Applying Control Actions for the Water Line and Sludge Line to

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Applying Control Actions for the Water Line and Sludge Line to Increase the Wastewater TreatmentIndustrial Plants Performances & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown


Marian Barbu, Ignacio Santín, and Ramon Vilanova

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown


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Bypass

Industrial & Engineering Chemistry Research

Influent wastewater

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Primary clarifier

Secondary clarifier

Activated sludge reactors

Water line

Internal recirculation External recirculation

Thickener

Gas

Anaerobic digester

ACS Paragon Plus Environment Storage tank

Effluent water

Sludge line

Dewatering

Sludge removal

SO,4setpoint SNH,5setpoint Industrial & Engineering Chemistry Research

1 2 3 4 Wastewater 5 6 7 8 9 10 11 12 13 14

KLa3,4

PI SO,4

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PI Effluent water

SNH,5 Unit 1

Unit 2

Unit 3

Unit 4

Unit 5 Secondary clarifier

Qcarb,1

SNO,2 PI Qa ACS Paragon Plus Environment SNO,2setpoint

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ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29


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Improving treatment plant performances Structuring wastewater treatment plant Automatic control Industrial & Engineering Chemistry Research Page 8 of 34 Influent 1 2 3 Sludge 4 and 5 water 6 recycle 7 8 9 10 11 12

Effluent Water line Underflow from secondary clarifier Sludge line Sludge removal ACS Paragon Plus Environment

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Applying Control Actions for the Water Line and Sludge Line to Increase the Wastewater Treatment Plants Performances Marian Barbu *,§, Ignacio Santin ǂ and Ramon Vilanova ǂ §

Department of Automatic Control and Electrical Engineering; "Dunarea de Jos" University of Galati; 800008 Galati, Romania; [email protected] ǂ

Department of Telecommunication and Systems Engineering; Universitat Autonoma de

Barcelona; 08193 Bellaterra, Spain; [email protected]; [email protected]

KEYWORDS: Control strategies evaluation, Wastewater treatment plant operation, BSM2.

ABSTRACT. The use of control engineering in the case of wastewater treatment processes can be an effective measure in the improvement of their performances. This paper presents the implementation and evaluation of control solutions for wastewater treatment plants taking into consideration the hypothesis that the plant can be structured as having two operating lines: a water line and a sludge line. For the water line there are proposed four control strategies and for the sludge line there are considered four control actions. After an initial evaluation of the control strategies used for the water line by comparison with a default closed loop strategy, the paper


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shows the influence of each control action of the sludge line over the considered evaluation indicators. The evaluation is presented for all the water line control strategies, trying to check the generality of the initial hypothesis. The results are validated using the Benchmark Simulation Model No. 2. The simulation results showed that the control strategies implemented for the water line act mainly on the global integral indicators and the control actions implemented for the sludge line act mainly on the acute effects.

1. Introduction Water is a key resource in sustaining the human existence. The continuous growth of the population and of the industrial activities threatens to affect this resource unless active measures are taken. One of these measures is to limit the impact of the human activities on water by treating the resulted wastes from the performing plants before their release into the receiving waters (rivers, lakes etc.). The importance of this topic is also indicated by the large number of publications, starting from the 1990’s

1, 2

and to the present day

3, 4, 5, 6

, that come from the

scientific community devoted to the wastewater treatment technologies. An efficient solution to improve the efficiency of wastewater treatment plants is to adopt the automatic control methods

7, 8, 9

. Their adoption for these systems is slow in the case of

wastewater treatment plants, the main reason being on one hand the fact that they are extremely complex processes, the lack or the high cost of the measurement equipment and on the other hand the significant reticence of the industry to allow the testing of the control solutions on real plants, given the potential environmental risks 10. Nevertheless, a few cases of plant automation, made possible by new monitoring instrumentation 11, have been recently cited in the literature 12 and more are being gradually implemented in full-size facilities. An alternative solution is to use


2

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realistic models that enable the evaluation of control strategies in conditions as close as possible to the real ones 13, 14. Software packages like SIMBA 15, BIOWIN 16, WEST 17 allow not only to model wastewater treatment plants but also to test on them different control strategies. Another proposed solution is to build some benchmark models which allow different users to test their control structures and algorithms on the same platform. A first model is the Benchmark Simulation Model No. 1 (BSM1) 18 which includes only water processing units and defines three scenarios for the influent, based on the rain conditions that may arise. A further development is the Benchmark Simulation Model No. 2 (BSM2)

19

that includes both water and sludge

processing units. In this model only one influent is defined, the performance evaluation of the system being done over a period of one year. In literature there are many comparative studies on control strategies that can be applied both to BSM1 and BSM2 20, 21, 22 but also on the use of the framework provided by the benchmark models for studying the performance of urban real plants 23, 24

. Although all these studies present good results in terms of improving the performances of

the treatment plant, a drawback is that they focus mainly on the manipulation of the variables corresponding to the water treatment units, the variables corresponding to the sludge treatment not being used to their full potential. A typical urban wastewater treatment plant can be structured as having two distinct operating lines: a water line and a sludge line, the two operating lines being interdependent, but, at the same time, influencing simultaneously the overall functioning of the plant. This paper continues our previous work 25 where this structure was introduced as a distinctive feature by defining four control strategies for the water line and four control actions for the sludge line. The goal is to show that the problem of designing the control structure can be treated as independent for these operating lines, although the evaluation regards the indicators for the entire treatment plant. In


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this way it is possible to improve the results obtained by means of the water line defined control strategies by appropriately adding, otherwise usually left aside, the operational complement from the sludge line. Also, the paper shows that the control strategies implemented for the water line act mainly on the global integral indicators, such as effluent quality index and operational cost index, and the control actions implemented for the sludge line act mainly on the acute effects, expressed through the percentage of time when the pollutants in the effluent are violating certain imposed quality limits.

2. Materials and Methods 2.1. Benchmark Simulation Model No. 2

BSM2 is a simulation environment which allows the evaluation of a typical urban wastewater treatment plant. For that, it includes all the necessary elements: plant layout, simulation model, influent data, test procedures and evaluation criteria 19. The plant layout includes the water line, consisting in the units used for the wastewater biological treatment, and the sludge treatment, consisting in the units used for processing the resulted sludge. Figure 1 presents the plant layout considered in BSM2, the two operating lines being underlined in the figure. As it can be seen in Figure 1, these operating lines are strongly interconnected and the evolution of each one influences the evolution of the other and thus, it influences the overall behavior of the treatment plant. The units included in the water line are: a primary clarifier, an activated sludge biological reactor and a secondary clarifier. The central unit of the water line is the activated sludge biological reactor. It contains two anoxic tanks, with a total volume of 3000 m3, and three


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aerobic tanks, with a total volume of 9000 m3. The anoxic tanks are used for denitrification and the aerobic tanks for nitrification, thus, achieving the biological nitrogen removal. This configuration implies the use of an internal recirculation for feeding the anoxic tanks with the nitrate resulted from the aerobic tanks. The units included in the sludge line are: a thickener, an anaerobic digester, a dewatering unit and a storage tank. The central unit of the sludge line is the anaerobic digester in which it takes place the fermentation of the solids wasted from the primary clarifier and the thickened sludge from the secondary clarifier. The methane production resulted from the anaerobic digestion is used to produce energy for the treatment plant. BSM2 uses the Activated Sludge Model no. 1 (ASM1)

26

to model the biological processes

that take place in the activated sludge biological reactors and for the secondary clarifier it is used the model described in 27 taking into consideration a 10 layers non-reactive unit. The Anaerobic Digestion Model (ADM1) 28 is used to describe the five biochemical steps that take place in the anaerobic digester. For BSM2 simulation it is provided an influent defined for 609 days which takes into account the rainfall effect and the seasonal temperature variations over the year. The first part of this influent data (245 days) is used to stabilize the plant under dynamic conditions and the last part (364 days) is used to assess the plant performance. The BSM2 evaluation considers two types of indexes which count the cumulative effects and the acute effects, respectively 19. The first type of performance evaluation is an integral one and it considers two terms: the Effluent Quality Index (EQI) related to the pollutants discharged into the receiving waters and the Operational Cost Index (OCI) related to the economic cost for


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operating the plant. The second type of performance evaluation regards the percentage of time when the pollutants in the effluent violate certain imposed quality limits. EQI is computed using the following equation: t = 609days

EQI =

(

)

1 ( 2·TSSe (t) + CODe (t) + 30·SNKj,e (t) +10·SNO,e (t) + 2·BODe (t))·Qe (t) dt tobs ·1000 t =245∫days

(1)

where Qe is the effluent flow rate, TSSe is the total suspended solids in effluent, CODe is the chemical oxygen demand in the effluent, SNKj,e is the Kjeldahl nitrogen concentration in the effluent, SNO,e is the nitrate concentration in the effluent and BODe is the biochemical oxygen demand in the effluent. OCI counts for the following terms: OCI = AE + PE + 3 SP + 3 EC + ME − 6 MET prod + HE net

(2)

where AE is the aeration energy, PE is the pumping energy, SP is the sludge production to be disposed, EC is the consumption of external carbon source, ME is the mixing energy (as the anoxic tanks are not aerated, in order to avoid settling, they are fully mixed), METprod is the methane production and HEnet is the heating energy. The quality limits considered in the BSM2 to evaluate the acute effects are the following: Ntot < 18 gN m3, CODe < 100 gCOD m3, SNH,e < 4 gN m3, TSSe < 30 gSS m3 and BODe < 10 gBOD5 m3. In these limits, Ntot stands for the total nitrogen which is calculated as the sum of the nitrate and Kjeldahl nitrogen concentrations and SNH,e is the ammonium concentration in the effluent.

2.2. Water Line Control

BSM2 is a simulation tool used to evaluate the performances of the different control strategies applied on the plant. It comes with a default control strategy which is used as a reference for


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studying the control strategies proposed by users. This default closed-loop control strategy, denoted in this study WL-S0, consists in controlling the dissolved oxygen concentration from the fourth tank, SO,4, by manipulating the oxygen transfer coefficient from the same tank, KLa,4 (denoted in this study as WL-A1 control action). The WL-S0 strategy also includes: the oxygen transfer coefficients from the third and fifth tanks that are KLa,3= KLa,4 and KLa,5= KLa,4/2; the addition of external carbon in the first tank at a fixed flow of 2 m3/day; the wastage flow rate varies depending on the winter or summer regime: 350 m3/day from day 0 to day 181 and from day 364 to day 545, and 400 m3/day from day 182 to day 363 and from day 546 to day 608. In addition to WL-S0, we consider other four control strategies for the water line. These strategies aim to control the concentration of the main measured variables from a wastewater treatment plant: ammonium and nitrate, using different manipulating variables available to the user. The strategies are based on the control actions implemented and presented in Table 1 and defined as follows: •

WL-S1 - contains the actions: WL-A2, the control of the ammonium concentration in the last aerobic tank, SNH,5, by manipulating the set point of the default oxygen controller, SO,4setpoint, and WL-A4, the control of the nitrate concentration in the last anoxic tank, SNO,2, by manipulating the external carbon addition in the first tank, Qcarb,1. We mention that the action WL-A2 also implies the use of WL-A1 as an inner loop in the cascade control solution;

•

WL-S2 - contains the actions: WL-A2 and WL-A5, the control of the nitrate concentration in the last anoxic tank, SNO,2, by manipulating the internal recirculation flow rate from the fifth tank to the first tank, Qa;


7


•

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WL-S3 - contains the actions: WL-A1, WL-A4 and WL-A3, the control of the ammonium concentration in the last aerobic tank, SNH,5, by manipulating the oxygen transfer coefficients from the third tank, KLa,3. In this case we also have KLa,5= KLa,4/2;

•

WL-S4 - contains the actions: WL-A2, WL-A5 and WL-A6, the control of the nitrate concentration in the last aerobic tank, SNO,5, by manipulating the external carbon addition in the first tank, Qcarb,1.

The proposed controllers are tuned using trial and error procedure considering both the loop output response, with direct implications on the plant quality variables, and the command effort, with direct implications on the operational cost of the plant. In Figure 2, there is depicted only the implementation of the strategy WL-S3, as being the most complex of all strategies. The rest of the control strategies are implemented in a similar manner.

Table 1. Description of the considered control actions for the water line. Label Measured variable Set point Manipulated variable Control type

WL-A1 SO,4

WL-A2 SNH,5

WL-A3 SNH,5

WL-A4 SNO,2

WL-A5 SNO,2

WL-A6 SNO,5

2 g/m3 KLa,4

1 g/m3 SO,4setpoint

1 g/m3 KLa,5

1 g/m3 Qcarb,1

1 g/m3 Qa

8 g/m3 Qcarb,1

PI

Cascade PI

PI

PI

PI

PI

2.3. Sludge Line Control

In the case of the sludge line the following control actions were proposed and implemented: •

SL-A1: the control of the total suspended solids in the last aerobic tank, TSS5, by manipulating the wastage flow rate, Qw;


8

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Following the implementation of this controller, it was performed an analysis of the behavior of this controller. This preliminary analysis concerned the adequacy of the control action to the variations of the influent flow rate, Qin, especially in case of significant rainfall events. It resulted that for very high values of Qin we cannot achieve the set point imposed for TSS5, although the manipulated variable Qw is equal to zero (see Figure 3). In the same figure one can notice that due to the controller dynamics, imposed by the slow dynamics of the process, when Qin exceeds a threshold value, the presence of the controller affects the obtained performances. This unsatisfactory behavior is due to the fact that the manipulated variable, Qw, still arrives at the zero value with an unwanted delay imposed by the controller dynamics. Taking into account the general good behavior of this controller, the SL-A1 control action is kept, but with a correction for the high influent regime. •

SL-A2: consists in the SL-A1 control actions corrected for the high influent regime with the following control logic: IF Qin > 33000 m3/day THEN Qw=0 ELSE apply Qw resulted from SL-A1;

•

SL-A3: as it was mentioned before in the case of the high influent regime the control actions based on the manipulation of the wastage flow rate, Qw, is not sufficient to ensure a proper sludge concentration in the activated sludge reactors. This usually causes problems regarding the violation of the imposed limits for the ammonium and the total nitrogen concentrations. Another control variable that can be used to control TSS5 is the external recycle flow rate, Qr. While Qw acts on the total amount of sludge, Qr changes the distribution of the sludge between the activated sludge reactors and the


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secondary clarifier. The advantage is that the manipulation of Qr has faster effects on SNtot,e and SNH,e than the manipulation of Qw

29

. At the same time, using Qr as a

command variable for the TSS5 control has some constraints regarding the operation of the secondary clarifier 30. Subsequently, in this control action, we will use Qr to act on the sludge concentration in the reactors for the high influent regime with the following control logic: IF Qin > 33000 m3/day THEN Qr = 41296 m3/day ELSE Qr = 20648 m3/day; •

SL-A4: the control of the storage tank volume by manipulating the tank output flow rate, Qstorage

31

. This control action implies the storage of the return flows during the

day and their release during the night, when the treatment plant influent has a lower load. Details on the control actions designed for the sludge line are given in Table 2. Table 2. Description of the considered control actions for the sludge line. Label SL-A1 Measured TSS5 variable Set point / 4000 critical g/m3 value

SL-A2 TSS5 and Qin

SL-A3 Qin

SL-A4 Time

IF Qin>33000 m3/day THEN Qw=0 ELSE TSS5,setpoint=4000 g/m3

IF 12am≤time33000 m3/day THEN Qr=41296 m3/day ELSE Qr=20648 m3/day Qr

Qstorage

PI and ON/OFF

ON/OFF

ON/OFF


10

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3. Results and Discussion The results are obtained by simulation using the BSM2 protocol. First of all, there were implemented the control strategies for the water line (WL-S0 to WL-S4) presented above. Table 3 contains the results in the case of the water line control strategies. Based on these results, we can compare the proposed control strategies with the default control loop strategy included in the BSM2 environment. For each of the proposed water line control strategies (WL-S1 to WL-S4), there were also implemented the control actions for the sludge lines (SL-A1 to SL-A4 and, finally, all the actions simultaneously). In this way we can evaluate the influence of each sludge line control action in a different context of the wastewater treatment plant, a context imposed by the water line control strategy used for that plant. The obtained results are shown in the Tables 4 to 7. In what follows, we present and explain the obtained results in detail.

3.1. Water Line Control

In the case of WL-S1, by introducing the SNH,5 and SNO,2 control we managed to improve all the considered BSM2 indicators. This is mainly due to the fact that the ammonium concentration is now controlled, at a set point of 1 gN/m3, by comparison with the default case when its evolution was free, being mainly influenced by the dissolved oxygen concentration in the aerobic tanks. Although this has a negative effect, the ammonium concentration in the effluent, 0.47429 g N/m3 for WL-S0 and 1.0568 g N/m3 for WL-S1, the important positive effect is on the nitrate concentration in the effluent which is reduced from 11.0455 g N/m3 in WL-S0 to 6.7151 g N/m3 in WL-S1. This loop actions in two ways: a smaller quantity of the ammonium is converted into nitrate and thus the nitrate brought into the anoxic tanks by the internal recirculation will have a


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lower concentration; a reduced aeration results in the aerobic tanks, with impact on the intensification of the denitrification process in that the internal recycled water has a lower dissolved oxygen concentration. The reduction of the aeration has also a positive effect on the OCI, the aeration energy cost index being reduced from 4225.4 for WL-S0 to 3648.5 for WL-S1. The evolution of the OCI is also influenced by the SNO,2 loop, the carbon source dosage cost index decreasing from 2400.0 for WL-S0 to 2193.4 for WL-S1. At the same time, the ammonium and the nitrate loops also influence the indicators regarding the percentage of time, when the pollutants in the effluent violate the quality limits. This is done by controlling the ammonium and nitrate peaks, which results in a lower value for the indicators TV_ SNH,e and TV_Ntot. Due to the fact that the WL-S1 and WL-S2 have a similar structure, the only difference being the way the SNO,2 loop is implemented, the effect on the considered BSM2 indicators is similar: EQI, OCI, TV_ SNH,e and TV_Ntot being improved with respect to the default control strategy. The explanations presented above are still valid with the remark that now OCI in addition to the decrease of aeration energy cost index (decreases now from 4225.4 for WL-S0 to 3672.7 for WL-S2) is also influenced by the increase in the pumping energy cost index from 445.4 for WLS0 to 492.0 for WL-S2. This is due to the cost associated with the pump used to ensure the internal recycled flow rate. WL-S3 proposes a different control structure regarding the SNH,5 loop with respect to WL-S1 and WL-S2. In this case, SNH,5 is controlled directly by manipulating KLa,5, the dissolved oxygen in the third and fourth tanks being controlled as in WL-S0. This results in an improvement of the EQI, TV_ SNH,e and TV_Ntot when compared with WL-S0 for reasons that are already presented in the case of WL-S1. An interesting aspect is that OCI is approximately the same with the one in


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WL-S0. This is due to a compensation between the decrease in the aeration energy cost index (from 4225.4 in WL-S0 to 3989.4 in WL-S3) and the increase in mixing energy cost index (from 768.0 in WL-S0 to 970.0 in WL-S3). The increase in the mixing energy cost index is caused by the drop in the aeration in the last tank when the ammonium concentration in the influent has low values, the aeration in the third and fourth tank ensuring an ammonium concentration under the imposed set point. WL-S4 is obtained in an incremental way by adding the SNO,5 loop to the WL-S2 strategy. As it can be seen in Table 3, the main positive effect is on the TV_Ntot and OCI. TV_Ntot decreases due to the better control on the nitrate peaks resulted from the direct control of the nitrate concentration at the output of the treatment units. OCI is mainly influenced by the decrease on the carbon source dosage cost index from 2400 in WL-S2 to 1612.0 in WL-S4. This decrease results from the fact that in the case of WL-S2 the nitrate concentration on the effluent is 6.576 g N/m3, so the introduced loop for SNO,5 from WL-S4 has a significant influence only in the case of the influent peaks which cause violation of the imposed quality limits.

Table 3. BSM2 simulation results for water line strategies Criteria EQI OCI TV_Ntot TV_ SNH,e

WL-S0 5576.7 9450.0 1.177 0.409

WL-S1 5071.5 8653.9 0.366 0.240

WL-S2 5055.9 8947.3 0.607 0.335

WL-S3 5104.0 9451.2 0.252 0.338

WL-S4 4993.1 8446.3 0.149 0.352

3.2. Sludge Line Control

We will first analyze the proposed control actions for the sludge line considering the water line control strategy WL-S1. The obtained results are presented in Table 4 and we assess how these


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sludge line control action enhance the water line control strategy. Subsequently, we attempt to understand if the conclusions of this evaluation are the same in the case of the other water line control strategies, thus offering a degree of generality to our findings. In WL-S1 the wastage flow rate, Qw, varies depending on the summer/winter season. Adding the SL-A1 control actions means that Qw is now used as a manipulated variable to control the total suspended solids in the last aerobic tank, TSS5. As it can be seen in Table 4, the obtained results show an improvement on all the considered BSM2 indicators. The EQI improvement (0.65% with respect to WL-S1) is the result of the decrease of the SNO,e from 6.7151 g N/m3 in WL-S1 to 5.9497 g N/m3 in WL-S1+SL-A1, although TSSe is increased from 15.1823 g SS/m3 in WL-S1 to 16.5071 g SS/m3 in WL-S1+SL-A1. This is due to the fact that using this control action we ensure the anoxic tanks with a properly sludge concentration for the entire year, an extremely important aspect especially during the cold season. This is also positively reflected on OCI (8.82% improvement with respect to WL-S1), mainly due to the carbon source dosage cost index which decreases from 2193.4 in WL-S1 to 1551.7 in WL-S1+SL-A1. OCI is also affected negatively by the net energy production from methane (this index decreases from 6507.9 in WLS1 to 6248.6 in WL-S1+SL-A1), but this effect is compensated by the improvement on the sludge production cost (this index decreases from 8093.6 in WL-S1 to 7751.2in WL-S1+SL-A1). The improvement on the denitrification conditions in the anoxic tanks results also in a decrease of the TV_Ntot with 61.75% with respect to WL-S1. Finally, TV_ SNH,e has a slight improvement with 2.5% with respect to WL-S1. Adding the SL-A2 control action to WL-S1 maintains the results stated above on water quality, EQI, and operating costs, OCI. As it was built, SL-A2 acts in the case of the influent peaks, by improving the results of the control regarding the TSS5 concentration so that it influences the


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percentage of time when the pollutants in the effluent violate the quality limits. Thus, the obtained results compared with the ones from the WL-S1 show an important improvement both on TV_Ntot (a decrease with 72.68% with respect to WL-S1) and TV_ SNH,e (a decrease with 33.33% with respect to WL-S1). The same effect can be seen in Figure 4, where dynamic simulation results for total nitrogen and ammonia concentrations in the effluent are presented. SL-A3 control action implies the use of the Qr when there is a peak of the influent flow rate. Qr acts on the amount of sludge brought to the biological treatment units by the external recirculation. Using SL-A3 control we try to take advantage of the fast impact of this action on the ammonium and the total nitrogen concentration and thus to minimize the violation time for the considered pollutants. Due to the fact that this control action is used for short time periods the influence over EQI and OCI is minimal. It is noticeable only the decrease of the carbon source dosage cost index from 2193.4 in WL-S1 to 2077.1 in WL-S1+SL-A3. As designed, the Qr increase during the influent peaks determines a higher quantity of recycled sludge, resulting thus in an improvement of the biological processes that take place and, consequently, in a considerable improvement of TV_Ntot and TV_ SNH,e. In Table 4 it can be seen that TV_Ntot is reduced with 77.32% with respect to WL-S1 and TV_ SNH,e is reduced with 55.83% with respect to WL-S1 (see also the dynamic results from Figure 5). The SL-A4 control action uses the storage tank capacity to avoid the overlap, during the day, of the high load influent and of the nitrogen-rich return flows from the sludge line. This control action has practically no influence over OCI and a limited one over EQI, expressed mainly by a decrease of Ntot,e from 9.820 g N/m3 in WL-S1 to 9.681 g N/m3 in WL-S1+SL-A4. As in the previous case, SL-A4 control action was chosen mainly due its important influence over the violation time for the considered pollutants. This can be seen also in Table 4 where TV_Ntot is


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reduced with 73.50% with respect to WL-S1 and TV_ SNH,e is reduced with 87.92% with respect to WL-S1 (see also the dynamic results from Figure 6). Finally, a simulation considering the WL-S1 and the SL-A2, SL-A3 and SL-A4 control actions was performed. The results show improvements on all the considered BSM2 indicators. We want to especially emphasize the major impact on the TV_Ntot and TV_ SNH,e which are reduced with respect to Wl-S1 with 91.26% and 98.75%, respectively. The effect on the EQI is limited because the decrease of Ntot,e is compensated by an increase of the TSSe, and the improvement over OCI is determined mainly by the decrease of the carbon source dosage cost index from 2193.4, in WL-S1, to 1536.4, when all the sludge control actions are used. This shows that the use of the external carbon to regulate the quality variables is partially replaced by the manipulation of the sludge in the entire plant. The results in Tables 5, 6 and 7 show that by using the sludge line control actions we can improve all the considered water line control strategies, although these strategies are different as structure and quality and manipulation variables. Of course, there is a variability regarding the impact magnitude of these sludge line control actions over the considered BSM2 indicators. For example, when an external carbon addition with fixed flow rate is used, as in the case of WL-S2, practically, the sludge line control actions have no influence over the OCI (see the data in Table 4). Also, the impact on EQI is limited mainly due to the fact that the decrease of Ntot,e is compensated by an increase of the TSSe. These variables were already improved in a conclusive manner by the water line control strategies (see Table 3). As it was already mentioned in the case of the sludge line control actions, we mainly want to improve the percentage of time for when the pollutants in the effluent violate the quality limits. The obtained results show that the efficiency of this approach is very high, the violation time being drastically reduced (e.g. see


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Table 6 where TV_Ntot is reduced with 96.43% with respect to WL-S3 and TV_ SNH,e is reduced with 97.34% with respect to WL-S3). We want also to remark that the performances of the sludge line control actions are calculated with respect to the water line control strategy, not to the default closed loop strategy (e.g. for WL-S3 and all the sludge line control actions TV_Ntot is reduced with 99.24% with respect to WL-S0 and TV_ SNH,e is reduced with 97.80% with respect to WL-S0). The same effect can be seen in Figure 7, where dynamic simulation results for total nitrogen and ammonia concentrations in the effluent are presented.

Table 4. BSM2 simulation results for WL-S1 and sludge line control actions. WL-S1

WL-S1 + SL-A1

WL-S1 + SL-A2

WL-S1 + SL-A3

WL-S1 + SL-A4

5071.5 8653.9 0.366 0.240

5038.4 7890.2 0.140 0.235

5038.6 7839.0 0.100 0.160

5045.8 8504.2 0.083 0.106

5021.1 8635.7 0.097 0.029

Criteria EQI OCI TV_Ntot TV_ SNH,e

WL-S1 + SLA2 + SL-A3+ SL-A4 4962.2 7801.7 0.032 0.003


WL-S2 + SL-A1

WL-S2 + SL-A2

WL-S2 + SL-A3

WL-S2 + SL-A4

5055.9 8947.3 0.607 0.335

4983.8 8984.8 0.315 0.192

4979.2 8978.0 0.223 0.155

5015.1 8941.0 0.229 0.123

4991.4 8863.4 0.910 0.043


WL-S2 + SLA2 + SL-A3+ SL-A4 4885.6 8927.0 0.343 0.003


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WL-S3 + SL-A1

WL-S3 + SL-A2

WL-S3 + SL-A3

WL-S3 + SL-A4

5104.0 9451.2 0.252 0.338

5100.4 8792.3 0.069 0.312

5100.3 8730.4 0.054 0.229

5074.7 9296.9 0.014 0.175

5043.4 9414.1 0.026 0.054


WL-S3 + SLA2 + SL-A3+ SL-A4 5016.4 8707.8 0.009 0.009


WL-S4 + SL-A1

WL-S4 + SL-A2

WL-S4 + SL-A3

WL-S4 + SL-A4

4993.1 8446.3 0.149 0.352

4948.0 7861.9 0.052 0.332

4947.4 7818.3 0.049 0.252

4979.6 8271.4 0.049 0.117

4991.1 8413.5 0.040 0.054


WL-S4 + SLA2 + SL-A3+ SL-A4 4915.3 7747.2 0.046 0.003

4. Conclusions An urban wastewater treatment plant has two operating lines: the water line and the sludge line. Although these lines are strongly interconnected and their evolution influences the overall behavior of the treatment plant, we can implement independent control structures for each of the operating lines. This is important especially for the sludge line where the implementation of the control actions is usually overlooked. Based on the simulation results, in the case of the water line control we can assess the different strategies for the control of the ammonium and nitrate concentration in the system. When SNH,5 is controlled directly by manipulating KLa,5 it leads to weaker results regarding OCI and TV_SNH,e by comparision with the case when SNH,5 is controlled by manipulating the set point of the default


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oxygen controller, SO,4setpoint. The increase of OCI is the result of an increase in the mixing energy cost index which is caused by the drop in the aeration in the last tank when the ammonium concentration in the influent has low values, the aeration in the third and fourth tank ensuring an ammonium concentration under the imposed set point. But when the ammonium concentration in the influent has high values, acting only on the aeration from the fifth tank, as in WL-S3, it leads to an increase of TV_SNH,e when compared with the case when the control is acting in the last three tanks, as in WL-S1. The addition of the SNO,5 loop leads to very good results, especially on TV_Ntot. This is due to the better control on the nitrate peaks resulted from the direct control of the nitrate concentration at the output of the treatment units. In the case of the control of the total suspended solids in the last aerobic tank, TSS5, by manipulating the wastage flow rate, Qw, the main positive effect is on OCI due to the carbon source dosage cost index decrease. The modified version of this control action (SL-A2) acts in the case of the influent peaks, by improving the results of the control regarding the TSS5 concentration so that it influences the percentage of time when the pollutants in the effluent violate the quality limits. The use of the Qr when there is a peak of the influent flow rate, as in SL-A3, determines a higher quantity of recycled sludge brought to the biological treatment units, which results in an improvement of the biological processes that take place and, consequently, in a considerable improvement of TV_Ntot and TV_SNH,e. The use of the storage tank capacity, as in SL-A4, allows to avoid the overlap, during the day, of the high load influent and of the nitrogenrich return flows from the sludge line, thus allowing the further improvement of TV_Ntot and TV_SNH,e. The proposed control solution from this paper, as any other solution obtained in the BSM framework, will need to take into consideration some practical issues before implementation on


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real plants. These issues are mainly related to the wastewater treatment plant structure and parameters, measurement equipment availability and, especially, ideal behavior consideration. The plant considered in the BSM2 has a typical layout, so this may imply the need to include new treatment units or to exclude some of the one included in the benchmark. Also, the model, and consequently the controller parameters, should be updated based on the parameters of the real plant. Although all the proposed control strategies are considering only variables for which commercial sensors are available, we still must consider that some of these measurement equipment is still expensive, therefore not all the treatment plants are equipped with them. This can limit the applicability of some of the proposed control actions. The main challenge is the fact that the real plants are affected by numerous unpredicted events related to the equipment running and to the biological processes. We consider that the adopted solution based on PI and ON/OFF controllers is suitable for practical implementation and, at the same time, can offer some robustness properties to cope with these parametric uncertainties.

AUTHOR INFORMATION Corresponding Author * Marian Barbu, e-mail: [email protected]. Author Contributions The authors contributed equally to this paper. Funding Sources This work was supported by the Spanish CICYT program under grant DPI2016-77271-R.


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ACKNOWLEDGMENT Dr. Ulf Jeppsson, from the Industrial Electrical Engineering division, at the Electrical Engineering Faculty of Lund University, Sweden, is gratefully acknowledged for providing the BSM2 Matlab/Simulink code.


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Supporting Information. Figure S1. Evolution of the main performance indicators with respect to the WL-S1. Figure S2. Evolution of the main performance indicators with respect to the WLS2. Figure S3. Evolution of the main performance indicators with respect to the WL-S3. Figure S4. Evolution of the main performance indicators with respect to the WL-S4. Figure S5. Evolution of the main performance indicators when using SL-A1 with respect to the WL-S1. Figure S6. Evolution of the main performance indicators when using SL-A2 with respect to the WL-S1. Figure S7. Evolution of the main performance indicators when using SL-A3 with respect to the WL-S1. Figure S8. Evolution of the main performance indicators when using SLA4 with respect to the WL-S1. Figure S9. Evolution of the main performance indicators when using all the sludge line control actions with respect to the WL-S1.

The following files are available free of charge. Supplementary_material (PDF)

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Applying Control Actions for the Water Line and Sludge Line to

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