Cost-efficient operation of a denitrifying activated sludge process

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41 (2007) 2325 – 2332

Available at www.sciencedirect.com

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

Cost-efficient operation of a denitrifying activated sludge process Pa¨r Samuelssona,, Bjo¨rn Halvarssonb, Bengt Carlssonb a

Department of Electrical Engineering, Dalarna University, SE-781 88 Borla¨nge, Sweden Division of Systems and Control, Department of Information Technology, Uppsala University, P.O. Box 337, SE-751 05 Uppsala, Sweden

b

art i cle info

A B S T R A C T

Article history:

In this paper, the choice of optimal set-points and cost minimizing control strategies for

Received 19 September 2005

the denitrification process in an activated sludge process are treated. In order to compare

Received in revised form

different criterion functions, simulations utilizing the COST/IWA simulation benchmark

18 August 2006

are considered. By means of operational maps the results are visualized. It is found that it is

Accepted 30 October 2006

easy to distinguish set-point areas where the process can be said to be efficiently controlled

Available online 23 April 2007

in an economic sense. The characteristics of these set-point areas depend on the chosen

Keywords:

effluent nitrate set-point as well as the distribution of the different operating costs. It is

Activated sludge process

also discussed how efficient control strategies may be accomplished.

Cost-efficient operation

& 2006 Published by Elsevier Ltd.

Denitrification Operational maps Fee functions

1.

Introduction

In recent years, cost minimization has become increasingly important in the control and operation of wastewater treatment plants. In order to run a plant economically efficient, operational costs such as pumping energy, aeration energy and dosage of different chemicals should be minimized. At the same time, the discharges to the recipient should be kept at a low level. Of course, minimizing the operational costs and at the same time treat the wastewater properly may lead to a conflict of interest that must somehow be solved. The main problem is how to keep the effluent discharges below a certain pre-specified limit to the lowest possible cost, see Olsson and Newell (1999). Part of the answer is to design the control algorithms in such a way that the overall operational costs are minimized. This goal can be attained in different ways. As an example, the controller setpoints could be separately optimized or the cost could be minimized online by some control strategy, for instance model predictive control (MPC). See Qin and Badgwell (2003)

for how MPC can be used in this context. In some countries, the authorities charge according to effluent pollution. A possible way to formulate the on-line minimization criterion in such a case is to use a cost function that takes actual costs (energy and chemicals) into account and at the same time economically penalizes the effluent discharges. Over the years, much effort has been put in developing economically efficient control strategies for operation of wastewater treatment plants. An interesting cost function is presented in Carstensen (1994) where the effluent nitrogen is penalized using a piecewise linear discontinuous function. The papers Yuan et al. (1997), Yuan et al. (2002) and Yuan and Keller (2003) all consider efficient control of the denitrification process. The optimal set-point for the nitrate concentration at the outlet of the anoxic zone is then found to be near 2 mg(N)/ l or, at least, in the interval 1–3 mg(N)/l. In Ingildsen et al. (2002), an optimization of the dissolved oxygen (DO) and nitrate set-points is made. In Galarza et al. (2001) steady-state operational maps are utilized to examine the feasible operating area for two activated sludge processes with emphasis on

Corresponding author. Tel.: +46 23 778949; fax: +46 23 778050.

E-mail addresses: [email protected] (P. Samuelsson), [email protected] (B. Halvarsson), [email protected] (B. Carlsson). 0043-1354/$ - see front matter & 2006 Published by Elsevier Ltd. doi:10.1016/j.watres.2006.10.031

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sensitivity analysis. Fuzzy control strategies was evaluated using multicriteria cost functions in Cadet et al. (2004). In Vanrolleghem and Gillot (2002), different multi-criteria control strategies are evaluated. In this paper, the choice of optimal set-points and cost minimizing control strategies for the denitrification process in an activated sludge process configured for pre-denitrification are evaluated. The manipulated variables (input signals) are the internal recirculation flow rate and the flow rate of an external carbon source. The controlled variables (output signals) are the nitrate concentrations in the last anoxic compartment and the effluent. In order to compare the impact of different criterion functions, stationary simulations utilizing the COST/IWA simulation benchmark, see Copp (2002), are considered. By means of operational maps the results are visualized. It is also discussed how efficient control can be accomplished. The organization of the paper is as follows: In Section 2, the simulation model (the COST/IWA simulation benchmark) is briefly described together with the associated operational costs. In Section 3 simulation results are presented using operational maps. The simulation results are discussed and interpreted in Section 4. Finally, in Section 5 the general conclusions are drawn.

2. The model and the operational cost functions In the simulation study presented in this paper, the COST/ IWA benchmark simulation model number 1 (BSM1) is used, see Jeppson and Pons (2004) for a general survey and Copp (2002) for a more technical description. BSM1 is an important tool for simulation of the activated sludge process in various realistic wastewater treatment scenarios. In BSM1, five biological reactors are implemented using the IWA activated sludge model no. 1 (ASM1), see Henze et al. (1987). Despite being a fairly complex model, ASM1 has some limitations. For example, the pH does not affect the process rates. The pH of the wastewater should hence be nearly neutral. The alkalinity is, however, calculated in ASM1 and may be used to detect pH related problems, see further Henze et al. (1987). The model plant is pre-denitrifying with two anoxic and three aerated compartments. A secondary settler is also implemented. To allow for consistent experiment evaluation, three dynamic data input files are defined, each describing different weather conditions. The purpose of the simulation benchmark is to provide an objective and unbiased tool for performance assessment and evaluation of proposed automatic control strategies. A great benefit of the benchmark is that it allows for comparison of many automatic control strategies given the same conditions. The aim here is to analyse the stationary operational costs of the denitrification process, and in order to visualize the costs, these are presented in stationary operational maps together with the considered output signals. The output signals are the nitrate concentration in the last anoxic compartment, San NO (mg(N)/l), and the nitrate concentration in the effluent, SeNO (mg(N)/l). The available control handles considered in this paper are the internal recirculation flow

rate Q i (m3/day) and the flow rate of an external carbon source, Q car (m3/day). Since only the denitrification process is studied here, aeration is not part of the control handles. To express the cost for controlling the denitrification process, a number of partial costs are taken into account:

 Pumping costs due to the required pumping energy.  Aeration costs due to the required aeration energy (which

 

varies due to the input load). Even though the denitrification process is anoxic, it is important to include the cost for aeration in the total cost. Excessive use of an external carbon source has a large impact on the required aeration energy, and thus on the total cost. External carbon dosage costs. A possible fee for effluent nitrate discharge.

In BSM1, the total average pumping energy over a certain period of time, T, depends directly on the internal recirculation flow rate Q i and is according to Copp (2002) calculated as EP ¼

Z

0:04 T

t0 þT

t0

ðQ r ðtÞ þ Q i ðtÞ þ Q w ðtÞÞ dt

(1)

expressed in units of kWh/day. In (1), Q r denotes the return sludge flow rate and Q w the excess sludge flow rate, both in units of m3 =day. The average energy in kWh/day required to aerate the last three compartments can in turn be written as EA ¼

24 T

Z

t0 þT

t0

5 X ð0:4032KL ai ðtÞ2 þ 7:8408KL ai ðtÞÞ dt,

(2)

i¼3

where KL ai ðtÞ is the oxygen transfer function in the aerated tank number i in units of h1 . Further, assuming a prize kcar (EUR/m3) on the external carbon source and that an external carbon flow rate, Q car ðtÞ (m3/day), is fed into the process during the time interval T, the cost per day of the external carbon flow rate is Ccar ¼

1 T

Z

t0 þT t0

kcar Q car ðtÞ dt

(3)

expressed in EUR/day. Normally, when nitrogen concentrations in the effluent water are economically charged, the fees consider the total nitrogen discharges in the effluent water. In this paper, only the operation of the denitrification process is considered. Therefore, the effluent discharge fees used in this paper only consider nitrate concentrations in effluent water. A reasonable way to describe a fee for the effluent nitrate discharge is to let the fee depend on how large mass of nitrate that is discharged per time unit. This depends, of course, on the effluent flow rate, Q e ðtÞ, and the nitrate concentration in the effluent, SeNO ðtÞ. The nitrate discharge cost may be expressed as (in EUR/day) CNO ¼

1 T

Z

t0 þT t0

f NO ðQ e ðtÞ; SeNO ðtÞÞ dt,

(4)

where f NO is some function describing the fee. Now, assuming a constant energy prize, kE , the total cost expressed (in EUR/ day) can be calculated during a representative time interval T

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from (1)–(4) as Ctot ¼ kE ðEP þ EA Þ þ Ccar þ CNO .

(5)

The fee functions for the discharge of nitrate can have different forms. Normally, the fee functions are set-up by the legislative authorities. Here, three typical fee functions are considered: (1) No charge is added for the disposal of nitrate, i.e. f NO ðQ e ðtÞ; SeNO ðtÞÞ  0. In practice, even though not associated with a direct fee, it is common to have legislative limits on the average effluent nitrate concentration in such a case. (2) Effluent nitrate is charged with a constant cost, say Da per discharged kg. Such a fee function is f NO ðQ e ðtÞ; SeNO ðtÞÞ ¼ DaSeNO ðtÞQ e ðtÞ.

(6)

(3) Effluent nitrate is charged with a constant cost per discharged kg, Da, up to a legislative discharge limit for the effluent concentration, alimit . Above this limit the cost of discharging additional nitrate is Db. Exceeding the limit also imposes an additional charge of b0 per volume effluent water. A mathematical description of this cost function is given in (7). f NO ðQ e ðtÞ; SeNO ðtÞÞ 8 DaSeNO ðtÞQ e ðtÞ > > ¼

< > > :

if SeNO ðtÞpalimit ;

Daalimit Q e ðtÞ þ b0 Q e ðtÞ

ð7Þ

þDbðSeNO ðtÞ  alimit ÞQ e ðtÞ if SeNO ðtÞ4alimit :

In the cost functions above, the effluent flow rate is calculated as Q e ðtÞ ¼ Q in ðtÞ  Q w ðtÞ. The difference between the cost functions presented in Carstensen (1994) and the one in (7) is that here only the nitrate discharge is penalized, while in Carstensen (1994) the nitrate and ammonium concentrations are lumped together and charged.

3.

Simulation results

The considered cost functions for penalizing nitrogen discharge are evaluated utilizing BSM1. In the simulation study, the benchmark WWTP is fed with constant influent flow rate of wastewater, 18 446 m3/day. Basically, the default values of BSM1 are used, with the following exceptions:

 The influent is assumed to have a concentration of readily biodegradable substrate, SS;in , of 60 mg(COD)/l.

 The external carbon source is ethanol with a COD of  

2327

4 1 (200 7) 232 5 – 233 2

1:2  106 mgðCODÞ=l. No limit for the carbon dosage is assumed. The last three compartments are aerated utilizing DO controllers with a fix set-point of 2 mg/l. The DO control is fast compared to the other control loops.

All of the other parameters adopt the standard values used in BSM1, these values can be found in, for instance, Copp (2002) or on the benchmark webpage IWA (August 16, 2006). See Tables 1 and 2 for other values describing the energy prices,

Table 1 – Nominal energy and carbon source prices used in the simulation studies Parameter kE kcar

Value

Unit

0.037 549

EUR/kWh EUR/m3

The energy price, kE , is based on yearly average prices from a major Swedish supplier.

Table 2 – Default parameter values of the nitrate fee functions used in the simulation studies Parameter alimit b0 Da Db

Value

Unit

8.0 1.4 2.7 8.2

mg(N)/l EUR/1000 m3 EUR/kg NO EUR/kg NO

the carbon source prices and fee functions. These values are in the sequel referred to as the nominal case. In each experiment, the benchmark has been run for 150 simulation days for a grid of constant input values. Only the last 100 simulation days were considered when evaluating the cost functions to avoid the influence of transients. In Figs. 1–5 the benchmark has been run for a grid of different values for the external carbon dosage, Q car , and the internal recirculation flow rate, Q i , in order to obtain stationary values of the nitrate concentrations. Q car has been varied in steps of 0:1 m3 =day and Q i in steps of 2500 m3 =day. In Fig. 1 the stationary operational map depicting the total cost is shown for the nominal case in Table 1 when no nitrate discharge fee is used. The level curves for the nitrate concentrations in the anoxic and aerobic compartments, e San NO and SNO , respectively, are plotted. From the figure, an optimal nitrate set-point in the anoxic compartment is easily found for any given effluent nitrate set-point. Approximate locations for these optima are marked by ‘X’ in the figures. If, for instance, an effluent set-point of 13 mg (N)/l is desired it is seen from the figure that the corresponding optimal set-point of nitrate in the anoxic compartment is around 2.5 mg (N)/l and that the corresponding optimal operational cost is around 400 EUR/day. With an effluent nitrate set-point of 7 mg(N)/l, the optimal set-point of nitrate in the anoxic compartment is between 1 and 1.5 mg(N)/l. If an operational map for a wider operational area is studied it is seen more clearly that the value of San NO at the cost optimal point decreases as SeNO decreases. For instance, if SeNO ¼ 2 mgðNÞ=l, San NO near 0.3 mg(N)/l gives the cost optimal operating point. Note from Fig. 1 that the difference in the operational costs between using optimal or non-optimal set-points of nitrate in the anoxic compartment may be significant. In order to illustrate the impact of changes in energy prices on the choice of optimal nitrate set-point in the last anoxic compartment, Fig. 2 shows simulations with the same

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Fig. 1 – Stationary operational map for a grid of different values of Q car and Q i for the case with no nitrate fee. Solid lines show the total cost in EUR, dash-dotted lines show the effluent nitrate concentration, SeNO mgðNÞ=l, and dotted lines show the nitrate concentration in the anoxic compartment, San NO mgðNÞ=l. Approximate locations for the optimal nitrate set-points in the anoxic compartment for given desired effluent nitrate concentrations are marked by ‘X’. Nominal energy and carbon source prices according to Table 1 are used.

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4

7

1

14

2

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[m3/day]

Fig. 2 – Stationary operational map with the same settings as in Fig. 1 except that the energy price is 10 times as high.

settings as in Fig. 1 except that the energy price is ten times as high ðkE ¼ 0:37 EUR=kWhÞ. It is seen that the external carbon dosage now is less dominant in the total cost and that the optimal set-point of nitrate in the anoxic compartment

decreases to below 1 mg(N)/l for effluent nitrate set-points less than 10 mg(N)/l, see Fig. 2. Another interesting case is illustrated in Fig. 3, where the cost for dosing an external carbon source is set to zero. This case is not unrealistic since

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Fig. 4 – Stationary operational map for a grid of different values of Q car and Q i . Solid lines show the total cost including a constant nitrate-charge per kg effluent nitrate, dash-dotted lines show the effluent nitrate concentration, SeNO , and dotted lines show the nitrate concentration in the anoxic compartment, San NO . The star indicates the minimum-cost point. Here Da ¼ 5:5 EUR=kg.

the carbon source may be provided free of charge if, for instance, industrial by-products are available. The principal behaviour of the cost function in this case is the same as if a very high energy price is used. The conclusion that can be drawn from Fig. 3 is that even though provided for free, the

dosage of an external carbon source may have a large impact on the total cost. This is due to the impact of external carbon addition on the aeration costs. The cheaper the price of carbon source, the lower the optimal nitrate set-point in the anoxic compartment becomes.

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Fig. 5 – Stationary operational map for a grid of different values of Q car and Q i . Solid lines show the total cost including a nitrate-charge according to (7), dash-dotted lines show the effluent nitrate concentration, SeNO , and dotted lines show the nitrate concentration in the last anoxic compartment, San NO . The additional charge for exceeding the legislative discharge limit at alimit ¼ 8:0 mgðNÞ=l is here b0 ¼ 1:4 EUR=1000 m3 . The star indicates the minimum-cost point.

Table 3 – Optimal values of San NO , Q i and Q car for a number of given values of SeNO are shown for the nominal case, the cases in Figs. 2 and 3 SeNO (mg(N)/l)

San NO (mg(N)/l)

Qi (m3/d)

Q car (m3/d)

kE ¼ 0:037 kcar ¼ 549

10 7 5 2

1.8 1.3 0.9 0.35

43 000 73 000 110 000 280 000

0.55 1.0 1.4 2.7

kE ¼ 0:37 kcar ¼ 549

10 7

1.0 0.6

35 000 58 000

0.6 1.1

kE ¼ 0:037 kcar ¼ 0

10 7

0.6 0.4

31 000 56 000

0.7 1.2

Case

The energy price kE are in units of EUR/kWh and the price for carbon source in EUR/m3.

Table 3 summarizes optimal set-point values of nitrate in the anoxic compartment for a number of effluent set-point values and operational costs. In the table, the tendencies described above is seen. The first part of the table shows the impact of a decreasing effluent nitrate set-point on the optimal value of the anoxic nitrate set-point, the second part of the table shows the impact of a higher energy price and the third part of the table shows some optimal values when the carbon source is provided without cost. Next, the case with a constant cost per discharged kg effluent nitrate according to (6) is investigated. Fig. 4 shows this case with

a fee of 5.5 EUR/kg effluent nitrate, i.e. Da ¼ 5:5 EUR=kg. The nominal energy and carbon source prices from Table 1 are used. The cost-optimum is now found at Q car ¼ 0:7 m3 =day and Q i ¼ 52 500 m3 =day with a cost of 1620 EUR/day corresponding to e San NO ¼ 1:7 mgðNÞ=l and SNO ¼ 8:8 mgðNÞ=l. The penalization of nitrate discharges creates a well-defined minimum in the total cost function and makes it less desirable to discharge more nitrate with the effluent. Consequently, the importance of the set-point choice for San NO has become larger. The overall optimal nitrate set-point is marked by a star. The effluent nitrate fee certainly has an impact on the costoptimal effluent nitrate set-point. However, since there is no impact of the desired (set-point) effluent nitrate concentration and in the discharge fee, it may be hard to relate the value of Da to the optimal operating point—or operating region—at (or below) a certain effluent nitrate concentration. The third investigated nitrate cost function given by (7) has a discontinuity at a certain predefined concentration of effluent nitrate, alimit . If the jump at the discontinuity, b0 , is sufficiently large it is easy to find the optimal operating point. Also in this case, the nominal energy price is considered. Fig. 5 shows the stationary operational map when using this cost function with parameter values given by Table 2 (nominal energy and carbon source prices in Table 1 were used). The optimum point is located at Q car ¼ 0:825 m3 =day and Q i ¼ 63 000 m3 =day with a total cost of 1227 EUR/day, e San NO ¼ 1:7 mgðNÞ=l and SNO equals 8 mg(N)/l. In practice, when treating non-constant influents, it is of course advisory to choose an operating point that is located slightly below the legislative limit. The region of economic

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efficient operation, say operation with a total cost below 1300 EUR/day, is relatively large. The larger b0, the sharper the limit at alimit becomes. To check the robustness of the results, the sensitivity to changes in model parameters on the location of the cost optimal operational points was investigated. It was found that there were only minor differences between the obtained operational maps, and in fact, for many changes of the model parameters most of the optimum set-points almost coincide. Since the conclusions from the sensitivity analysis were similar to the conclusions in the previous cases, the results are omitted here, see Samuelsson et al. (2005a) for details. Furthermore, in Samuelsson et al. (2005a), the case with time varying influents were also considered in order to study the effects of dynamic influents and different load situations. The time varying influents used were the ones specified by BSM1 to describe different weather conditions. These experiments did not lead to any essential changes in the results and are therefore omitted in this paper.

4.





Discussion

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Given the desired nitrate effluent concentration, SeNO , the optimal set-point of San NO can be found from the operational maps depending on process conditions. For instance, in the nominal case, if the set-point for SeNO is chosen as 8 mg(N)/l, the optimal set-point for San NO is close to 1.5 mg(N)/l. If decentralized control is to be used, Q car should control SeNO and Q i San NO . Other control structures may, however, yield a better performance, see Samuelsson et al. (2005b). Another possibility in the nominal case, see Fig. 1, is to use a constant high internal recirculation flow rate Q i and to use only Q car in order to control the effluent nitrate concentration. Since Q i has a much smaller impact on the total cost than Q car , this would render a close to costoptimal operation. This possibility has also been mentioned by Ingildsen (2002). This is, however, not a suitable strategy if the carbon source is inexpensive, see Fig. 3. To achieve a cost-optimal performance in the nominal case, the total cost could be minimized on-line using quadratic criteria yielding for example LQG or MPC controllers. Such a criterion has the typical form Z T eT ðtÞQ 1 eðtÞ þ uT ðtÞQ 2 uðtÞ dt, (8) V¼ 0

Cost efficient operation of the denitrification process was studied using operational maps. Considering the case when no charge on effluent nitrate is imposed it is seen that, in the nominal case, the cost for dosing an external carbon source dominates the total cost. It is also clear that the operational area with respect to effluent nitrate is divided in two parts with different gain characteristics, and that for each desired value (set-point) of SeNO , there is a cost-optimal point in the operational map corresponding to a certain value of the nitrate concentration in the last anoxic compartment, San NO . This point naturally also corresponds to certain stationary values of the input signals, Q car and Q i . In contrast to what is described in, for instance Yuan and Keller (2003), the findings in this paper are that the cost-optimal set-point of nitrate in the anoxic compartment depends on the choice of effluent nitrate set-point, as well as the specific operational costs. The optimal San NO level decreases with decreasing SeNO levels and with increasing energy costs (or decreasing costs for external carbon). The location of the optimal set-point choice is, however, not very sensitive to variations in the ASM1 parameters. From the results in the previous section, some questions may arise. The first is which control structures that can be expected to give a good performance in terms of disturbance rejection and set-point tracking. In different operating points different control structure selections may be suitable. As indicated by e.g. Fig. 1, in the area of the operational map corresponding to cost-efficient nitrate control, SeNO is mostly affected by the input signal Q car , while San NO is affected by both input signals in this area. This situation in particular is further discussed by Samuelsson et al. (2005b) and also to some extent by Yuan and Keller (2004). The second question is how to design the actual control law in order to minimize operational costs. Below, some possibilities for this control design are discussed:

 One approach is to use two different control loops (for instance PI-controllers) to control SeNO and San NO separately.



where e is a column vector containing the control errors and u the input signals. The weighting matrix Q 2 can be chosen to reflect the costs for the different input signals and Q 1 can be seen as a performance weight. The difficulty with this criterion is how to weight control performance against cost minimization, i.e. how to choose the matrices Q 1 and Q 2 . From the prior knowledge obtained from Fig. 1, in the nominal case, the elements of the matrix Q 2 could be chosen in an intuitive manner. A rule of thumb could be to choose Q 2 as a diagonal matrix with the element corresponding to Q car significantly larger than the element corresponding to Q i , since the external carbon source is much more expensive than the pumping of the internal recirculation flow rate in the nominal case and thereby dominates the total cost. Such a choice clearly penalizes a large value of Q car in the criterion. A simple grid search could be performed on-line until the optimal point is reached. This method is simple and has the advantage that no operational map and thereby no model is required. One such optimization algorithm is presented by Ayesa et al. (1998). This algorithm is employed to minimize a global penalty function combining effluent requirements and costs.

For the case when the nitrate discharge is penalized with a constant charge per kg, see (6), it is seen from Figs. 4 that this creates a minimum in the total cost function (5). The main drawback using such a cost function for automatic control is that it is hard to relate the location of the cost optimal setpoint to the nitrate discharge fee, Da, and thereby hard to say which set-point a certain fee results in. This also depends on the effluent flow rate, Q e . Due to the discontinuity, this problem can be overcome if instead the fee function according to Carstensen (1994) is implemented. The location of the discontinuity of the fee function (7) immediately coincides with the optimal set-point for the effluent nitrate, SeNO , if the discontinuity is sufficiently large. Using this fee in the total

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cost is a convenient way to achieve cost optimality for a certain set-point of effluent nitrate, SeNO , see Fig. 5. Minimizing this total cost function on-line using some automatic control strategy would be a good way to impose the importance of good performance via penalizing the effluent discharge into the control design. If the discharge of nitrogen over a certain legislative limit is directly associated with a higher fee, this could clearly motivate the use of more advanced control strategies. The impact of the fee in the control design is also easy to understand even for people with a limited knowledge in automatic control, compared to the related matter of choosing a performance weight in some quadratic criterion.

5.

Conclusions

In this paper a bioreactor model describing a pre-denitrifying wastewater treatment plant is studied from a process economic point of view. The impact of different nitrate cost functions on the location of the cost-optimal operating point were examined. Given the desired value of the effluent nitrate concentration, the energy price and the price of carbon source, there is a corresponding optimal set-point for the nitrate in the anoxic compartment. For low effluent setpoints, the optimal anoxic set-point may be located well below 1 mg(N)/l. The simulations also show that the difference in the operational costs between an optimal and nonoptimal anoxic set-point may be large. The locations of the optimal set-point values are, however, not very sensitive to changes in the ASM1 parameters. In summary, it can be concluded that the approach presented in this paper may give a valuable tool towards running a WWTP in a more cost effective way. Natural extensions and topics for further research include:

 Evaluation of the suggested approach using live data from a full-scale WWTP.

 Extending the approach to also consider the nitrification



process and then also consider ammonia discharges. The natural control signal is then the air flow rate. Extending the criteria function with costs for the sludge handling. This will indeed penalize an excessive carbon dosage. Another interesting extension is to include chemical precipitation for phosphorous removal. In a pre-denitrifying plant, there is an interesting trade-off between removal of phosphorous and substrate where the latter is useful for the denitrification process.

Acknowledgements This work has been financially supported by the EC 6th Framework programme as a Specific Targeted Research or Innovation Project (HIPCON, Contract Number NMP2-CT2003-505467). Please note that the European Community is not liable for any use that may be made of the information contained herein. We are grateful to Dr. Ulf Jeppsson for letting us use his Simulink implementation of BSM1.

R E F E R E N C E S

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