic conditions in EBPR systems and the description of the sludge settleability are some examples of the need to secure a satisfactory modelling description of the operation of SST. So far, some of the mentioned processes could be mimicked by the addition of an anoxic (denitrifying) tank in addition to the secondary settling tank. However, this is more an intermediate than the final solution to the problem (e.g. Chapter 3, Figure 3.6). Computational fluid dynamics (CFD) can help to improve the description and operation of SST (Plósz *et al.*, 2012). Furthermore, by studying the influence of diffusion limitations and gradients through CFD, the interactions between bacterial morphology and bacterial competition could be better understood and lead to a better prediction of the sludge bulking phenomenon (Martins *et al.,* 2004a,b). The latter affecting not only the SST performance but also the whole WWTP efficiency and capacity by reducing the sludge settleability. However, besides the added complexity, reliable experimental methods and set‐ups are still needed to support model development and cope with the lack of (quality) data that has limited the use of advanced settling models (Plósz *et al.*, 2012). Such an approach requires tightening the collaboration links between practice and research to assess and provide feedback on newly

biodegradable organics (Ikumi *et al.,* 2014a,b).

developed models under real case scenarios.

2010). Currently, most of PST separation processes are still modelled as black boxes with lumped and gross removal coefficients assigned to all particulate organics, whereas unbiodegradable particulate organics have shown to be subject to higher removal efficiencies than the biodegradable organics (Ikumi *et al.,* 2014a,b).

Another important aspect in plant‐wide modelling is the coupling of the state variables (Volcke *et al.*, 2006) from the activated sludge process tanks and those from secondary settling tanks (SST) (Bürger *et al.,* 2011; Torfs *et al.,* 2013). Models for clarifiers use total suspended solids as a state variable, which is not explicitly used in ASM models and need to be calculated as a composite variable of the activated sludge processes. In addition, due to the different redox conditions created, the bottom of a clarifier needs to be dynamically modelled in a similar way like a bioreactor to take into account the potential redox effects on the active biological processes. Among them, rising sludge due to denitrification under anoxic conditions in nitrogen removal plants, secondary P‐release under anaerobic conditions in EBPR systems and the description of the sludge settleability are some examples of the need to secure a satisfactory modelling description of the operation of SST. So far, some of the mentioned processes could be mimicked by the addition of an anoxic (denitrifying) tank in addition to the secondary settling tank. However, this is more an intermediate than the final solution to the problem (e.g. Chapter 3, Figure 3.6). Computational fluid dynamics (CFD) can help to improve the description and operation of SST (Plósz *et al.*, 2012). Furthermore, by studying the influence of diffusion limitations and gradients through CFD, the interactions between bacterial morphology and bacterial competition could be better understood and lead to a better prediction of the sludge bulking phenomenon (Martins *et al.,* 2004a,b). The latter affecting not only the SST performance but also the whole WWTP efficiency and capacity by reducing the sludge settleability. However, besides the added complexity, reliable experimental methods and set‐ups are still needed to support model development and cope with the lack of (quality) data that has limited the use of advanced settling models (Plósz *et al.*, 2012). Such an approach requires tightening the collaboration links between practice and research to assess and provide feedback on newly developed models under real case scenarios.

Nevertheless, to apply plant‐wide modelling the biggest challenges can be found when coupling ASM models with anaerobic digestion models (like the ADM1) (Batstone *et al*., 2002). Those challenges are related not only to the description of the sludge digestion processes that take place in sludge thickeners and anaerobic sludge digesters but also to the physicochemical processes occurring within these systems. One of the first challenges is the different sets of state variables used by ASM models and ADM1. Overall, there are two different ways to deal with this issue: (1) the 'super model' approach where a complete set of variables valid for both aerobic and anaerobic environments is defined (Grau *et al.,* 2009), which is also available in e.g. the BioWim simulator developed by EnviroSim (REF) and (2) the use of established interlinked models by applying a set of algebraic transformation equations ('transformers') based on a Gujer matrix description of the two models (Vanrolleghem *et al.,* 2005; Volcke *et al.,* 2006; Nopens *et al.,* 2009). A pioneering and successful attempt of plant‐wide modelling and coupling ASM and ADM models using a designed interface/transformer was demonstrated on the WWTP Anjana in India (Brdjanovic *et al.*, 2007), arguably the first published application of activated sludge modelling in a developing country (Chapter 8). Overall, the previous approaches can satisfactorily help out to apply a plant‐wide model, the supermodel having the biggest potential. However, they cannot be directly applied to the plant‐wide model description of a system performing EBPR. The latter because the anaerobic digestion fate of EBPR sludge (highly rich in phosphorus and intracellular compounds) is not included in ADM1 (Ikumi *et al.*, 2014a,b). Thus, the anaerobic endogenous processes at which an enriched EBPR sludge is exposed in anaerobic digesters cannot be currently described by ADM1. This is also directly linked with the strong

on 15 December 2021

**446**

As a plant‐wide modelling starting point, the mathematical description of the separation processes in the primary settling tanks (PST) affecting the COD fractions needs to improve (Vanrolleghem *et al.,* 2014). This will contribute to maximize the recovery of energy via the anaerobic digestion of organics and favour the role of WWTP as energy factories (Kartal *et al.*,

As a consequence of the interaction between the existing but also for the implementation of new technologies, plant‐wide modelling will acquire and deserve special attention to: (i) develop an optimal WWTP control strategy, (ii) increase the efficiency of the WWTP removal processes, (iii) reduce the operating costs, (iv) maximize energy recovery through biogas production, and (v)

maximize the removal and recovery of nutrients in the side‐stream processes.

Mexico (Fall *et al.,* 2012), Croatia (Meijer and Brdjanovic, 2012) and Uruguay (Betancur, 2014) (Chapters 8, 10, 12, 13, and 15). This demonstrates that efforts are undertaken also in developing countries to apply the models to existing activated sludge systems (mostly for

Concerning the future development of activated sludge modelling, it is important to underline and take into consideration the current and future needs and developments. In this regard, the trends regarding existing wastewater process technologies will likely need to further focus on providing a better description of the nutrient removal processes, not only for the sake of the nutrients removal itself, but also to reduce the associated energy costs and environmental impact. Modelling the description of nitrous oxides emissions in nutrient removal plants (Ni *et al.,* 2013) as well as achieving a satisfactory description of the actual (anaerobic, aerobic and anoxic) metabolic activities of the relevant EBPR populations (e.g. *Accumulibacter* type I and type II) and their interactions with "new" bacterial populations (such as *GAO*) (Lopez‐Vazquez *et al.,* 2009; Oehmen *et al.,* 2010) can be some examples of the required developments. Regarding innovative wastewater treatment technologies, a stronger focus can be expected on modelling the bioprocesses and hydrodynamics involved in the aerobic granular sludge (de Kreuk *et al.,* 2007), on the implementation of the Anammox process and related N‐removal processes in the mainstream treatment line (Kartal *et al.,* 2010; Wett *et al.,* 2013; Lackner *et al.,* 2014), and even on the intracellular storage processes of organics for bioplastic production (Bengtsson *et al,.* 2008; Jian *et al.,* 2012). While the aerobic granular technology offers significant benefits for WWTP optimization on both resources and footprint, the last two open new doors towards the conversion of existing WWTP from 'removal‐type systems' towards 'resource‐recovery systems' and 'energy‐factories' (Kartal *et al.,* 2010; van Loosdrecht and Brdjanovic, 2014). Also, the application of the sulphur‐cycle processes to wastewater treatment will likely continue to attract attention for the treatment of sulphur contaminated waters resulting from saline water intrusion, use of seawater for sanitation to alleviate water scarcity, cooling purposes, and industrial activities (Wang *et al.,* 2009; Hao *et al.*, 2014). Undoubtedly, mathematical modelling will be a strong tool to get a better understanding of the factors affecting these processes and facilitate their implementation. Such efforts may imply the need to incorporate and take into account more complex biokinetic models (Nielsen *et al.,* 2010; Oehmen *et al.*, 2010), elemental balance approaches (Takács *et al.*, 2007; Lu *et al.*, 2009), and likely also to establish stronger links with genomics, molecular techniques and metabolome analyses (Fiehn, 2001) as well as to develop the required experimental methods to determine and understand the microbial activities involved. The incorporation of the new processes (e.g. Anammox, and sulphur conversions) will likely follow the IWA GMP‐TG concepts (Rieger *et al.*, 2012) and certainly most of the developments could be expected on the implementation of Anammox processes. The increased complexity of these combined biological processes require development of effective process controls in which application of dynamic ASMs could become crucial for successful full‐

optimization or upgrade).

scale application.

need to achieve a satisfactory description of the metabolism of the dominant EBPR populations since it will define the fractions of the different intracellular compounds (from poly‐P to glycogen and poly‐hydroxy‐alkanoates) contained in the sludge.

Concerning the physical‐chemical processes occurring within the anaerobic systems, ASM models contain only the alkalinity state, which acts mostly as an indicator of the potential inhibition of a biological process if alkalinity decreases, and a single gas transfer model. Whereas the pH description in ADM1 is only valid for dilute systems and do not include a mechanistic (pH‐ based) precipitation (Batstone *et al.,* 2012). However, if EBPR sludge is anaerobically digested the related physical‐chemical processes that take place in the anaerobic sludge digestion systems are required to be modelled. Of particular importance are the physical‐chemical processes affecting the multi‐mineral precipitation (with cations such as iron and aluminium and anions like ortho‐phosphate, carbonate and even sulphur) and the prevailing pH in anaerobic sludge digesters. In this regard, in recent years, an IWA Task Group was initiated for the development of a Generalized Physical‐chemical Framework (Batstone *et al.,* 2012). Although the fundamentals of the physical‐chemical reactions are well understood, available from other disciplines, and do not need calibration (since thermodynamics define the end points of the kinetic processes), the organic compounds driving the bioprocesses and the thermodynamics of the precipitation rates are not yet defined/known, implying that the end points of these reactions need to be known, involving considerable calibration efforts (Ekama, *personal communication*, 2014). These plant‐wide modelling aspects require further research involving both experimental and modelling development activities to clarify and achieve a satisfactory modelling of the physical‐chemical processes. Together with those concerning the implementation of recently developed technologies (such as the implementation of Anammox for the treatment of nitrogen‐rich reject waters) it can contribute to reach the objectives of the plant‐wide modelling philosophy. Ikumi *et al.* (2011, 2014a) have made one of the first steps towards upgrading ADM1 and account for the potential effects of the anaerobic digestion of EBPR sludge.

Besides the potential energy savings that hydroinformatics tools (such as CFD) could bring (Rieger *et al.*, 2012), with an increasing need and interest in water reuse and integrated modelling the biological and physical‐chemical removal processes of micro‐pollutants will be another modelling area of major expansion and development (Gujer *et al.,* 2006; Clouzot *et al.,* 2013) where CFD could also be applied (Radu *et al.,* 2010). Moreover, a growing interest in integrated (urban) water modelling will continue to motivate integration of wastewater treatment process models with receiving water quality (RWQM) and sewer models (Gujer, 2006; Vanrolleghem *et al.,* 2014). Until a few years ago, only hydraulics and pollutant transport phenomena in the sewers were taken into account (Hvitved‐Jacobsen, 2013). However, recent models start to consider the chemical and biological processes that take place in the sewer system, looking at the sewers as physical, chemical and biological reactors (Rauch *et al.,* 2002). One of the first examples of holistic modelling (combined sewage network, WWTP and the recipient/river) using different models (combining Mike Urban, BioWin and HEC‐RAS), although carried out in a sequential mode (as opposite to a better and more realistic but much more complex real‐time approach), showed great advantages of such modelling application (Hodzic *et al.,* 2011, Chapter 10; Price and Vojinovic, 2010). This is of major importance for the design, operation and maintenance of sewer networks, not only from a holistic water management perspective but also from a potential future asset management focus (which needs a satisfactory modelling description of the removal of micropollutants). In collaboration with the University of Cape Town, WEST, a hydraulic modelling software developed by Gent University (Vanhooren *et al.,* 2003) and nowadays held by the Danish Hydraulic Institute (DHI), has been upgraded to make the prime efforts to link a wastewater treatment model with a RWQM and a sewer model

on 15 December 2021

**449**

(Ikumi *et al.,* 2014b). Together with the plant‐wide modelling advances, this can open promising lines towards the development of an integrated urban water model suitable and capable to describe and optimize the entire urban water system. Likely, such a holistic approach will be also of importance and useful when dealing with secondary quality water sources for sanitation to contribute to alleviate water scarcity issues (like the use of saline water for sanitation and the

Bearing in mind that 2.6 billion people still do not have access to sanitation, that most of population in developing countries is not connected to sewer systems, and that only a small fraction of sewage in developing countries is treated, brings the issue of holistic modelling where the urban drainage and sewerage models and wastewater treatment models (not only ASM and ADM) will be complemented by and integrated with (de)centralized sanitation models in cities which are not fully covered by sewerage. There are several similar examples worldwide, especially in developing countries. Recent advances in this direction include a decentralized sanitation model created by the software developers of the SIMBA simulator (funded by the Bill & Melinda Gates Foundation and therefore expected to be freely accessible) and a decision support tool called WAMEX by UNESCO‐IHE and funded by the Asian Development Bank (and

Cloud computing has gained in interest lately (Armbrust *et al.,* 2010), by joining efforts and contributing to standardize approaches and notation (Corominas *et al.*, 2010), sharing wastewater treatment models between researchers, software developers, and practitioners, while being in different longitudes and latitudes, may not be far from a dream. This can be a strong tool to facilitate the application of plant‐wide and integrated urban water modelling to contribute to optimize the water quality and quantity transported through the aquatic veins and

From a commercial and practical perspective, the incorporation of the processes and approaches described previously will considerably increase the model complexity. However, understandably, practitioners feel uncomfortable working with increasingly complex models. So, possibly, vendors with specific modelling skills will appear on the market, since conventional wastewater treatment 'generalists' will not be able to cope with the fast release and development of more complex models for particular applications. Thus, like in other fields, in the near future consultants will outsource their modelling activities to specific vendors (Ekama,

It is not impossible to imagine that sooner or later new interfaces and way of interactions between (probably or even likely less specialized) users and models will be created. Maybe, in the form of multi‐layer serious gaming and using 3D urban water system simulators with simplified 'surface' user interfaces and more complex expert models running 'invisibly' in the background (Ekama and Brdjanovic, *personal communication*, 2014). An expected future development is the use of models built in data acquisition systems (SCADA) of larger wastewater treatment facilities. Thereby the complex knowledge contained in ASMs is made available for process operators making more efficient and safe plant operation possible on a daily basis. It is also expected that the modelling boundaries will be further extended reaching trans‐disciplinary character as other issues will be included, e.g. emergencies, risks, and social aspects (Vojinovic and Abbott, 2011; Brdjanovic *et al.,* 2013). By doing so, modelling will come closer to decision makers and increase and facilitate the use of models by different and currently not involved

implementation of the SANI process) (Lu *et al.,* 2009; Wang *et al.,* 2009).

also freely available).

arteries of an urban settlement.

*personal communication*, 2014).

stakeholders.

(Ikumi *et al.,* 2014b). Together with the plant‐wide modelling advances, this can open promising lines towards the development of an integrated urban water model suitable and capable to describe and optimize the entire urban water system. Likely, such a holistic approach will be also of importance and useful when dealing with secondary quality water sources for sanitation to contribute to alleviate water scarcity issues (like the use of saline water for sanitation and the implementation of the SANI process) (Lu *et al.,* 2009; Wang *et al.,* 2009).

Bearing in mind that 2.6 billion people still do not have access to sanitation, that most of population in developing countries is not connected to sewer systems, and that only a small fraction of sewage in developing countries is treated, brings the issue of holistic modelling where the urban drainage and sewerage models and wastewater treatment models (not only ASM and ADM) will be complemented by and integrated with (de)centralized sanitation models in cities which are not fully covered by sewerage. There are several similar examples worldwide, especially in developing countries. Recent advances in this direction include a decentralized sanitation model created by the software developers of the SIMBA simulator (funded by the Bill & Melinda Gates Foundation and therefore expected to be freely accessible) and a decision support tool called WAMEX by UNESCO‐IHE and funded by the Asian Development Bank (and also freely available).

Cloud computing has gained in interest lately (Armbrust *et al.,* 2010), by joining efforts and contributing to standardize approaches and notation (Corominas *et al.*, 2010), sharing wastewater treatment models between researchers, software developers, and practitioners, while being in different longitudes and latitudes, may not be far from a dream. This can be a strong tool to facilitate the application of plant‐wide and integrated urban water modelling to contribute to optimize the water quality and quantity transported through the aquatic veins and arteries of an urban settlement.

From a commercial and practical perspective, the incorporation of the processes and approaches described previously will considerably increase the model complexity. However, understandably, practitioners feel uncomfortable working with increasingly complex models. So, possibly, vendors with specific modelling skills will appear on the market, since conventional wastewater treatment 'generalists' will not be able to cope with the fast release and development of more complex models for particular applications. Thus, like in other fields, in the near future consultants will outsource their modelling activities to specific vendors (Ekama, *personal communication*, 2014).

It is not impossible to imagine that sooner or later new interfaces and way of interactions between (probably or even likely less specialized) users and models will be created. Maybe, in the form of multi‐layer serious gaming and using 3D urban water system simulators with simplified 'surface' user interfaces and more complex expert models running 'invisibly' in the background (Ekama and Brdjanovic, *personal communication*, 2014). An expected future development is the use of models built in data acquisition systems (SCADA) of larger wastewater treatment facilities. Thereby the complex knowledge contained in ASMs is made available for process operators making more efficient and safe plant operation possible on a daily basis. It is also expected that the modelling boundaries will be further extended reaching trans‐disciplinary character as other issues will be included, e.g. emergencies, risks, and social aspects (Vojinovic and Abbott, 2011; Brdjanovic *et al.,* 2013). By doing so, modelling will come closer to decision makers and increase and facilitate the use of models by different and currently not involved stakeholders.

on 15 December 2021

**448**