Control strategies for nitrous oxide emissions reduction on wastewater treatment plants operation

Accepted Manuscript Control strategies for nitrous oxide emissions reduction on wastewater treatment plants operation I. Santín, M. Barbu, C. Pedret, R. Vilanova PII:

S0043-1354(17)30716-9

DOI:

10.1016/j.watres.2017.08.056

Reference:

WR 13179

To appear in:

Water Research

Received Date: 25 April 2017 Revised Date:

22 August 2017

Accepted Date: 23 August 2017

Please cite this article as: Santín, I., Barbu, M., Pedret, C., Vilanova, R., Control strategies for nitrous oxide emissions reduction on wastewater treatment plants operation, Water Research (2017), doi: 10.1016/j.watres.2017.08.056. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Control strategies for nitrous oxide emissions reduction on wastewater treatment plants operation. I. Sant´ına,∗, M. Barbua,b , C. Pedreta , R. Vilanovaa a Departament

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de Telecomunicaci´o i d’Enginyeria de Sistemes, Escola d’Enginyeria, Universitat Aut`onoma de Barcelona, 08193 Bellaterra, Barcelona, Spain b Department of Automatic Control and Electrical Engineering, ”Dunarea de Jos” University of Galati, 800008 Galati, Romania

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Abstract

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The present paper focused on reducing greenhouse gases emissions in wastewater treatment plants operation by application of suitable control strategies. Specifically, the objective is to reduce nitrous oxide emissions during the nitrification process. Incomplete nitrification in the aerobic tanks can lead to an accumulation of nitrite that triggers the nitrous oxide emissions. In order to avoid the peaks of nitrous oxide emissions, this paper proposes a cascade control configuration by manipulating the dissolved oxygen set-points in the aerobic tanks. This control strategy is combined with ammonia cascade control already applied in the literature. This is performed with the objective to take also into account effluent pollutants and operational costs. In addition, other greenhouse gases emissions sources are also evaluated. Results have been obtained by simulation, using a modified version of Benchmark Simulation Model no. 2, which takes into account greenhouse gases emissions. This is called Benchmark Simulation Model no. 2 Gas. The results show that the proposed control strategies are able to reduce by 29.86% of nitrous oxide emissions compared to the default control strategy, while maintaining a satisfactory trade-off between water quality and costs. Keywords: Wastewater Treatment Plant, BSM2G Benchmark, Greenhouse gases emissions, Control strategies, PI controller

∗ Corresponding

author Email addresses: [email protected] (I. Sant´ın ), [email protected] (M. Barbu), [email protected] (C. Pedret), [email protected] (R. Vilanova) Preprint submitted to Water Research

August 24, 2017

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Control strategies for nitrous oxide emissions reduction on wastewater treatment plants operation.

List of Abbreviations Aeration Energy (kWh/d)

ASM1

Activated Sludge Model no. 1

BOD5

5-day Biological Oxygen Demand (mg/l)

BSM

Benchmark Simulation Model

CH4

Methane (kg equivalent CO2 /d)

CO2

Carbon dioxide (kg/d)

COD

Chemical Oxygen Demand (mg/l)

EC

Consumption of External Carbon source (kg/d)

EQI

Effluent Quality Index (Kg of pollutants/d)

GHG

Greenhouse gases

HEnet

Net Heating Energy (kWh/d)

IMC

Internal Model Control

KL a

Oxygen transfer coefficient (d−1 )

ME

Mixing Energy (kWh/d)

METprod

Methane production in the anaerobic digester (kg/d)

N2 O

Nitrous oxide (kg equivalent CO2 /d)

OCI

Overall Cost Index

PE

Pumping Energy (kWh/d)

PI

Proportional-Integral

Q

Flow rate (m3 /d)

Qa

Internal recycle flow rate (m3 /d)

Qin

Influent flow rate (m3 /d)

Qr

External recycle flow rate (m3 /d)

Qw

Wastage flow rate (m3 /d)

qEC

External carbon flow rate (m3 /d)

SNtot

Total nitrogen concentration (mg/l)

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Ammonium and ammonia nitrogen concentration in the effluent (mg/l)

SNO

Nitric Oxide concentration (mg/l)

SNO2

Nitrite concentration (mg/l)

SNO3

Nitrate concentration (mg/l)

SN2 O

Dissolved nitrous oxide concentration (mg/l)

SO

Dissolved oxygen concentration (mg/l)

SS

Readily biodegradable substrate concentration (mg/l)

SP

Sludge Production (kg/d)

TSS

Total Suspended Solids (mg/l)

WWTP

Wastewater Treatment Plants

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SNH,e

1. Introduction

The industrialized world has increased Greenhouse gases (GHG) emissions since the last century. Currently, there is a scientific consensus, almost generalized, about the idea that our mode of production and energy consumption is generating a global climate change, which will cause serious impacts on both the land and socioeconomic systems. The increasing concern of the scientific community about GHG emissions and their negative effects on the environment has led researchers to include this issue in the studies dealing with wastewater treatment plants (WWTPs). Some works, summarized in Mannina et al. (2016); Ni & Yuan (2015), analyze the effects on GHG emissions of some WWTPs parameters, such as anaerobic digester volume, ratio of chemical oxygen demand (COD) to nitrogen, settler removal efficiency, etc. Within GHG emissions, nitrous oxide (N2 O) has a 300 fold stronger warming effect than carbon dioxide (CO2 ) (Solomon et al. (2007)). There are previous works in the literature that have dealt with N2 O emissions in WWTPs (Osada et al. (1995); Kimochi et al. (1998); Tallec et al. (2006); Kampschreur et al. (2008, 2009); Foley et al. (2011); Law et al. (2012); FloresAlsina et al. (2011, 2014); Aboobakar et al. (2013); Wang et al. (2016)). In these works it is shown that N2 O results from incomplete nitrification and denitrification processes and that dissolved oxygen (SO ) is a key factor for its production.

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Total nitrogen concentration in the effluent (mg/l)

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Different models have been developed to estimate and evaluate GHG emissions of WWTPs. It has to be pointed out that the use of these models is currently restricted to the research domain, due to the incomplete knowledge of the N2 O production pathways (Mannina et al. (2016); Ni & Yuan (2015)). For the evaluation of control strategies in WWTPs, Benchmark Simulation Model no. 1 (BSM1) was developed in Copp (2002), which includes the biological treatment and a secondary clarifier, using one-week period to evaluate results. Next, the new version Benchmark Simulation Model no. 2 (BSM2) (Gernaey et al. (2014)) included the whole cycle of a WWTP, adding the sludge treatment and a primary clarifier, applying a more complete influent with a one-year period for evaluation. BSM1 and BSM2 allow the evaluation of effluent quality, operational costs and effluent limits violations, but do not take into account GHG emissions. A modified version of BSM2 was introduced by Flores-Alsina et al. (2011), the Benchmark Simulation Model no. 2 gas (BSM2G), in order to estimate and evaluate GHG emissions in WWTPs. Application of control strategies in WWTPs is one of the possible solutions to avoid the key factors of GHG generation. Flores-Alsina et al. (2011, 2014); Barbu et al. (2017) applied several control strategies for WWTPs by using BSM2G. Specifically, Flores-Alsina et al. (2011) tested the effect of traditional control strategies in GHG emissions, but regardless of those produced by nitrification. Flores-Alsina et al. (2014) show the effect in GHG emissions of the different areas of a WWTP. Barbu et al. (2017) present the effects of other traditionals control strategies on water quality, operational cost and, especially, greenhouse gas emissions, by an integral indicator for performance evaluation. However, it was not the goal of these works to implement specific control strategies in order to reduce N2 O emission in the nitrification process. Boiocchi et al. (2016) proposes a fuzzy controller in order to reduce N2 O emissions. It consists of the manipulation of the oxygen transfer coefficients (KL a) of the aerobic reactors by measuring ammonia nitrogen concentration (SNH ) and nitrate (SNO3 ) in the input and in the output of the nitrification process. However, this control strategy is not designed with the objective of improving effluent quality and reducing operational costs and consequently these are worsened. On the other hand, previous works such as Vrecko et al. (2006); Stare et al. (2007); Nopens et al. (2010); Vrecko et al. (2011); Sant´ın et al. (2015), have shown the advantages of manipulating the SO set-points of the aerated tanks in an ammonium and ammonia nitrogen concentration (SNH ) in the fifth tank (SNH,5 ) cascade controller, where the SO feedback controller manipulates KL a of the aerobic tanks. However, in these referred works, GHG emissions are not evaluated, and

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2. Materials and methods

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The working scenario applied in this work for the evaluation and comparison of the proposed control strategies is BSM2G, a modified version of BSM2, which takes into account the evaluation of GHG emissions including methane (CH4 ), CO2 and N2 O emissions. It was introduced by Flores-Alsina et al. (2011) and it has been modified by the same authors during the last years. Within these modifications, it is included the incorporation of the ammonia oxidizing bacteria (AOB) denitrification pathway for N2 O emissions based on Guo & Vanrolleghem (2014). Therefore, the present BSM2G includes two pathways for N2 O emissions (heterotrophic denitrification and AOB denitrification).

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the present paper shows how the SNH,5 cascade controller alone is not a satisfactory option for taking care of N2 O emissions in the nitrification process. The purpose of the present work is not only to include GHG emissions in the evaluation of the control strategies, but to design specific control strategies to reduce them meaningfully. These control strategies are applied with the aim of improving simultaneously GHG emissions and, at the same time, the more usual criteria of effluent quality, operational costs and effluent limits violations. Specifically, the control strategy proposed here is based on two different kinds of cascade control configurations. One controls SNH,5 and the other controls SNO2 at each aerated tank. Both manipulate the SO set-points of the three aerobic reactors. The idea is to modify the manipulation of SO , also taking into account the N2 O emissions. Two alternatives for the combination of both cascade control configurations are tested. One applies the weighted sum of both controllers and the other consists of a switching system that selects the suitable control strategy based on decision rules. The control system proposal is based on Proportional-Integral (PI) controllers. For the combination of the control strategies by switching, a bumpless transfer system (Peng et al. (1996)) is added in order to avoid abrupt changes in the manipulated variables. It is worth to say that even simple controllers are employed, the advantages of the control strategy are clearly shown. The paper is organized as follows. First, BSM2G working scenario is explained. Next, the proposed control strategies and the tuning of the controllers are presented. After, results are shown, as well as the discussion about them. Finally, the most important conclusions are drawn.

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2.1. Layout The BSM2 layout (Fig. 1) includes BSM1 for the biological treatment of the wastewater and the sludge treatment. The biological treatment consists of five biological reactors and a secondary settler. The first two reactors are anoxic for carrying out the denitrification process and the next three tanks are aerobic where the nitrification process takes place. The Activated Sludge Model no. 1 (ASM1) (Henze et al. (1987)) describes the biological phenomena that takes place in the biological reactors. In BSM2G, ASM1 has been modified with respect to BSM2 with the principles proposed by Hiatt & Grady (2008) and Mampaey et al. (2013) in order to estimate not only SNO3 , but also the other intermediates in the nitrification and denitrification processes: SNO2 , nitric oxide (SNO ), N2 O and dinitrogen (N2 ).

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Qin

Primary clarifier

Qpo

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Secondary clarifier

Activated sludge reactors

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Anaerobic digester

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Dewatering Sludge Removal

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The sludge treatment is composed of a primary clarifier, a thickener, a digester and a dewatering unit. Four recirculation flow rates complete the system: internal 6

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2.2. Evaluation indices The plant performance of the control strategies is evaluated by the percentage of time that the effluent limits are not met, the Effluent Quality Index (EQI), the Overall Cost Index (OCI) and the GHG emissions. The effluent concentrations of total nitrogen concentration (SNtot ), total COD (CODt), SNH , Total Suspended Solids (TSS) and 5-day Biological Oxygen Demand (BOD5) should always be under the limits given in Table 1. SNtot is the sum of SNO3 , SNO2 , SNO , SN2 O and Kjeldahl nitrogen (SNKj), which is the sum of organic nitrogen and SNH .

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recirculation from the last tank (Qa ), external recirculation from the secondary settler (Qr ), recirculation from the thickener (Qto ) and recirculation from the dewatering unit (Qdu ). Wastage flow rate (Qw ) leads the amount of sludge to be treated. The plant is designed for an average influent dry weather flow rate of 20,648.36 3 m /d and an average biodegradable COD in the influent of 592.53 mg/l. The total volume of the bioreactor is 12,000 m3 , 1500 m3 each of the two anoxic tanks and 3000 m3 each of the three aerobic tanks. Its hydraulic retention time is 14 hours. The influent is defined for 609 days and includes rain events and temperature variations. For stabilization, a constant influent is previously applied for 200 days. Only the results from day 245 to day 609 are considered for evaluation.

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Variable

Value

SNtot

< 18 mg/l

CODt

< 100 mg/l

SNH

< 4 mg/l

T SS

< 30 mg/l

BOD5

< 10 mg/l

Table 1: Limits for the effluent pollutants

EQI (Kg of pollutants/d) is defined to evaluate the quality of the effluent and is calculated weighting the different compounds of the effluent loads:

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+10 · (SNO3 + SNO2 + SNO + SN2 O )(t) + 2 · BOD5 (t)) · Q(t) · dt 131

where T is the evaluation period and Q is the flow rate. OCI is defined to evaluate the operational cost as:

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OCI = AE + PE + 3 · SP + 3 · EC + ME − 6 · METprod + HEnet

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where AE is the aeration energy, PE is the pumping energy, SP is the sludge production, EC refers to the carbon that could be added to improve denitrification, ME is the mixing energy, METprod is the produced methane and HE is the heating energy. The method proposed by Flores-Alsina et al. (2011, 2014) is also used here to calculate GHG emissions in a WWTP. It takes into account the GHG production due to the biological treatment, the sludge treatment, relationship between electric consumption and electric generation, the external carbon source production and the sludge to be disposed. The N2 O emissions are calculated based on the principles proposed by Hiatt & Grady (2008). For the N2 O emissions, AOB denitrification pathway based on Guo & Vanrolleghem (2014) has been added in the updated BSM2G to the heterotrophic denitrification pathway reported in FloresAlsina et al. (2011). The heterotrophic denitrification pathway is calculated based on the principles proposed by Hiatt & Grady (2008). As it is explained in FloresAlsina et al. (2011), this model incorporates AOB and nitrite oxidizing bacteria (NOB) using free ammonia and free nitrous acid respectively as their substrates. The model includes also four step denitrification, (sequential reduction of nitrate to nitrogen gas via nitrite, nitric oxide, and nitrous oxide), using individual reaction specific parameters. The parameter values suggested in Hiatt & Grady (2008) were used, except for inhibition constant for free nitrous acid that was reduced from 1·10−4 mg/l (used for high nitrogen loads) to 1·10−6 mg/l (used for low nitrogen loads) to promote NOB growth (Snip (2010); Corominas et al. (2010)).

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3. Control strategies The proposed control strategies have been focused mainly in achieving a reduction in GHG emissions, especially in N2 O. In addition, control strategies to 8

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take into account water quality and operational costs have also been applied. PI controllers have been proposed for these objectives. Fig. 2 and Table 2 show the proposed control strategies (CS1, CS2, CS3 and CS4) as well as the default control strategy (CS0) defined in Jeppsson et al. (2007). They are explained below. For all control strategies, external carbon flow rate (qEC ) in the first reactor (qEC,1 ) is added at a constant flow rate of 2 m3 /d. For the rest of the reactors there is no external carbon addition. Two different Qw values are imposed depending on the time of the year: from 0 to 180 days and from 364 to 454 days Qw is set to 300 m3 /d; and for the remaining time periods Qw is set to 450 m3 /d. Qa is fixed at 61,944 m3 /d. CS0 has been used as a starting point for comparison with the proposed control strategies. The closed-loop control configuration consists of a PI controller that tries to control the SO in the fourth reactor (SO,4 ) at a set-point of 2 mg/l by manipulating KL a in the third tank (KL a3 ), KL a in the fourth tank (KL a4 ) and KL a in the fifth tank (KL a5 ) with KL a5 set to the half value of KL a3 and KL a4 . CS1 is proposed with the aim of avoiding partial nitrification, which occurs when there is not enough oxygen to complete the process, and therefore to reduce emissions of N2 O. It consists of a SNO2 cascade control configuration by manipulating the SO set-points of the default feedback control. However, in order to control N2 O emissions produced by the three aerobic reactors, the feedback control strategy has been extended to three controllers, where each PI controls the SNO2 in one of the three aerobic reactors. Thus, the controlled variables are SNO2 in the third tank (SNO2 ,3 ) at a set-point of 0.015 mg/l, SNO2 in the fourth tank (SNO2 ,4 ) at a set-point of 0.0075 mg/l and SNO2 in the fifth tank (SNO2 ,5 ) at a set-point of 0.005 mg/l. The manipulated variables are SO in the third reactor (SO,3 ) set-point, SO,4 set-point and SO in the fifth tank (SO,5 ) set-point respectively. Furthermore, for the feedback control of SO,3 , SO,4 and SO,5 , three PI controllers are applied that manipulate KL a3 , KL a4 and KL a5 respectively. On the other hand, due to the fact that the only goal of CS1 is to reduce N2 O levels, the important criteria of effluent quality and costs are not taken into account. In order to avoid this, CS2 is applied, which consists of a SNH,5 cascade controller. Different alternatives of this control strategy have already been presented in previous works (Vrecko et al. (2006); Stare et al. (2007); Nopens et al. (2010); Vrecko et al. (2011); Sant´ın et al. (2015)). In this paper, a PI controller is proposed to control SNH,5 at a set-point of 1.5 mg/l by manipulating the set-points of SO,3 , SO,4 and SO,5 . The feedback control is the same as in CS1. By applying CS2, when SNH,5 is higher, SO is increased in order to oxidize more SNH,5 and thus to reduce its peaks. Conversely, when the influent variables result in a lower

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level of SNH,5 , the SO set-point is accordingly decreased to reduce costs and SNO3 generation. However, the negative point of this control strategy is the potential increase of SNO2 in the aerated tanks due to the SO reduction and the consequent increase of N2 O. Thus, by applying CS1 and CS2 together, all the proposed criteria of the performance plant (N2 O emissions, effluent quality and operational costs) are taken into account. However, both control strategies have the same manipulated variables. Therefore, they must be combined. This paper proposes two ways of combination for the two control strategies. They are called CS3 and CS4 respectively. CS3 applies a weighted sum of the resulting SO set-points of both CS1 and CS2. Specifically, it has been tested by applying a weight of 0.5 to each one. In the case of an increase of influent flow rate (Qin ) (Qin > 40000) or SNH,5 (SNH,5 > 3.5) the CS2 weight is changed to 1.25. CS4 combines, by switching, the two control strategies CS1 or CS2 based on decision rules:

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if (SNO2 ,3 >0.025 or SNO2 ,4 >0.025 or SNO2 ,5 >0.025)) or ((SNO2 ,3 >0.0075 or SNO2 ,4 >0.0075 or SNO2 ,5 >0.0075) and SNH,5 <2.5 and Qin < 40000 and TMR>2.4h) or (SO,CS1 2.4h) then CS1

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if (((SNH,5 >3.5 or Qin >40000) and (SNO2 ,3 <0.025 or SNO2 ,4 <0.025 or SNO2 ,5 <0.025)) or (SNH,5 >2.5 and TMR>2.4h and (SNO2 ,3 <0.025 or SNO2 ,4 <0.025 or SNO2 ,5 <0.025)) or (SO,CS1 >SO,CS2 and (SNO2 ,3 <0.0075 or SNO2 ,4 <0.0075 or SNO2 ,5 <0.0075) and TMR>2.4h) then CS2 where TMR is a timer that is reset each time that the control strategy is switched, SO,CS1 is the sum of SO,3 set-point, SO,4 set-point and SO,5 set-point resulting from CS1 whereas SO,CS2 is the sum of SO,3 set-point, SO,4 set-point and SO,5 set-point resulting from CS2. Then, the selection of the control strategy to be applied is based on SNO2 and SNH,5 values. If both variables have low values, the control strategy that generates a lower costs will be selected. This cost is measured here in terms of the resulting SO,CS1 and SO,CS2 values. In order to avoid several switches in a short time (chattering), a timer is added, which sets the minimum time that a control strategy must be applied (residence time). However, if either SNO2 or SNH,5 values reach

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the values of 0.025 mg/l and 3.5 mg/l respectively, the control strategy is switched regardless of the timer. On the other hand, to avoid abrupt changes in the manipulated variables, the bumpless transfer technique has been applied. The previously presented combined control strategies, CS3 and CS4, relay on lower level PI control loops that are combined in a specific way. The following remarks regarding the implementation of these control strategies are therefore to be noticed. Remark 1: Regarding the tuning of the PI controllers, it has been obtained by the Internal Model Control Method (IMC) (Vilanova & Visioli (2012)). Model identification was performed by the Matlab’s System Identification Toolbox. The tunings applied in this work are shown in Table 3. Remark 2: In the case of CS3, the kp value of the SNO2 controllers has been multiplied by two to avoid an increase of N2 O emissions due the following reason: With higher temperatures, SNH,5 values are lower than the set-point established by CS2. As a result, CS2 is not active and only the half value of CS1 is applied to the plant. Therefore, the gain has to be adjusted accordingly. Remark 3: CS4 is based on switching between CS1 and CS2. This fact implies that the corresponding lower level PI control loops should change its operation. In this case, in order to avoid abrupt changes in the manipulated variables, the bumpless transfer technique has been applied. Remark 4: Constraints for the manipulated variables have also been established. Specifically, the values of KL a3 , KL a4 and KL a5 are constrained between 0 and 360 d−1 and SO,3 set-point, SO,4 set-point and SO,5 set-point are constrained between 0.1 and 5 mg/l, except for the PI that controls SNO2 ,3 , which its minimum value is 0.4 in CS1 and CS4, and 0.8 in CS3. This is due to the fact that if SO,3 is decremented at too low levels, the controller keeps SO,3 set-point at its minimum level to reduce SNO2 ,3 . Then, the oxygen levels would not rise until SNH,5 reached a high level. This would require too much time and would produce an undesirable effect on the effluent. Remark 5: As summarized in Table 3, it can be noticed that the operating conditions for the SNO2 control loops impose quite low set-point values for the SNO2 , between 0.005 and 0.015. With the current available sensors technology, it could be difficult to accomplish such measures with precision, as this will need the sensor works with enough measurement accuracy at the lower part of the mea-

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CS0 PI

SO,4

kLa3

kLa4 kLa5

WWTP

CS1

PI

SNO2,4 set-point (0.0075 mg/l)

PI

SNO2,5 set-point (0.005 mg/l)

PI

SO,3 set-point

CS2

PI

SO,4 set-point

PI PI

SO,5 set-point

SNH,5 set-point (1.5 mg/l)

SO,3 SO,4 kLa4 WWTP SO,5 SNO2,3 SNO2,4 kLa5 SNO2,5 kLa3

SO,4 set-point

(0.015 mg/l)

PI

SNO2,4 set-point (0.0075 mg/l)

PI

SNO2,5 set-point (0.005 mg/l)

PI

SO,3 set-point SO,4 set-point

PI

SO,3 SO,4 kLa4 WWTP SO,5 SNH,5 kLa5

kLa3

CS4

PI PI PI

SO,5 set-point

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SNH,5 set-point (1.5 mg/l)

SO,3 SO,4 SO,5 kLa4 WWTP SNH,5 SNO2,3 kLa5 SNO2,4 SNO2,5

kLa3

PI

Decision rules

SO,3 set-point SO,4 set-point

SNO2,3 set-point

(0.015 mg/l)

PI

SNO2,4 set-point (0.0075 mg/l)

PI

SNO2,5 set-point (0.005 mg/l)

SO,5 set-point

PI PI PI

SO,3 SO,4 SO,5 kLa4 WWTP SNH,5 SNO2,3 kLa5 SNO2,4 SNO2,5

kLa3

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suring scale. Existing solutions 1 announce a measuring interval between 0 and 14 mg/l or 2.9 mg/l (depending on the temperature). In addition, the detection limit is announced at 2.9 g/l approximately. This makes the authors be positive on the possibility to work at these low levels. In any case, this is a point that deserves future attention as here just a Benchmark simulation is provided and some pilot-plant or real plant measurement tests should be performed.

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Figure 2: Layouts of the proposed control strategies

4. Simulation results and discussion The control configurations proposed in the previous section are tested and compared. Without loss of generality, ideal sensors have been considered for the simulation results. CH4 and N2 O are given in units of CO2 equivalent. It has to be noted that the present proposal, as any other based on that framework, will need to consider some practical issues when moving to a real plant 1 http://www.unisense.com/NOx

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Control Strategy Controlled variables Set-points

Manipulated variables

Control algorithm Method of combination

CS0

SO,4

2 (mg/l)

KL a3 ; KL a4 ; KL a5

1 feedback PI

CS1

SNO2 ,3 ; SNO2 ,4 ;

0.015; 0.0075;

SO,3 set-point; SO,4 set-point; 3 cascade PI

SNO2 ,5

0.005 (mg/l)

SO,5 set-point

SNH,5

1.5 (mg/l)

SO,3 set-point; SO,4 set-point; 1 cascade PI SO,5 set-point

CS4

SNO2 ,3 ; SNO2 ,4 ;

0.015; 0.0075;

SO,3 set-point; SO,4 set-point; 4 cascade PI

SNO2 ,5 ; SNH,5

0.005;1.5 (mg/l) SO,5 set-point

SNO2 ,3 ; SNO2 ,4 ,

0.015; 0.0075;

SNO2 ,5 ; SNH,5

0.005;1.5 (mg/l) SO,5 set-point

SO,3 set-point; SO,4 set-point; 4 cascade PI

-

Weighted sum

Switching with bumpless

SC

CS3

-

RI PT

CS2

-

Controller

Controlled variables

3 feedback PI SO,3 ; SO,4 ; SO,5

M AN U

Table 2: Configuration of the proposed control strategies Manipulated variables

kp

Ti

KL a3 ; KL a4 ; KL a5

25

0.002

3 cascade PI

SNO2 ,3 ; SNO2 ,4 ; SNO2 ,5 SO,3 set-point; SO,4 set-point; SO,5 set-point -50 0.2

1 cascade PI

SNH,5

SO,3 set-point; SO,4 set-point; SO,5 set-point -1

0.2

Table 3: Tuning of the PI controllers

283 284 285 286 287 288 289 290 291 292 293 294 295 296 297

D

TE

282

EP

281

implementation. The aim of the proposed paper is to highlight the possibilities that the presented plant operation and control configurations provide. This constitutes the first step of the study further to be tested in a pilot plant and finally in a real plant. Regarding to GHG emissions, recent articles such as Ni & Yuan (2015) or Mannina et al. (2016) remark that future research on N2 O emissions as well as real applications of N2 O models in large-scale WWTPs will still be necessary for the models evolve. Even so, Boiocchi et al. (2017) performed a comparison between sensitivity analysis and experiments on real plants, confirming that N2 O emissions are triggered by low oxygen concentrations and/or SNO2 accumulation, as it occurs in BSM2G. Being these events the key factors of the proposed control strategies. In the same way, Boiocchi et al. (2017) also proves that SNtot,e removal efficiency increases with temperature and is limited at cold temperatures by AOB activity, which is also taken into account in the present article and discussed in this section. In Table 4, the results obtained with the proposed control strategies are shown. They are compared to CS0 by the percentage of improvement. The evaluation criteria taken into account are: operational costs, effluent quality and GHG emis-

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sions. On the other hand, Fig. 3, Fig. 4, Fig. 5 and Fig. 6 show the evolution over time of the most important variables. This is shown for summer and winter weather conditions, where their behavior is different due to the effect of temperature. It is due to the fact that biological processes, liquid-gas saturation constants, kinetic parameters, transfer coefficients and equilibrium reactions are temperature dependent (Hiatt & Grady (2008)). These figures show only two days of simulation for each extreme weather station in order to have a better view of the differences between the control strategies. Evolution over the time of SNO2 and N2 O (Fig. 4 and Fig. 5 ) are only shown in the aerobic reactors because N2 O emissions are mainly produced in these tanks. First due to the heterotrophic denitrification pathway, because incomplete nitrification results in SNO2 increase in the anoxic tanks by the internal recirculation. This fact worsens the denitrification process, accumulating more SN2 O (FloresAlsina et al. (2011)). SN2 O is transferred to N2 O in the aerobic reactors (Foley et al. (2010)). Secondly due to the AOB denitrification pathway, which involves the reduction of SNO2 to N2 O by AOB. In this case, also the SNO2 generation due to incomplete nitrification results in N2 O emissions. Starting with the CS1 results, it is observed that a N2 O emissions reduction of 15.08% is achieved, which is the main objective of CS1. This together with the 13.16% decrease of CO2 due to electric consumption, results in a reduction of 3.36% of total GHG emissions. Significant cost reduction by decreasing OCI by 7.83% and a slight improvement in EQI of 1.59% are also obtained. Referring to the limit violations, a large reduction in SNtot,e violations of 97.45% is achieved, but the increase of 5550% in SNH,e violations is excessively large to be considered admissible. SNO2 levels decrease for lower temperatures (Fig. 4 (b)), and therefore the SO values of CS1 (Fig. 3 (b)) are also kept low in the three aerobic tanks, which leads to a CO2 decrease due to electrical consumption. This fact also results in a deterioration of the nitrification process and consequently in a decrease of SNtot,e but also in a meaningful increase of SNH,e (Fig. 6 (b)). This is due to the fact that SNtot depends mainly on SNH and SNO3 , being SNO3 usually greater than SNH . Thus, by oxidizing less SNH , less SNO3 is generated, resulting that the SNO3 reduction is greater than the SNH increase. On the other hand, with higher temperatures, the critical issue is the SNO2 control (Fig. 4 (a)). In this case, SO values of CS1 (Fig. 3 (a)) are higher in order to control SNO2 and consequently to avoid too large N2 O increases (Fig. 5 (a)). In addition, during summer time, SNtot,e and SNH,e (Fig. 6 (a)) are kept under the established limits. As explained in the previous section, the minimum SO,3 value is limited to 0.4,

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as it can be observed in Fig. 3. This is due to the fact that there must be a minimum of aeration to generate SNO2 . Otherwise, the controllers would keep SO set-points at too low values to reduce SNO2 , deteriorating the nitrification process that results in an SNH,e and SNtot,e increase. In this case, the aeration would be so low that SNH increases to excessively high values. Although less SNO3 is generated, the resulting SNtot values are much higher due to the SNH increase. With a minimum of SO , the nitrification process is active, and the controllers supply enough SO to perform the full process and to avoid SNO2 generation. Regarding the results obtained with CS2, Table 4 shows that this control strategy obtains the highest percentages of improvement of EQI, OCI and violation time of SNtot,e and SNH,e . However, besides a slight increase in the time of violation of COD and TSS, there is a very large increase in GHG emissions by 96.96% that is not admissible. Although CO2 emissions due to electrical consumption is decreased by 18.92%, N2 O presents an increase of 1110.35%. This significant N2 O growth is observed when CS2 is applied, because the SO necessary to keep SNH,e at a set-point of 1.5 mg/l is lower compared to the one generated with the other control strategies (Fig. 3, Fig. 5 and Fig. 6). On one hand, with lower levels of SO , costs are reduced, less electricity is consumed and less SNO3 is generated, which results in a SNtot,e reduction and therefore in an EQI reduction. On the other hand, the nitrification is only partial due to the lower levels of SO , which generates more SNO2 and consequently more N2 O emissions. The N2 O peaks obtained in Fig. 5 are approximately at 6.5·104 , 3·105 and 2.5·104 for the summer simulation and around 2·105 , 2.5·105 and 4·104 for the winter simulation for the third, fourth and fifht reactors respectively. They are not shown in the figure because the Y axis is limited to lower values in order to have a better comparison between the other control strategies. In CS3, the application of CS1 and CS2 is done at the same time, applying 50% of the output of each one, except when there is an SNH,5 or/and Qin increase. In that case, the main goal is to reduce SNH,e , therefore CS2 weight is incremented to 1.25. Thus, the large increases of SNH,e violations and N2 O emissions produced by CS1 and CS2 respectively are avoided. By applying CS3 all criteria are improved compared to CS0. Obviously, the EQI and OCI improvements are not as great as using CS2. However, a significant reduction of N2 O of 29.86% is achieved, which is higher than that obtained by applying CS1. As Fig. 3 shows, the SO added by applying CS3 is similar to the SO of CS1 in summer and to the SO of CS2 in winter. This is due to the fact that the most critical control is the SNO2 in summer and the SNH,5 in winter. As explained in the previous section, to compensate for the fact that only half of the values of both controllers

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are applied to the plant, kp of CS3 is doubled. This SO increase compared to CS1 and CS2 has the consequence that the reduction of CO2 emissions due to electrical consumption be lower. The higher SO,3 minimum value of 0.4 mg/l in the SNO2 control results in a higher electricity consumption (higher OCI and CO2 emissions) and in a reduction of N2 O emissions in winter in comparison with CS4 (Fig. 5 (b)). CS4 also combines CS1 and CS2, but selecting one of the two options based on the decision rules explained in the previous section, instead of applying both at the same time as CS3 does. The results obtained with CS4 are very similar to those obtained with CS3, improving also all the criteria in comparison with CS0. The differences regarding CS3 are a slight improvement in EQI, OCI, SNtot,e violations and CO2 emissions due to electrical consumption and a slight worsening of SNH,e violations and N2 O emissions. Fig. 3 shows the manipulated variables and the selection of the control strategy by CS4. At high temperatures, SNO2 control is active most of the time (Fig. 3 (a)), and at low temperatures, SNH,5 control is usually active (Fig 3 (b)). However, when the SO values given by both controllers are low, the controller that gives less SO is selected in order to reduce operational costs. For that reason, CS4 switches between SNH,5 and SNO2 cascade controllers several times along all the year. Another reason for the costs decrease by applying CS4 is the higher SO,3 minimum value established in SNO2 ,3 control by CS3. On the other hand, switching to SNH,5 control at higher temperatures (Fig. 6 (a)) by applying CS4 results in a slight reduction of electricity consumption (lower OCI and CO2 emissions), N2 O increase (Fig. 5 (b)), SNtot,e reduction and SNH,e increase (Fig. 6 (b)) in comparison with CS3. As seen in the results of Table 4, the SNtot,e reduction has more impact on EQI that the SNH,5 increase. As it can be seen in Fig. 3, the manipulated variables do not suffer abrupt changes when CS4 switches from one controller to another. This shows the right performance of the bumpless transfer technique. Finally, Fig. 7 shows the time evolution of the variables during the evaluation year (from 245 day to 609 day) by using CS4. This figure is presented to give a broader view of the variables evolution and not the details as in previous figures. For this reason only one control strategy has been shown. This figure clearly shows that SNO2 and N2 O have similar changes at different scales of values, increasing with high temperatures and decreasing with low temperatures. Conversely, SNH,e and SNtot,e are more critical in winter. During this period, between the days 430 and 480, coinciding with the lowest temperatures, the selected control strategy is always CS2, because the high values of SNH,5 do not allow

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Evaluation Criteria

CS0

CS1

% of

CS2

improvement

% of

CS3

improvement

% of

CS4

improvement

% of improvement

5665.98

5575.55

1.59

4184.86

26.14

5490.42

3.1

5469.22

3.47

OCI

9272.78

8547.01

7.83

8238.44

11.15

8656.93

6.64

8635.33

6.94

SNtot,e violations

10.60

0.27

97.45

0.0086

99.92

1.87

82.35

1.72

85.09

0.14

7.91

-5550

0.086

38.57

0.034

75.71

0.054

38.57

0.054

0.054

0

0.057

-5.55

0.054

0

0.054

0

0.29

0.26

10.34

0.31

-6.89

0.27

6.89

0.27

6.89

0.23

0.23

1596.20

1355.52

2465.11

2140.81

(% of operating time) SNH,e violations COD violations (% of operating time) TSS violations BOD5 violations (% of operating time) N2 O emissions (Kg CO2 equivalent/d) CO2 due to electric consumption (Kg CO2 /d) Total GHG emissions

17,851.10 17,251.03

0

0.23

0

0.23

0

0.23

0

15.08

19319.60

-1110.35

1119.48

29.86

1197.79

22.17

13.16

1998.67

18.92

2189.71

11.17

2133.71

13.44

3.36

35,159.48

-96.96

17,065.18

4.4

17,134.19

4.02

D

(Kg CO2 /d)

M AN U

(% of operating time)

SC

(% of operating time)

RI PT

EQI (Kg of pollutants/d)

TE

Table 4: Results of the proposed control strategies and its comparison with CSO

switching to CS1 in order to reduce costs. Figure 4: Resulting SNO2 of two days simulation in summer (a) and in winter (b) for the default and the proposed control strategies.

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Figure 3: Resulting SO of two days simulation in summer (a) and in winter (b) for the default and the proposed control strategies.

Figure 5: Resulting N2 O of two days simulation in summer (a) and in winter (b) for the default and the proposed control strategies.

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4 2

300

350

0.02 0

250

300

15000

5000 0

250

10

0

300

250

300

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350

350

EP

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400

450

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selection of concentration (mg/l) control strategy temperature (ºC)

N2O (kg CO2 /d)

250

D

2

SNO (mg/l)

0 0.04

M AN U

SO (mg/l)

SC

Figure 6: Resulting SNtot,e and SNH,e of two days simulation in summer (a) and in winter (b) for the default and the proposed control strategies.

350

500

SO,3 SO,4 SO,5 550

600 SNO

,3

SNO

,4

SNO

,5

2

2

2

450

500

550

600 N2O,3 N2O,4 N2O,5

400

450

500

550

600 SN

tot,e

SNH,e Limits Temperature

400

450

500

550

600

1=CS1 0=CS2

0.5 0

250

300

350

400

450

500

550

600

time (days)

Figure 7: Time evolution of the controlled and manipulated variables of all the evaluation year

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This paper has proposed the implementation of control strategies in WWTPs with the main objective of reducing N2 O emissions and also trying to improve effluent quality, to reduce pollutants limits violations at the effluent and to reduce operational costs. Specifically, the work has proposed to reduce N2 O emissions by applying a SNO2 cascade control configurations in the aerobic tanks by manipulating the SO set-points of the default feedback control. This SNO2 control has been combined with a cascade controlller previously used in the literature with the objectives of improving water quality and reducing costs. Since both control strategies use the same manipulated variables, two combinations of the two cascade control configurations have been proposed. One by a weighted sum of both control strategies and the other by switching between the two controllers based on decision rules. The simulation results show that the two proposals for the combination of the two cascade control configurations are similar, and both improve all the criteria compared with the default control strategy. Specifically they have been reduced by 29.86% with CS3 and by 22.87% with CS4. It is the authors’ opinion that these observed small differences could be even lower by adjusting the tuning parameters or the set-points. The results also show that both cascade control configurations, each one applied separately, are very poor in some criteria. Specifically, the SNH,5 control has a very large increase in N2 O emissions mainly generated by partial nitrification that can maintain the SNH,5 set-point generating less SNO3 and reducing costs, but without taking into account the N2 O emissions. Regarding to SNO2 , mainly in winter, SNO2 levels are lower and the SO required to keep the SNO2 set-points is also lower, resulting in a SNH,e increase that is not considered. In conclusion, it has been shown that the combinations of SNH,5 control and SNO2 control are a right option to reduce N2 O emissions and to obtain a satisfactory trade-off between water quality, effluent limits violations and operational costs.

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Acknowledgment

This work was partially supported by the the Spanish CICYT program under grant DPI-9016-77271-R. Lund University and Technical University of Denmark are gratefully acknowledged for providing the BSM2G Matlab/Simulink code, with a special mention for Dr. Flores Alsina and Dr. Ulf Jeppsson. 19

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Barbu, M., Vilanova, R., Meneses, M., & Santin, I. (2017). On the evaluation of the global impact of control strategies applied to wastewater treatment plants. Journal of Cleaner Production, 149, 396–405.

Boiocchi, R., Gernaey, K. V., & Sin, G. (2016). Control of wastewater N2 O emissions by balancing the microbial communities using a fuzzy-logic approach. IFAC-PapersOnLine, 49, 1157–1162.

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Boiocchi, R., Gernaey, K. V., & Sin, G. (2017). Understanding N2 O formation mechanisms through sensitivity analyses using a plant-wide benchmark simulation model. Chemical Engineering Journal, 317, 935–951. Copp, J. B. (2002). The Cost Simulation benchmark: Description and simulator manual (COST Action 624 and Action 682). Luxembourg: Office for Official Publications od the European Union. Corominas, L., Flores-Alsina, X., Snip, L., & Vanrolleghem, P. (2010). Minimising overall greenhouse gas emissions from wastewater treatment plants by implementing automatic control. In Proceedings 7th IWA Leading-Edge Conference on Water and Wastewater Technologies. Phoenix, AZ, USA, June 2e4.

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Aboobakar, A., Cartmell, E., Stephenson, T., Jones, M., Vale, P., & Dotro, G. (2013). Nitrous oxide emissions and dissolved oxygen profiling in a full-scale nitrifying activated sludge treatment plant. Water Research, 47 (2), 524–534.

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Flores-Alsina, X., Arnell, M., Amerlinck, Y., Corominas, L., Gernaey, K. V., Guo, L., Lindblom, E., Nopens, I., Porro, J., Shaw, A., Snip, L., Vanrolleghem, P. A., & Jeppsson, U. (2014). Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Science of the Total Environment, 466-467, 616–624.

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Effluent quality and operational costs are also taken into account.

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Two cascade control strategies are combined in order to achieve all objectives.

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Benchmark Simulation Model No 2 Gas is used for simulation.

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