Model based optimization of partial nitrification by monitoring nitrous oxide (N2O) emission

Journal of Environmental Chemical Engineering 3 (2015) 1602–1613

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Journal of Environmental Chemical Engineering journal homepage: www.elsevier.com/locate/jece

Model based optimization of partial nitrification by monitoring nitrous oxide (N2O) emission Jun Wu* , Ting Xu, Xinyue Jiang, Gang Yan, Lingtang Yu School of Environmental Engineering and Science, Yangzhou University, 196 West Huayang Road, Yangzhou, Jiangsu 225127, China

A R T I C L E I N F O

A B S T R A C T

Article history: Received 23 December 2014 Accepted 21 May 2015

The successful operation of partial nitrification relies on the optimal condition in which the ammonia oxidizing bacteria (AOB) is favored over nitrite oxidizing bacteria (NOB). The fluctuation of influent load means the optimal condition varies dynamically. The routine monitoring such as dissolved oxygen (DO), nitrite and ammonia concentration is usually used for the system optimization. The sensors used for these routine monitoring are usually submerged in a hostile activated sludge environment and are subjected to sensor fouling. In this paper, the non-invasive off-gas nitrous oxide (N2O) monitoring was used for the optimization of partial nitrification. An empirical N2O emission model and a two-step nitrification activated sludge model No. 1 (ASM1) were used for the optimization study based on the concept of model predictive control. The results indicated that the non-invasive N2O monitoring can be used as an alternative to the nitrite and ammonia sensor for the optimization of partial nitrification process. ã 2015 Elsevier Ltd. All rights reserved.

Keywords: Model based optimization Model predictive control Partial nitrification Nitrous oxide emission Oxygen transfer coefficient

Introduction Biological wastewater treatment process (WWTP) is a highly non-linear process, subject to constant perturbations in flow and load. It is impossible to operate a WWTP efficiently with constant settings of various pumps, compressors and valves [1]. The strict effluent regulations and energy shortage mean that the operation of WWTP needs to be optimized continuously [2,3]. New technology such as partial nitrification has a unique feature in energy saving [4], but needs to be optimally controlled to maximize its efficiency [5]. Partial nitrification Wastewater nitrogen removal is usually carried out by the biological nitrification–denitrification process. The complete nitrification can be described by a two-step process, with the first conversion of the ammonia (NH4+) into nitrite (NO2
) by ammonia oxidizing bacteria (AOB) and subsequent nitrite into nitrate (NO3
) by nitrite oxidizing bacteria (NOB) [6]. Recently, partial nitrification (ammonia oxidation to nitrite) has been shown to be an attractive technology due to the reduction of 25% in the

* Corresponding author. Tel.: +86 514 87971389; fax: +86 514 87978626. E-mail address: [email protected] (J. Wu). http://dx.doi.org/10.1016/j.jece.2015.05.017 2213-3437/ ã 2015 Elsevier Ltd. All rights reserved.

oxygen supply in the nitrification step and the 40% reduction of carbon source requirement in denitrification step. Significant reduction in operational cost and carbon dioxide emission can be achieved by the partial nitrification process [7]. Partial nitrification is usually achieved by providing growth condition that is favourable for AOB but inhibits NOB growth. The growth condition includes high temperature, high pH, low dissolved oxygen (DO) concentration, high free ammonia concentration or addition of chemicals for inhibition of nitrification [4,7–10]. Except for the DO, manipulation of other operational variables is not feasible for practical wastewater treatment. In practice, partial nitrification can be maintained by applying a low DO concentration. NOB has a lower DO affinity (higher DO saturation value) than AOB. Under low DO, the growth rate of NOB is much lower than AOB, therefore nitrite accumulation occurs [11,12]. Optimization strategies for partial nitrification Due to the external disturbance in influent flow rate and concentration, the load to the partial nitrification process is always in a dynamic condition. Process optimization is necessary to maximize nitrite accumulation and reduce oxygen supply. Several optimization studies in partial nitrification were carried out on the sequencing batch reactor (SBR) [5,13,14]. Bournazou et al. [5] developed optimal intermittent aeration policy for the partial

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Table 1 System design for the partial nitrification process. Hydraulic retention time

Solid retention time

Volume ratio between anoxic and aerobic tank

Internal return flow

Recycle sludge flow

HRT 10 h

SRT 15 days

Vd:Vn 1:3

QR 2

Qc 0.5

The sensors used for wastewater treatment process are usually submerged into the liquid phase (“wet” sensor) [15]. These sensors are prone to sensor fouling due to the hostile environment of placement and can be costly to maintain [16]. Recently, the noninvasive process monitoring technology (“dry” sensor) has been shown to be a potential alternative for the “wet” sensors by removing contact with hostile environment [17]. One type of the “dry” sensor is based on the measurement of off-gas from the aeration tank to quantify the activated sludge biological activity [16–21]. The carbon dioxide (CO2) online off-gas monitoring was used by Weissenbacher et al. [21] to detect changes in activated sludge biological activity under limited DO conditions such as simultaneous nitrification–denitrification (SND). The nitrification rate estimated from CO2 online off-gas measurement matched well with the liquid phase nitrate analysis [20]. This suggested that the online off gas measurement can be an alternative or complement to the liquid phase off-line measurement or “wet” sensors. Nitrous oxide (N2O) emission monitoring was used by Burgess et al. [22] and Butler et al. [16,23] for nitrification failure early warning. The disruption of nitrification due to shocking load, toxic substance was reflected in the immediate increasing of N2O emission. A long lag time was observed between the increased effluent ammonia concentration and N2O emission increase. Therefore the authors argued that N2O emission can be used for nitrification failure early warning. A N2O off-gas model that linked the DO and N2O emission was used by Sivret et al. [17] for aeration control. The oxygen depletion due to ammonia shocking load can be successfully managed by applying aeration control from the N2O off-gas data.

emission [26,27]. Pilot [28] and full-scale investigation both pointed a strong correlation between the nitrite accumulation and N2O emission [29]. The amount of N2O emission was found to be correlated to the effluent nitrite concentration from partial nitrification process [30,31]. A linear relationship between the ammonia oxidation rate and N2O emission rate was observed under partial nitrification [32]. This suggested that the extent of partial nitrification can be estimated from the N2O emission rate. From the process optimization point of view, maximizing ammonia conversion to nitrite during partial nitrification can be achieved by maximizing the N2O emission rate. As the N2O emission can be monitored by non-invasive optical sensors, it offers a better alternative to the liquid phase “wet” sensors for the optimization of partial nitrification process. In addition to be a process monitoring tool, the N2O emission also poses a serious threat to the environment due to its greenhouse effect [33]. The stratospheric reaction between atomic oxygen and N2O produces NO, which can cause the depletion of ozone layer [34]. Therefore, the N2O emission should also be limited while maximizing the partial nitrification. This seemingly contradictive task can only be solved by using a model based optimization. The N2O emission monitoring has been used for early warning and aeration control of complete nitrification process [16,17,22,23]. The integration of N2O emission data in partial nitrification optimization has not been investigated. Due to the close connection between N2O emission and nitrite accumulation [24], it is expected that N2O emission can play a role in the partial nitrification optimization. In this study, a model based optimization procedure for partial nitrification was developed to: (1) test the feasibility of using N2O emission data for partial nitrification optimization; (2) minimize aeration supply; (3) maximize ammonia conversion in partial nitrification; (4) limiting N2O emission. A process model for the partial nitrification and N2O emission was developed based on the two-step nitrification ASM1 (activated sludge model No. 1) [35].

Partial nitrification considering N2O emission

Material and methods

Partial nitrification is usually achieved by maintaining a low DO concentration, which was also considered to be the main factor for the N2O emission [24]. Under low DO concentration, N2O can be produced by AOB using nitrite as electron acceptor and ammonia as electron donor, a process called nitrifier denitrification [25]. Pulse addition of nitrite leads to immediate response of N2O

plant layout and influent characteristics

nitrification in SBR, in which both the operation time of an SBR cycle and aeration energy supply can be minimized. A total and stable wash of NOB can be achieved in a partial nitrification process by controlling inflow rate, DO and pH [7]. Process monitoring with “wet” and “dry” sensor

A typical pre-nitrification nitrogen removal plant shown in Fig. 1 is used for the evaluation of various optimization strategies. The plant configuration is shown in Table 1. Both steady state and dynamic modelling were carried out on the plant. The

Fig. 1. Plant configuration of the pre-denitrification partial nitrification process.

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Table 2 The flow-weighted average influent concentration.

1 2 3 4 5 6 7 8 9 10 11 12 13

Symbol

Definition

Value

Influent concentration

Initial value in the denitrification tank

Initial value in the nitrification tank

SI SS XI XS XH XAOB XNOB SO SNO2 SNO3 SNH4 SND XND

Inert soluble COD Readily biodegradable COD Particulate inert COD Slowly biodegradable COD Heterotrophic biomass Ammonia oxidizing biomass Nitrite oxidizing biomass DO concentration Nitrite Nitrate Ammonia Soluble organic nitrogen Particulate organic nitrogen

mg COD/L mg COD/L mg COD/L mg COD/L mg COD/L mg COD/L mg COD/L mg O2/L mg N/L mg N/L mg N/L mg N/L mg N/L

30 69.5 51.20 202.32 28.17 0 0 0 0 0 31.56 6.95 10.59

30 55.43 2807.18 35.47 1530.68 177.09 3.52 0 0.05 0 12.74 1.36 2.36

30 3 2814.11 14.83 1547.39 178.83 3.55 0.7 6.23 0.01 1.16 0.79 1.21

flow-weighted average influent concentration from [36] was used for the steady state modelling (Table 2). The dynamic simulation was carried out using the “dry weather” influent data from the ASM benchmark simulation manual [36]. The benchmark influent data are available in 14 days with 15 min interval. To eliminate the influence of initial conditions on the modelling results, the initial state was derived by simulating the plant over 150 days using the flow-weighted average influent data (Table 1). The DO concentration for the simulation over 150 days was set to be 2 mg/L, although the partial nitrification is usually favored at a much lower concentration. If low DO concentration was used for the 150 days simulation, the NOB washout will occur. This will cause bias for the evaluation of optimization strategies derived from the dynamic simulation with 14 days dry weather data. In NOB washout situation, highest ammonia to nitrite conversion rate occurs at highest DO concentration, which differs from the typical partial nitrification.

disturbance such as toxic substances, shocking load, increased organic or nitrogen load, unsuitable sludge residence time (SRT), or insufficient aeration capacity [7]. For the partial nitrification process, as its main operational objective is for the maximum conversion of ammonia into nitrite, the one-step nitrification model is not suitable. Various modifications of the original ASMs into two-step nitrification exist in the literature [37–39]. Compared to the standard ASM1 4 new model state variables were included. The SNOX is replaced with SNO2 and SNO3. The autotrophic biomass XA is replaced with XAOB and XNOB. The following 6 new processes will be used for the two-step nitrification and de-nitrification model.

The plant models

     

The two-step nitrification ASM1 model The ASM family (ASM 1, 2, 2 D, 3) [35] do not include nitrite as model state variable because nitrite is an intermediate product, readily oxidized into nitrate and exists at very low concentration (less than 0.1 mg/L) during normal operation [37]. The nitrite concentration can increase due to microbiological processes

The two-step nitrification was mainly modelled by the growth of AOB and NOB shown in Eqs. (1) and (2). At the low DO concentration, the AOB is favored and NOB is inhibited. This is achieved by choosing a lower DO half-saturation value for AOB (KO, AOB) than NOB (KO,NOB). Experimental verification also supported a small DO half-saturation value for the AOB [11,12]. In the two-step

Aerobic growth of XAOB Aerobic growth of XNOB Endogenous respiration of XAOB Endogenous respiration of XNOB Anoxic growth of XH (NO3
1–NO2
1) Anoxic growth of XH (NO2
1–N2)

Table 3 Process kinetics and stoichiometry for the two-step nitrification ASM1 model. Component

Components 1

1

Processes Aerobic growth of XH

2

Anoxic growth of XH()

3

Anoxic growth of XH()

4

Aerobic growth of AOB

5

Aerobic growth of NOB

2

SI SS
1/ YH
1/ YH
1/ YH

Reaction rate 4

XI XS

5

6

7

XH 1

XAOB XNOB SO 1
1/YH

1

1

7

8

1

1

6

Endog. respiration of XH Endog. respiration of XAOB Endog. respiration of XNOB 8 Ammonification of SND 9 Hydrolysis of entrapped XND 10 Hydrolysis of XS

3

fp fp

1
fp
1 1
fp
1

fp

1
fp

1
3.43/ YA 1
1.14/ YA

9

10

11

12

SNO2

SNO3

SNH4
iXB

SND XND

1
YH/ 1.14YH 1
YH/ 1.72YH 1/YA


1
YH/ 1.14YH


1/YA

1/YA


1

SO S mH K SSþS XH S K O;H þSO


iXB

K O;H SNO3 S hg mH KSSþS XH S K O;H þSO K NO3 þSNO3


iXB

K O;H SNO2 S hg mH KSSþS XH S K O;H þSO K NO2 þSNO2


1/ YA
iXB
iXB

SO mAOB K A;NHSNHþSNH K O;AOB þSO X AOB SNO2 SO mNOB KA;NO2 þSNO2 K O;NOB þSO X NOB


iXB
fpiXP bHXH
iXB
fpiXP bAXAOB
1


iXB
fpiXP bAXNOB 1

1

13


1 1


1

kaSNDXH r10(XND/XS) S =X H XH kH K XXþX S =X H

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Table 4 Kinetic and Stoichiometric parameters for the modified ASM1. No.

Symbols

Descriptions

Values

Source

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

mH

Maximum specific growth rate for heterotrophic biomass (d
1) Half-saturation coefficient for heterotrophic biomass (mg COD/L) Oxygen half-saturation coefficient for heterotrophic biomass (mg DO/L) Correction factors for under anoxic conditions Nitrate half-saturation coefficient for heterotrophic biomass (mg N
NO3/L) NitrIte half-saturation coefficient for heterotrophic biomass (mg N
NO2/L) Maximum specific growth rate for AOB (d
1) Ammonia half-saturation coefficient AOB (mg N
NH4/L) Oxygen half-saturation coefficient for AOB (mg DO/L) Maximum specific growth rate for NOB (d
1) Nitrite half-saturation coefficient for NOB (mg N
NH4/L) Oxygen half-saturation coefficient for NOB (mg DO/L) Decay coefficient for heterotrophic biomass (d
1) Decay coefficient for AOB and NOB (d
1) Ammonification rate (d
1) Maximum specific hydrolysis rate (d
1) half-saturation coefficient for hydrolysis Yield for heterotrophic biomass Mass of Nitrogen per biomass COD (gN/g COD) Yield for AOB and NOB Fraction of biomass leading to particulate products Mass of Nitrogen per biomass day product COD (gN/g COD)

6 20 0.2 0.8 0.5 0.5 0.95 2 0.74 0.65 0.5 1.75 0.62 0.061 0.08 3.0 0.03 0.67 0.086 0.24 0.08 0.06

Henze 2000 Henze 2000 Henze 2000 Henze 2000 Guisasola 2005 Guisasola 2005 Guisasola 2005 Guisasola 2005 Guisasola 2005 Guisasola 2005 Guisasola 2005 Guisasola 2005 Henze 2000 Henze 2000 Henze 2000 Henze 2000 Henze 2000 Henze 2000 Henze 2000 Henze 2000 Henze 2000 Henze 2000

KS KO,H

hg

KNO3 KNO2

mAOB KA,NH KO,AOB

mNOB

KA,NO2 KO,NOB bH bA ka kH kX YH iXB YA fP iXP

nitrification model by Ostace et al. [38], a same DO half-saturation value for AOB and NOB was used. The inhibition of NOB under low DO was achieved by an additional ammonia switching function for NOB. dX AOB SNH SO ¼ mAOB X
bA X AOB dt K A;NH þ SNH K O;AOB þ SO AOB

(1)

dX NOB SNO2 SO ¼ mNOB X NOB
bA X NOB dt K A;NO2 þ SNO2 K O;NOB þ SO

(2)

The completed model matrix can be found in the Table 3 and the typical parameter values can be found in Table 4. A complete explanation of model symbols and abbreviations can be found in Ref. [35]. N2O emission model N2O can be produced in both nitrification and denitrification process [24]. Three major biological pathways for the N2O emission during the nitrification and denitrification process have been identified [24,26,28]: (1) incomplete oxidation of hydroxylamine (NH2OH) [40]; (2) nitrifier denitrification [25]; (3) heterotrophic denitrification [41]. Based on the three pathways, various mathematical models for N2O emission were proposed. Most of the proposed models were developed by augmenting the existing activated sludge models (ASMs) with more model states and processes [29,42–45]. This leads to very complicated models, which are not suitable for optimization purpose. In this study, a simplified empirical model was developed by considering the major factors for N2O emission [24]. The empirical model is by no

means a replacement for the mechanistic N2O emission models [29,42–45]. DO and nitrite (NO2
1) was considered to be the main factor affecting the N2O emission during nitrification [24]. Under low DO concentration, N2O can be produced by AOB using NO2
1 as electron acceptor and NH4+ as electron donor [25]. Pulse addition of NO2
1 leads to immediate response of N2O emission [26,27]. Pilot [28] and full-scale investigation both pointed a strong correlation between the nitrite accumulation and N2O emission [29]. Eq. (3) is used to represent the N2O emission considering the nitrite and DO concentration. r N ¼ mN

SNO2 kN SNO2 þ kNNO2 kNDO þ SO

(3)

where mN is the maximum specific N2O gas emission rate (mg N
N2O g
1 SS h
1), kNNO2 is the half-saturation constant for NO2
1, kNDO is the half-saturation constant for DO in the N2O emission model. Parameter estimation for the N2O emission model The experimental ammonia, nitrite, DO and N2O emission data from Ref. [28] was used for the parameter estimation of the N2O emission model by curve fitting method. Table 5 is a list of the derived parameter values. The measured and modelled N2O emission rate is shown in Fig. 2. To test the validity of the estimated parameters, N2O emission data from various experimental conditions with different reactor types (batch or continuous), scales (laboratory or full scale), feed characteristics (synthetic or real wastewater) [26,27,46] were compared to the N2O emission model (data not shown). The same half-saturation parameters (kNDO, kNNO2) estimated from the pilot scale experiment [28] can be used for the calibration of the N2O

Table 5 parameters used for the N2O emission model. Symbol

Definition

Value

Unit

kNDO

DO half-saturation constant for the inhabitation of N2O emission by high DO Maximum specific N2O emission rate Half-saturation constant for NO2
1

1.7 33 3.9

mg O2/L mg N-N2O g
1 SS h
1 mg N-NO2+/L

mN

kNNO2

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7

2.5 Measured N2O emission

6

2

Modeled N2O emission 1.5

4 3

1

NO+2 (mg/L)

N2O (µg g-1SS h-1)

Measured NO+2 5

2 0.5 1 0

0

5

10

15

20

25 Time (hour)

30

35

40

45

0 50

Fig. 2. Measured nitrite (triangle), N2O emission (square) (Lotito et al. [28]) and modelled N2O emission profile (circle).

emission model in different experimental conditions. However, the maximum specific N2O emission rate parameters identified in full scale study was much smaller than the laboratory studies. This means that half-saturation parameters (kNDO, kNNO2) are insensitive parameters and can be kept constant during piratical application.

[26–28,30,32] provides the basis for the N2O emission monitoring to be used in the partial nitrification process optimization, as its main objective is to maximize ammonia conversion into nitrite.

Model simulation of N2O emission rate and nitrite concentration

The oxygen transfer coefficient KLa was the only manipulated variable for the various optimization strategies. Other variables affecting the partial nitrification, such as the temperature, pH and toxic substance, cannot be easily manipulated for practical reasons. The manipulated variable KLa was calculated at each 15 min from the solution of various objective functions. The highest limit KLa for was set to 300 days
1.

Using the two-step nitrification and N2O emission model developed above, the steady state nitrite concentration and N2O emission rate can be simulated. Fig. 3(1) shows the steady state nitrite concentration and N2O emission rate under different DO concentration. It can be seen that similar trend was simulated for the N2O emission and nitrite concentration profile. The highest nitrite concentration and N2O emission rate was both shown to be at 0.6 mg O2/L, which corresponds to the experiment observation by Ref. [47]. The oxygen transfer coefficient KLa for the nitrification tank was set to be 150 days
1 for the dynamic simulation. The variation of the nitrite concentration and N2O emission rate is shown in Fig. 3(2). For graphic clarity, only the first 48 h data were shown. A similar trend for the N2O emission rate and nitrite concentration profile is also simulated during the dynamic simulation. The same trend was also observed in the experiment by [28] (Fig. 2). The similar trend of the nitrite concentration and N2O emission rate shown in the simulation and experimental results

The model based optimization strategies

The model base optimization procedures The partial nitrification performance is directly related to the optimized supply of aeration to the nitrification tank. The model based optimizer (MPO) was used for estimation of the best KLa value (Fig. 4). The influent flow and concentration was measured once in 15 min. Therefore, the time step used in the optimization was set to15 min. Once the current influent characteristics was measured, the optimized KLa for the next time step was derived from the MPO considering the objective function, influent characteristics and current state of the partial nitrification process.

(1)

(2)

7

20 NO+2 Concentration

Modeled N2O emission

4 3

16 5

→ 12

8

2

12

4 3

8

2 4

1 0

4 1

0

0.5

1

1.5 2 DO (mg O2/L)

2.5

0 3

0

0

6

12

18 24 30 Time (hour)

36

42

Fig. 3. Comparison of the trend of N2O emission and nitrite concentration under steady and dynamic state.

0 48

N2O (µg g-1SS h-1)

NO+2 Concentration

16

NO+2 (mg/L)

5

20

6

Modeled N2O emission

N2O (µg g-1SS h-1)

6

NO+2 (mg/L)

7

J. Wu et al. / Journal of Environmental Chemical Engineering 3 (2015) 1602–1613

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Fig. 4. Optimization scheme of the partial nitrification process.

The concept of model predictive control (MPC) was used for the estimation of optimized KLa. MPC is a process control algorithms that search for the manipulated variable adjustments in order to optimize the future behaviour of a process [48]. The time steps used for searching manipulated variable is called control horizon (N  1) and the time steps for optimization of future plant behaviour is called optimization horizon (P  1). The control horizon (N) is usually less than optimization horizon (P). The MPC algorithms try to optimize plant behaviour for the next P time steps by applying different manipulated variables for the next N time steps. In the partial nitrification process, once the objective function was satisfied and N KLa values for the next N time steps were derived from the MPC algorithms. The first KLa value was assigned to the aeration system for the next step. The formation of objective function depends on operation objectives. In this study, the optimal solutions from the various objective functions were compared with constant DO concentration of 0.6 mg/L and constant manipulated variable of KLa = 120 days
1 situation. The DO = 0.6 mg/L and KLa = 120 days
1 situation were chosen for comparison because highest ammonia to nitrite conversions were simulated under steady state conditions.

The objective functions The objective functions were constructed in a way that optimal solution can be derived by minimizing the objective function values. Maximizing nitrite concentration (OFA) Perhaps the most intuitive solution for maximizing partial nitrification efficiency is to maximize the nitrite concentration (Eq. (4)). Zt2 J¼


NO
2 ðtÞdt

(4)

t1

The duration from t1 to t2 is 15 min. A minimization of the objective function maximizes the integral of nitrite concentration within t1 and t2. Minimizing ammonia concentration and aeration supply (OFB) The efficient operation of partial nitrification means that the first step of nitrification dominated the ammonia conversion reactions. The aeration supply should be enough for the ammonia

(3) N2O emission

(1) Ammonia, Nitrite and Nitrate 10

(3) DO

14

3

9 12

2.5

10

6 5 4 3

2 8

mg O2/L

7 N2O (µg g-1SS h-1)

N-NH4+, N-NO-2, N-NO-3 (mg/L)

8

6

1.5

1 4

2

0.5

2 1 0

8

8.5

9 Time (days )

9.5

10

0

8

8.5

9 Time (days)

9.5

10

0

8

8.5

9 Time (days)

9.5

10

Fig. 5. Partial nitrification at constant KLa = 120 days
1 nitrite (thick solid line), nitrate (thick dashed line) and ammonia concentration (thin solid red line); (2) oxygen transfer coefficient KLa; (3). N2O emission rate.

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(2) K La (d-1)

(1) Ammonia, Nitrite and Nitrate 9

(3) N2O emiss ion

200

16

180

15

160

14

6 5 4 3

N2O (µg g-1SS h-1)

7

KLa (d-1)

N-NH4+, N-NO2-, N-NO3- (mg/L)

8

140

120

13

12

2 100

11

1 0

8

8.5

9 9.5 Time (days )

10

80

8

8.5

9 9.5 Time (days )

10

10

8

8.5

9 9.5 Time (days )

10

Fig. 6. Partial nitrification at constant DO = 0.6 mg/L; (1) nitrite (thick solid line), nitrate (thick dashed line) and ammonia concentration (thin line); (2) oxygen transfer coefficient KLa; (3) N2O emission rate.

J ¼ $ NH4

Zt2 t1

Zt2 K L aðtÞdt

(5)

t1

(2) KLa (d-1)

(1) Ammonia, Nitrite and Nitrate 10

250 200

5

0

8

8.5 9 9.5 Time (days)

150 100 50

10

8

(3) N2O emission 1.5

15

1

10 5

8

8.5 9 9.5 Time (days)

8.5 9 9.5 Time (days)

10

(4) DO

20

mg O2/L

N2O (µg g-1SS h-1)

NHþ 4 ðtÞdt þ $ KLa

where vNH4 and vKLa are the weighting factors for ammonia concentration and KLa.

KLa (d-1)

N-NH+4, N-NO-2, N-NO-3 (mg/L)

conversion into nitrite, but be tightly controlled to prevent the oxidation of nitrite to nitrate. Therefore, the objective function can be formatted by the weighed sum of ammonia concentration and oxygen transfer coefficient KLa (Eq. (5)).

10

0.5 0

8

8.5 9 9.5 Time (days)

10

Fig. 7. Partial nitrification optimized by objective function of (OFA) maximizing nitrite and (OFB) minimizing ammonia and KLa: (1) OFA–nitrite (thick grey solid line), OFB– nitrite (thick black solid line), OFA–nitrate (thick grey dashed line), OFB–nitrate (thick black dashed line), OFA–ammonia concentration (thin red solid line), OFB–ammonia concentration (thin black dashed line); (2) OFA–oxygen transfer coefficient KLa (grey line), OFB–oxygen transfer coefficient KLa (black line); (3) OFA–N2O emission rate (grey line), OFB–N2O emission rate (black line).

J. Wu et al. / Journal of Environmental Chemical Engineering 3 (2015) 1602–1613

Maximizing the N2O emission (OFC) Due to the similar trend for N2O emission and nitrite concentration observed in experiments and simulations, maximizing the N2O emission can be used to achieve high nitrite accumulation in partial nitrification by apply the following objective function (Eq. (6)). Zt2 J¼


N2 OðtÞdt

(6)

t1

Maximizing N2O emission with constraint on ammonia concentration (OFD) Maximizing N2O emission might not guarantee the ammonia concentration below a certain limit. Two types of constraint: hard and soft bypass constraints were added to the objective function [5]. Hard bypass constraint is included by using additional inequality equation to the objective function (Eq. (7)). Zt2 J¼


N2 OðtÞdt

(7)

J ¼ $ NH4

Zt2

NHþ 4 ðtÞdt
$ N2 O

t1

1609

Zt2 N2 OðtÞdt

(8)

t1

According to the objective function, the optimization process will try to search for a high N2O emission while minimizing the ammonia concentration. However, there is no hard limit for the ammonia concentration. Maximizing nitrite concentration with N2O emission constraint (OFE) The N2O emission poses a serious threat to the environment due to its greenhouse effect. Therefore N2O emission constraint was applied to the objective function (Eq. (9)). Zt2 J¼


NO
2 ðtÞdt

(9)

t1

Subject to: N2 O  10or14mg=ðgSS  hÞ Two hard bypass constraints of 10 and 14 mg/(g SS  h) were used for the N2O emission rate to find out the effect of N2O emission constraint on optimization results. Results of various optimization strategies

t1

Constant DO and constant KLa Fig. 5 shows the partial nitrification process under the constant KLa = 120 days
1 from day 8 to day 10. The constant KLa means no control was applied to the partial nitrification process, as constant

(2) KLa (d-1)

(1) Ammonia, Nitrite and Nitrate 10

5

0

200

KLa (d-1)

N-NH+4, N-NO-2, N-NO-3 (mg/L)

Subject to: SNH4  2:0mg=L The above inequality equation set a limit on the maximum allowed ammonia concentration to be 2.0 mg/L The soft bypass constraint is defined by appending the integral of ammonia concentration to the objective function (Eq. (8)):

8

8.5 9 9.5 Time (days )

150 100 50

10

8

(3) N2O emiss ion 1 0.8

15

mg O2/L

N2O (µg g-1SS h-1)

10

(4) DO

20

10 5

8.5 9 9.5 Time (days )

0.6 0.4

8

8.5 9 9.5 Time (days )

10

0.2

8

8.5 9 9.5 Time (days )

10

Fig. 8. Partial nitrification optimized by objective function of maximizing N2O emission (OFC) using different control horizon (N) and optimization horizon (P). Case 1: long horizon, N = 4, P = 2; case 2: short horizon, N = 1, P = 1; (1) case 1-nitrite (thick grey solid line), case 2-nitrite (thick black solid line), case 1-nitrate (thick grey dashed line), case 2-nitrate (thick black dashed line), case 1-ammonia concentration (thin red solid line), case 2-ammonia concentration (thin black dashed line); (2) case 1-oxygen transfer coefficient KLa (grey line), case 2-oxygen transfer coefficient KLa (black line); (3) case 1-N2O emission rate (grey line), case 2-N2O emission rate (black line).

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aeration flow rate was used for constant KLa situation. The KLa = 120 days
1 was chosen as highest ammonia to nitrite conversion was simulated under steady state. Without control, the ammonia removal was not successful except for two low load periods around 3:00–5:00 a.m. each day (Fig. 5(1) thin solid red line). Nitrite accumulation is not significant except for two peaks just before middle night each day (Fig. 5(1) thick solid line). The DO concentration shown in Fig. 5(3) indicated under-aeration except for two low load periods. The constant KLa simulation indicated that, without proper control, the partial nitrification operation cannot be successful due to variation of influent flow rate and concentration. Fig. 6 shows the partial nitrification process under the constant DO of 0.6 mg/L from day 8 to day 10. Overall, a successful nitrite accumulation was observed (Fig. 6(1), thick solid line). The oxygen transfer coefficient KLa responds to the oxygen demand due to variation of influent flow and load. The DO = 0.6 mg/L was chosen from the steady state analysis in which the highest ammonia to nitrite conversion rate were found at 0.6 mg DO/L (Fig. 3(1)). However the DO = 0.6 mg/L is not an optimal value as can be seen from the two peaks in nitrate concentration from around 3:00– 5:00 a.m. (Fig. 6(1), thick dashed line), suggesting unnecessary aeration during the low load period. There are two high peaks in ammonia concentration around 7:00–9:00 a.m. each day, corresponding to morning peak load. Optimization by maximizing nitrite or minimizing ammonia concentration and KLa

Optimization by maximizing N2O emission Fig. 8 shows the partial nitrification optimized under the objective function of maximizing N2O emission (OFC) using different control horizon (N) and optimization horizon (P) (case 1: long horizon, N = 4, P = 2; case 2: short horizon, N = 1, P = 1). The computation time increased sharply with the increasing of the control and optimization horizon. A significant low nitrite conversion and under-aeration were derived in the short horizon case. The long horizon case produced similar results to the optimization case using OFA and OFB objective functions. Choosing a long horizon allows the optimizer to consider the future requirement of partial nitrification process, instead of only

(2) KLa (d-1)

(1) Ammonia, Nitrite and Nitrate 15

300

10

200

KLa (d-1)

N-NH+4, N-NO-2, N-NO-3 (mg/L)

The partial nitrification optimized by maximizing nitrite concentration (OFA) and minimizing ammonia and KLa (OFB) is

shown in Fig. 7. The optimization results from the OFA and OFB objective function were similar. A slight higher nitrite concentration, KLa and DO concentration were derived from the optimization with OFA than OFB. During the low influent load around 3:00–5:00 a.m. each day, the two nitrate peaks (Fig. 7(1), thick dashed lines) were eliminated in comparison to the constant DO operation (Fig. 6(1), thick dashed line) by lowering the KLa and DO concentration (Fig. 7 (2,4)), which means saving in unnecessary aeration and high nitrite conversion rate. During the morning peak influent loads, the aeration supply increased to cope with the peak loads, therefore decreased the ammonia peak concentration shown in the constant DO operation (Fig. 6(1), thin line). However, the increasing in KLa was limited to reduce the nitrite conversion into nitrate. The KLa supplied was optimized by solving the OFA and OFB objective functions, which results in an intelligent change of DO concentration to maximize the nitrite conversion.

5 0

8

8.5 9 9.5 Time (days )

100 0

10

8

3

15

2

10 5

8

8.5 9 9.5 Time (days )

10

(4) DO

20

mg O2/L

N2O (µg g-1SS h-1)

(3) N2O emiss ion

8.5 9 9.5 Time (days )

10

1 0

8

8.5 9 9.5 Time (days )

10

Fig. 9. Partial nitrification optimized by objective function of (OFD) with hard (A) and soft (B) bypass constraint: (1) A-nitrite (thick grey solid line), B-nitrite (thick black solid line), A -nitrate (thick grey dashed line), B-nitrate (thick black dashed line), A-ammonia concentration (thin red solid line), B-ammonia concentration (thin black dashed line); (2) A-oxygen transfer coefficient KLa (grey line), B-oxygen transfer coefficient KLa (black line); (3) A-N2O emission rate (grey line), B N2O emission rate (black line).

J. Wu et al. / Journal of Environmental Chemical Engineering 3 (2015) 1602–1613

optimized for the current step, although at the expense of computation time. Optimization results with the OFC objective function indicated that the partial nitrification process can be optimally controlled using N2O emission sensors and long horizon model predictive control method. Optimization by maximizing N2O emission with ammonia constraint

NH4 with N2O constraints Due to its greenhouse effect of N2O emission to the environment, a hard bypass constraint of N2O emission less than 10 and 14 mg/(g SS h) was applied to the objective function (OFE). The hard bypass constraint guaranteed the N2O emission rate did not exceed the limits. A low allowed N2O emission rate limit leaded to low partial nitrification efficiency (Fig. 10), as high DO concentration is required for low N2O emission. Under the 10 mg/ (g SS h) limit for N2O emission, high nitrate concentration and low nitrite concentration were derived. Discussion Table 6 shows a comparison of optimization results using different objective functions and constraints. Although the same average aeration of KLa = 110–120 days
1 was supplied to the various cases, the nitrite accumulation efficiency varies significantly depending on the applied optimization strategies. For the constant aeration case, the lowest average nitrite concentration was recorded. Similar nitrite accumulation was observed for system optimization with N2O emission monitoring and routine process monitoring such as nitrite and ammonia sensor. Therefore, the N2O emission monitoring can be used as an alternative for nitrite or ammonia sensor for the process control of partial nitrification process. When the N2O emission monitoring was used in model base optimization, a long horizon method should be applied, in which the computation time increased to around 10 s for optimization of each time step. The control and optimization horizon have no

(2) KLa (d-1)

(1) Ammonia, Nitrite and Nitrate 8

250

6

200 KLa (d-1)

N-NH+4, N-NO2-, N-NO-3 (mg/L)

Due to variation of influent flow rate and concentration, the effluent ammonia concentration could exceed limits if the optimization was solely based on N2O emission rate (OFC). Two ammonia peaks is shown in the Fig. 8(1) (thin lines). In the objective function OFD, the hard and soft bypass ammonia concentration constraints were added to the OFC objective function. A hard bypass constraint of 2 mg N-NH4+/L significantly reduced the efficiency of ammonia conversion to nitrite. The nitrite concentration under hard bypass constraint is much lower than the soft bypass constraint (Fig. 9(1) thick solid lines). The aeration supply (KLa) during the morning peak loads reached to the highest limit of 300 days
1 in order to bring the ammonia concentration below the 2 mg N-NH4+/L limit. Two peaks in nitrate concentration were observed for the hard bypass constraint during morning peak loads (around 8.5 and 9.5 d), suggesting over-aeration. For the soft bypass constraint for ammonia, the effluent ammonia concentration exceed 2 mg NNH4+/L briefly during the morning peak loads. Other than that, the nitrite accumulation and ammonia concentration both shows satisfactory results.

4 2 0

8

8.5 9 9.5 Time (days )

150 100 50

10

8

(3) N2O emiss ion

10

2 1.5

12

mg O2/L

N2O (µg g-1SS h-1)

8.5 9 9.5 Time (days ) (3) DO

14

10 8

1611

1 0.5

8

8.5 9 9.5 Time (days )

10

0

8

8.5 9 9.5 Time (days )

10

Fig. 10. Partial nitrification optimized by objective function of (OFE) with hard bypass constraint of N2O emission less than 10 (A) and 14 (B) mg/(g SS h): (1) A-nitrite (thick grey solid line), B-nitrite (thick black solid line), A-nitrate (thick grey dashed line), B-nitrate (thick black dashed line), A-ammonia concentration (thin red solid line), Bammonia concentration (thin black dashed line); (2) A-oxygen transfer coefficient KLa (grey line), B-oxygen transfer coefficient KLa (black line); (3) A-N2O emission rate (grey line), B N2O emission rate (black line).

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Table 6 Comparison of optimization of different objective functions and constraints. Objective functions

Constant aeration supply Constant DO concentration OFA Maximizing nitrite concentration OFB Minimizing ammonia concentration and aeration supply OFC Maximizing the N2O emission-short horizon Maximizing the N2O emission-long horizon OFD Maximizing N2O emission with hard constraint on ammonia concentration Maximizing N2O emission with soft constraint on ammonia concentration OFE Maximizing nitrite concentration with 10 mg/(g SS h) N2O emission constraint Maximizing nitrite concentration with 14 mg/(g SS h) N2O emission constraint

Average value

Computation time for each step (Second)

N2O emission 10 mg/ NH4+ (g SS h) mg/L

NO2
mg/L

NO3
mg/L

DO mg/L

KLa d
1

5.05 12.96 13.88 13.83 11.38 14.09 12.08

3.13 1.33 1.37 1.54 2.62 1.61 1.78

2.84 5.14 5.51 5.17 2.91 5.26 3.84

2.55 2.25 1.09 0.68 0.15 0.62 0.65

0.94 0.6 0.63 0.55 0.38 0.55 0.53

120 118.73 117.64 115.7 110.46 115.3 116.25

N/A N/A 1.1 1.14 0.48 9.74 1.06

14.19

1.16

6.63

0.01

0.76

121.09

1.12

9.92

1.02

3.41

5.21

0.91

125.19

1.01

13.5

1.29

5.38

1.72

0.67

118.73

0.98

significant impact on the optimization results when using other objective functions (data not shown). To save computation time, N = 1 and P = 1 were used for these objective functions. A perfect ideal model was used in the optimization study. The two-step nitrification ASM1 was developed based on the generally accepted ASM1 model [35]. The N2O emission model was developed and calibrated using experimental data from literatures [28]. In general, the model can represent the major dynamics in partial nitrification process and can be used validly in the optimization study. For the optimization techniques to be used in the control of partial nitrification process in pilot and plant scale, a more rigorous parameter identification work should be carried out and a state observer can be included for the accurate estimation of AOB and NOB biomass concentration and other important variables [49]. The N2O emission rate not only depends on the biological reactions dynamics but also on the N2O solubility and gas stripping efficiency [24]. The maximum specific N2O gas emission rates (mN) were calibrated under relative stable aeration intensities [28]. It is expected their value could change with the aeration intensity. Therefore the actual amount of N2O emitted can change with the aeration intensity [17]. More research on the effect of gas stripping on emission should be carried out. In the piratical application, the maximum specific N2O gas emission rates (mN) can be estimated in real time by solving a cost function that minimizes the sum of least square errors between the model predicted and actually N2O emission over a moving horizon [49]. The application of N2O emission for process optimization is based on the routine variation of influent flow rate and ammonia concentration. Besides the routine variation, the toxic compounds can also leads to the sharp increasing or decreasing of N2O emission [24]. In this circumstance, the N2O emission monitoring is no longer effect for the process optimization. A form of pattern reorganization can be applied to identify the toxic shocking load and take appropriate actions [17]. Conclusions in this study, the optimization strategies of using non-invasive N2O emission monitoring as an alternative to the routine wastewater treatment process analysis were evaluated for the optimization of the partial nitrification process. A two-step nitrification ASM1 model and empirical N2O emission model were used for the process optimization purpose. The following major conclusions can be drawn from the model based optimization study:

 Without optimization, the partial nitrification process cannot operate successfully due to the constant variation of influent flow rate and concentration.  The N2O emission monitoring can be used for the optimization of partial nitrification process in elimination of influent flow rate and concentration disturbances and maximizing nitrite accumulation efficiency.  A similar optimization result can be derived from the N2O emission monitoring and other routine process monitoring such as nitrite and ammonia concentration.

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