A Planning Tool for COD Flow Optimisation to a Waste Water

Treatment Plant

Kirsten M. Nielsen and Tom S. Pedersen

Automation and Control, Department of Electronic Systems, Aalborg University, Aalborg, Denmark

Keywords:

Model Predictive Control.

Abstract:

The waste water ﬂows to a typical wastewater treatment plant (WWTP) is comprised from periodic domestic

ﬂows and more stochastic industrial ﬂows. Especially variations in the ﬂow of Chemical Oxygen Demand

(COD) at the inlet to the WWTP are problematic due to the biological puriﬁcation process and bio gas pro-

duction. Traditionally the inlet is un-controlled. A way to reduce variations is to insert a buffer tank near the

industrial areas and control the tank outlet according to a prediction of household COD ﬂow. As a ﬁrst step

a planning tool for operator control of the buffer tank outlet 24 hours ahead is designed. The WWTP in the

Danish town Fredericia is used as a case. At the moment the only on-line measurement is the inlet ﬂow to the

wastewater treatment plant and reliable measurements in the network are difﬁcult to establish. A Model Pre-

dictive Control scheme is shown to be able to give considerable reduction in the COD ﬂow variations. To do

this two models are introduced; one describing the buffer tank and sewer network from the tank to the WWTP

and one describing the daily variations in the household ﬂow. Additionally prediction of the industrial outlet

is included. The control scheme has been tested showing good results in a SWMM simulation environment

(ProtectionAgency, 2016) based on network architecture and measurements in Fredericia.

1 INTRODUCTION

The topic within this paper is to improve the the pu-

riﬁcation and bio-gas production in wastewater treat-

ment systems by smoothing the COD Flow to a

WWTP.

The sewer system drains wastewater from indus-

tries and private households. A sewer system network

consists of gravity pipes, pressurised pipes, pumps,

manholes, weirs etc. making up a complex system,

there is no retain tanks in the network. At the moment

the inﬂow to the WWTP is un-controlled. The idea is

to insert a retrain tank in the system and control the

tank outlet. The ﬁrst iteration which is in focus in this

work is an operator support system making 24 hours

ahead plans for outﬂow from the tank. The planning is

based op prediction of industrial and household waste

water ﬂow and concentration and potentially precipi-

tation. The execution of the plan is left to the opera-

tor. In this work, the sewer network in the Danish city

Fredericia with approximately 50000 inhabitants is in

focus.

Wastewater is comprised of different pollutants

like phosphor, nitrogen and COD. The inlet ﬂow and

concentration to the WWTP is varying due to daily

variations in household wastewater, varying industrial

outlets and different time delays from these sources

to the WWTP inlet. In addition, precipitation causes

irregular variations. In Fredericia in dry periods ap-

proximately 50 % of the wastewater comes from in-

dustries.

A detailed model description of all phenomena

is extremely comprehensive as seen in e.g. the

simulation tool WATS (T. Hvitved-Jacobsen et al.,

2013),(DHI, 2017) and is not well suited for con-

troller design. Therefore, a simple model describing

the main dynamics is formulated, additionally only

ﬂow and COD are taken into account and furthermore

it is assumed, that no biological processes takes place

in the sewer network.

The sewer system as well as the biological pro-

cesses are complex and further it is difﬁcult to make

on-line measurements of the pollutants. In Fredericia

the only available real time on-line measurement is

the inlet ﬂow to the WWTP. Off-line measurements of

COD ﬂow and concentration are available from Octo-

ber 2017. The average and ﬁltered average of the total

ﬂow as well as the COD concentration and COD ﬂow

in October 2017 are shown in Fig. 1.

As seen in the ﬁgure the shapes of the COD and

222

Nielsen, K. and Pedersen, T.

A Planning Tool for COD Flow Optimisation to a Waste Water Treatment Plant.

DOI: 10.5220/0011296200003271

In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2022), pages 222-229

ISBN: 978-989-758-585-2; ISSN: 2184-2809

ﬂow inlet show the same tendencies. In (Nielsen and

T. S. Pedersen, 2020) a control scheme for minimis-

ing the total mass ﬂow variations at the inlet to the

WWTP using a buffer tank was developed. In the

present work the beneﬁt from including COD mea-

surements is investigated.

Figure 1: 24 hours average ﬂow [l/s], average COD concen-

tration [mg/l] and average COD ﬂow [mg/s] based on mea-

surements from 30 days at the inlet to Fredericia WWTP

(frese.dk, 2018).

Control of sewer systems are described in (Mari-

naki and Papageorgiou, 2005; ?; Pilgaard and Peder-

sen, 2018; ?). A way to minimize the ﬂow variations

is to insert buffer tanks in the sewer network and con-

trol the outputs from these. At the moment no such

tanks are available in the sewer system in Fredericia.

Due to large industrial waste water variations a log-

ical place for a buffer tank is close to the industrial

outlets. An algorithm to control the outﬂow from a

buffer tank in order to minimize the input ﬂow varia-

tions at the WWTP is developed.

To design a controller, models of the buffer tank,

the sewer network, the household ﬂows and industrial

ﬂows are necessary.

A simple tank model based on a mass balance is

used. A dynamic model describing main characteris-

tics of the sewer network is formulated; in (Nielsen

and T. S. Pedersen, 2020) it is shown that the Saint-

Venant equations under certain assumptions can lead

to a delay model for the ﬂow. The ﬂow delay from a

buffer tank to the WWTP inlet is found from cross

correlation on simulated data. Similarly the COD

concentration delay in the network is identiﬁed; this

delay combined with a linear transfer function consti-

tutes the COD model. The model describing the ﬂows

from households to the WWTP is based on measure-

ments.

A Model Predictive Controller (MPC) with a per-

formance function aiming to minimize the variance

of inlet COD ﬂow to the WWTP has been formulated

given buffer tank volume constraints.

To test the beneﬁt of a buffer tank inserted in

the sewer system a realistic simulation describing

ﬂow and concentration dynamics in the Fredericia

sewer system is developed using the EPA’s Storm Wa-

ter Management Model (SWMM) (ProtectionAgency,

2016).

In section 2 the Fredericia sewer system is de-

scribed. Section 3 considers the control concept. The

SWMM simulator is presented in section 4. The

sewer system modelling is described in section 5.

Section 6 is a description of the actual control of the

buffer tank output. The control concept is tested in

SWMM which is described in section 7 and ﬁnally

the conclusion is in section 8.

2 FREDERICIA SEWER SYSTEM

Fredericia wastewater treatment plant covers the town

of Fredericia, nearby villages and industrial areas

north and west of the town. The total sewer net is

among the largest in Denmark. Households and in-

dustrial areas north and west of the town dominate the

wastewater in Fredericia. The map shows the north-

ern part of Fredericia divided in subareas. A large

number of pipes leads to the WWTP. In this work, the

pipes from the industrial areas to the WWTP are con-

sidered. These are indicated in the map Fig. 2.

Figure 2: Main sewer system in Fredericia, (Pilgaard and

Pedersen, 2018). The blue square is the WWTP, oval shapes

are industrial areas, black circle is inlet from industry to the

main pipe.

Household wastewater is predictable with regard

to ﬂow (Schlutter, 1999). As seen in Fig. 2, the area

covered by the wastewater plant is large and the sewer

network is split into numerous branches implying that

the shape of the inﬂow from the households to the

wastewater treatment plant is inﬂuenced by varying

delays in ﬂow.

In the WWPT, the quality of the wastewater treat-

ment and biogas production are dependent on the in-

A Planning Tool for COD Flow Optimisation to a Waste Water Treatment Plant

223

ﬂow, as the biological processes needs time for scal-

ing. Smoothing the input ﬂow will improve the qual-

ity of the WWTP processes.

A case study covering the north part of the area

is considered; it comprises the industrial area north

to the city, a buffer tank added close to the industrial

area, the sewer network from the buffer tank to the

WWTP and the residential areas indicated in Fig. 2.

A controller is optimising the output ﬂow from the

tank.

3 CONTROL CONCEPT

The main goal for the control system is to reduce the

COD ﬂuctuations in the inlet, Y , to the WWTP. It

is assumed that the only measurement is Y and the

only controllable variable is the outlet ﬂow U from

the buffer tank. The inlet ﬂow from industries to the

buffer tank is Q

. Y

re f

is the WWTP inlet ﬂow refer-

ence. The household ﬂows is added to one ﬂow di-

rectly to the WWTP inlet. In this work, we look at

one buffer tank. The concept for controlling this may

easily be extended to more detention tanks. Fig. 3

shows a sketch of the simpliﬁed system.

Figure 3: Simpliﬁed sewer system with the main compo-

nents buffer tank, households, sewer pipe and WWTP.

ref

U Y

Controller Main pipe

−

Figure 4: Classical control concept for the problem showing

inputs, outputs and disturbances.

A classical control concept is illustrated in Fig. 4.

is the total household ﬂow disturbance and is seen

as a ﬂow directly to the WWTP. The model of the

main pipe may include a transport delay; therefore, a

classic controller will result in a low bandwidth and

poor disturbance rejection (Aastrom and Haegglund,

2006; ?). The disturbance is periodic, see Fig. 1, this

periodicity is difﬁcult to take into account in a clas-

sic control concept. It is well known from the clas-

sical control theory that cascading can improve the

performance. More ﬂow measurements in the main

pipe will make this possible and up-stream measure-

ments of the household ﬂow could be used as feed

forwards. Iterative learning control could be another

way to improve a classic controller. A third concept

is to use a neural network. In this work it is cho-

sen to use a Model Predictive controller (MPC); the

method is based on optimisation from prediction via

a plant model. The model can include time delays

and it is possible to incorporate knowledge of future

disturbances (household ﬂow and potentially precipi-

tation) in the optimisation.

4 SWMM MODEL OF

FREDERICIA NORD

A realistic simulation model of the main pipes in

Fredericia Nord is developed using the EPA’s Storm

Water Management Model (SWMM) (ProtectionA-

gency, 2016). SWMM is used for simulations of wa-

ter runoff quantity and quality. It provides an environ-

ment for hydraulic and water quality simulations. The

main sewer system in the northern part of Fredericia

Fig. 2 is modelled based on information on dimen-

sions and location of pipes, channels, drains, man-

holes, storage’s and pumps. Dominating residential

and industrial areas are connected to the main pipe

Fig. 5, the household ﬂow from these areas are con-

structed from typical data from households (Schlutter,

1999) and scaled by the number of inhabitants. In ad-

dition to the existing sewer system elements a buffer

tank are added close to the industrial area.

The purpose of the model is twofold. Test se-

quences of ﬂow and concentrations can be applied

in the network and corresponding data sets for ﬂow

and concentration e.g. at the inlet to the WWTP can

be used to investigate propagation rates and ﬁltration

for ﬂow and concentration. Additionally evaluation of

the control concept can be carried on in the simulation

environment.

5 MODELLING THE SEWER

SYSTEM

A model predictive controller requires development

of an appropriate model. The model may consists of a

description of the industrial wastewater ﬂow and con-

centration, a tank model combined with a model of

the sewer network from the tank to the WWTP and a

ICINCO 2022 - 19th International Conference on Informatics in Control, Automation and Robotics

224

Figure 5: SWMM model of the main sewer system in Fred-

ericia Nord (Pilgaard and Pedersen, 2018).

household ﬂow and concentration disturbance model.

The Sewer Network and Tank Model

The model of the combined tank and sewer system

must be able to show mass ﬂow dynamics and dy-

namics corresponding to the COD ﬂow in the sewer

net from the tank inlet of industrial waste water to the

WWTP. Separate models for mass ﬂow and COD con-

centrations in the network and the tank are developed

and combined to one state space description.

The sewer system is dominated by pipes but also

consisting of manholes, pumps, minor accumulating

tanks etc, Fig. 5. In (Nielsen and T. S. Pedersen,

2020) the connection from the tank to the WWTP is

considered not ﬁlled pipes and the pipes are described

as an open channel. Therefore the ﬂow, the level and

the COD content in sewer networks are modelled

by the Saint-Vernant equations (Crossley, 1999),

(Michelsen, 1976), (Andersen, 1977). A simpliﬁed

linear model is derived (Nielsen and T. S. Pedersen,

2020) showing that the mass ﬂow in the sewer

network can be modelled as a delay. The COD

concentration propagation rate is assumed to be the

same as for the ﬂow.

In this work a more thorough analysis of the net-

work model is presented. The SWMM model Fig. 5 is

used to provide realistic data for the Fredericia sewer

system. Average ﬂows and concentration are used as

initial values. A mass ﬂow impulse and a COD con-

centration impulse are used to identify delays from

the tank outlet to the WWTP inlet Fig. 6 upper left

and lower left. The simulated values are given with a

sampling rate T

of 5 minutes. Even though the out-

let from the tank is diluted by mixing the ﬂow and

concentration from the remaining part of the net, the

ﬁgures show clear delays for ﬂow and concentration

from the tank to the WWTP.

Figure 6: SWMM simulations for pipe responses. Upper

left: Impulse mass ﬂow added at the buffer tank (blue) and

the ﬂow at the WWTP (red). Lower left: Impulse in COD

concentration added at the buffer tank (blue) and the con-

centration at the WWTP (dotted). Upper right: Mass ﬂow

at the buffer tank (blue) and at the WWTP (red). The input

is delayed 70 minutes corresponding to 14 samples. Lower

right: COD concentration at the buffer tank (blue) and at

the WWTP (red). The input is delayed 90 minutes corre-

sponding to 18 samples. At all simulations other inputs are

constant.

The delays T

d− f low

and T

d−COD

are determined by

cross correlation..

d− f low

= 14samples = 70minutes

d−COD

= 18samples = 90minutes

d−di f f

= T

d−COD

− T

d− f low

= 4samples = 20minutes (1)

To ﬁnd the transfer functions mass ﬂow and con-

centration steps are added at the tank outlet. Fig. 6

upper right and lower right shows input steps and cor-

responding data sets for the inlet to the WWTP; in the

ﬁgures the tank outputs are staggered by the identiﬁed

delays. Fig. 6 upper right shows that the dynamics for

mass ﬂow is negligible and it will not be taken into

account. Fig. 6 lower right is a step in COD concen-

tration. The concentration dynamics are modelled as

a second order system. The parameters are identiﬁed

using the system identiﬁcation tool SENSTOOL (?)

giving a function of the type

out

(s) =

Kω

+ 2ζωs +ω

k(s) (2)

In the optimisation algorithm the ﬂow delay T

d− f low

is handled by shifting the industrial sequences in rela-

tion to the household sequences. This shifting is done

A Planning Tool for COD Flow Optimisation to a Waste Water Treatment Plant

225

for both ﬂow and concentration. The additional con-

centration delay T

di f f

is included in the model.

out

(s) =

Kω

+ 2ζωs +ω

−T

di f

k(s) (3)

The network model is rewritten in state space

form and discretised. The discrete form is





(t + T

)

(t + T

)





= A

sec





(t)





+ B

sec

k(t) (4)

out

= C

sec



(t)



and x

is from equation 3, the additional states

correspond to the number of samples in T

di f

. In short

the state space description for the pipe from the tank

to the WWTP is written as

net

(t + T

) = A

net

(t) + B

net

k(t) (5)

out

= C

net

(t)

The input to the pipes is output from the buffer

tank. The tank is illustrated in Fig. 7. Tank inﬂow

is industrial waste water mass ﬂow m

with the con-

centration k

, tank output is the mass outﬂow m

with

concentration k

Figure 7: Buffer tank with COD (dark blue) and Water

(light blue).

The total mass in the tank is given by:

dM(t)

= m

(t) − m

(t) (6)

where M is mass of ﬂuid in the tank.

The COD mass balance is :

dM(t)k(t)

= m

(t)k

(t) − m

(t)k(t)

= M(t)

dk(t)

+ k(t)

dM(t)

(7)

where k is COD concentration in the tank deﬁned

k =

kg COD in tank

mass in tank

(8)

Combining equation 6 and 7 and linearizing gives

a model for the MPC

M(t)

= ˆm

(t) − ˆm

(t) (9)

k(t)

= −

¯m

(

−

M(t) +

−

ˆm

(t)

¯m

(t) −

¯m

k(t) (10)

where ¯· is the operating point and the small signal

value ˆ· is the deviation from the operating point. To

simplify the notationˆis ignored in the succeeding.

The linearised equations 9 and 10 are formulated

in a state space description.

Using the state vector [

M(t)

k(t)]

gives

M(t)

k(t)

0 0

−

¯m

(

−

¯m



M(t)

k(t)





1 0 −1

−

¯m







ˆm

(t)

ˆm

(t)





(11)

The input matrix is divided : B

acting on the in-

dustrial ﬂow and concentration to the tank and B

act-

ing on the output ﬂow from the tank ˆm

. This output

ﬂow ˆm

is the controlled signal.

M(t)

k(t)

= A



M(t)

k(t)



+ B



ˆm

(t)



+ B

ˆm

(t) (12)

On discrete form



M(t + T

)

k(t + T

)



= A



M(t)

k(t)



+ B



ˆm

(t)



+ B

ˆm

(t)

(13)

The two equation systems 5 and 13 are combined

in series giving a new state space system where the

output is the tank mass

M(t) and the concentration

out

(t).

X(t + T

) = AX(t)+ B



ˆm

(t)



+ B

ˆm

(t) (14)



M(t)

out

(t)



= CX(t)

X(t) is

M and

k in the tank combined with a num-

ber of states from the second order system combined

with the delays T

di f

ICINCO 2022 - 19th International Conference on Informatics in Control, Automation and Robotics

226

The Household Flow and Concentration

Using MPC for optimisation of the COD ﬂow inlet

to the WWTP necessitates prediction of the house-

hold ﬂow. There is no on-line measurements avail-

able. Fredericia WWTP has measured ﬂow and COD

concentration at the inlet to the WWTF during Octo-

ber 2017, Fig. 1. This ﬂow is a combination of house-

hold waste water, industrial waste water and rain. The

industrial outlet is not periodic but it is known that the

average industrial ﬂow is approximately 50 % of the

inlet to the WWTP during dry periods. Rain raises the

ﬂow and lower the concentration. In the network the

household ﬂow has different time delays from differ-

ent parts of the town. Adding waste water from the

inhabited areas these delays have to be taken into ac-

count when estimating the total ﬂow from the house-

holds to the WWTP. In (Nielsen and T. S. Pedersen,

2020) it is shown that it is possible to ﬁnd a model of

the total the household ﬂow based on the frequency

spectrum for a large number of measurements. A

Kalman ﬁlter based on this model and on-line mea-

surements are able to give a prediction of the ﬂow.

Due to the lack of measurements in the sewer-net

it is chosen to use ﬁltered and scaled 24-hour vari-

ations of the total ﬂow and concentration measured

at the inlet to the WWTP as typical average house-

hold waste water values. The optimisation is based

on small signal values, therefore the mean value of

ﬂow and concentration are subtracted. Test sequences

including e.g. weather forecast information can be

added to the mentioned average ﬂow. The small sig-

nal values of the household ﬂow is ˆm

and the con-

centration is

6 CONTROL OF THE BUFFER

TANK OUTPUT

The aim of the optimisation is to minimise the varia-

tion of the COD-inlet ﬂow to the WWTP.

The total discrete linearised inlet ﬂow

Y to the

WWTP at time j is

Y ( j) =

out

ˆm

( j) + ¯m

out

( j) +

ˆm

( j + T

− 1)

+ ¯m

( j + T

− 1) − ˆµ (15)

where

is the average household concentration

and ˆm

is the household ﬂow variation, ¯m

is the av-

erage household ﬂow and

is the household concen-

tration variation. ˆµ is the mean value variation.

For the discretised and linear system dynamics,

the MPC problem can be formulated as

min

J =

−1

∑

j=0

(

out

ˆm

( j) + ¯m

out

( j) +

ˆm

( j +T

di f

− 1)

+ ¯m

( j +T

di f

− 1) − ˆµ)

+ (W

M( j))

(16)

subject to :

x( j + 1) = A

x( j) +B

( j) (17)

is a weight.

The constraints are given by the buffer tank size

and the outﬂow capacity

0 ≤ M( j) ≤ M

max

0 ≤ m

( j) ≤ m

o,max

(18)

Future values for ˆm

k, ˆm

and

is part of the

performance. The mean value µ is ˆµ + ¯µ where ˆµ

typically is 0, ¯µ is

¯µ =

∑

j=1

( j + T

di f

( j + T

di f

) + k

( j)m

( j)

(19)

The pipe model includes a delay T

d− f low

repre-

sented as 14 states, eq. (1). The off-line use of the op-

timisation makes it possible to to streamline the opti-

mization code by reducing the pipe model order using

time shifting. The tank out ﬂow is shifted T

d− f low

relation to the household sequences. This shifting is

done for both ﬂow and concentration. The additional

industrial concentration delay T

di f f

is included in the

model.

The performance function is now

J =

−1

∑

j=0

(

k ˆm

( j) + ¯m

k( j) +

ˆm

( j) + ¯m

( j) − ˆµ)

+ (W

M( j))

(20)

The optimisation problem is solved in (Nielsen.

and Pedersen, 2021) using the technique mentioned

in (Maciejowski, 2002) and the MATLAB function

quad prog.

7 TEST OF THE OPTIMISATION

STRATEGY

Two different tests of the optimisation strategy are

tested in this section. First the concept is tested us-

ing a simple model of the sewer network where the

liquid ﬂow from the tank to the WWTP is approx-

imated by a delay and the concentration propagation

A Planning Tool for COD Flow Optimisation to a Waste Water Treatment Plant

227

rate is approximated by a delay combined with second

order system. Secondly the optimisation algorithm is

tested using the SWMM model of the Fredericia Nord

sewer system in combination with a buffer tank (Pil-

gaard and Pedersen, 2018).

7.1 Test using a Simple Sewer Network

Model

Here the control algorithm is tested using a model of

the sewer network based on one delay for the mass

ﬂow and another delay combined with a second order

transfer function for the COD concentration.

Fig. 8 shows the input sequences for the simula-

tion. The household ﬂow and concentration is based

on measurements from Fredericia; The industrial ﬂow

and the concentrations are constructed test signals.

COD ﬂows are calculated.

Figure 8: Inputs to the test. Upper left is COD concentra-

tion from industry, upper right is mass ﬂow from industry,

mid left is COD ﬂow from industry, mid right is COD con-

centration from households, lower left is mass ﬂow from

households and lower right is COD ﬂow from households.

The results of optimisation is shown in Fig. 9.

The upper left ﬁgure shows the outﬂow from the tank,

the upper right is the COD ﬂow from the tank, the

lower left plot shows the COD ﬂow at the inlet to the

WWTP and the lower right ﬂow is the COD ﬂow to

the WWTP without a tank. It is seen that the algo-

rithm is capable of smoothing changes in the inputs.

7.2 Test using EPA SWMM

At the moment no tank has been established in Fred-

ericia, therefore the control strategy is tested using

the EPA SWMM model of Fredericia Nord sewer net-

work with a tank added at the industrial area. Fig. 10

upper plot shows the COD ﬂows from the household

(yellow) and from industry (blue). The household

COD ﬂow is based on measured data, the industrial

Figure 9: Optimisation results. Upper left is the outﬂow

from the tank, upper right is the optimised COD ﬂow the

tank, lower left is the COD inﬂow to the WWTP using op-

timisation and lower right is the COD inﬂow to the WWTP

without a tank.

input is a test sequence. Additionally the optimised

COD outﬂow from the buffer tank (red) is shown.

Fig. 10 lower is the COD inﬂows to the WWTP corre-

sponding to the wastewater ﬂows in the upper ﬁgure.

The blue curve is the inlet to the WWTP correspond-

ing to the existing situation without a tank, the red

curve is the result of inserting a tank and optimisa-

tion of the outlet. As seen the ﬂows is signiﬁcantly

smoothened using the optimisation.

Figure 10: Upper:COD-ﬂows from households(yellow) and

industry (blue) used in the EPA SWMM simulation. Ad-

ditionally the optimised COD outlet (red) from the tank is

shown. Lower: COD ﬂows at the inlet to the WWTP with-

out a tank (blue) and with optimised tank outlet (red).

8 CONCLUSION

The aim of this work is to improve the puriﬁcation and

bio gas production in WWTPs by securing a smooth

COD ﬂow at the inlet. This is done by inserting a

ICINCO 2022 - 19th International Conference on Informatics in Control, Automation and Robotics

228

buffer tank in the sewer network and develop a opti-

misation algorithm for control of the tank outlet. As

a transition from manual control with few interaction

possibilities to full automation is difﬁcult, an opera-

tor support system to assist in control of the tank is

developed. The control system tends to optimise the

output from the tank with respect to COD ﬂow varia-

tions at the inlet to the WWTP using MPC. The MPC

is based on knowledge on average household waste

water mass ﬂow and COD concentration, prediction

of industrial mass ﬂow and concentration and a model

of the sewer network. Under linear assumptions it is

shown that the mass ﬂow model results in a time delay

and the COD concentration propagation can be de-

scribed as another delay model combined with a sec-

ond order ﬁlter. The output from the MPC is the tank

outlet ﬂow. As the result of the work is part of the

basis for building a buffer tank in Fredericia the sys-

tem is tested on a simpliﬁed network model and on a

realistic model for Fredericia Nord sewer network im-

plemented in EPA SWMM (ProtectionAgency, 2016).

Tests are showing good results for planning up to 24

hours ahead.

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