Real-Time Con ol-O ien ed Quali y Modelling in
Combined U ban D ainage Ne wo ks ?
Congcong Sun ∗Be na Joseph-Du an ∗∗ Thibaud Ma uejouls ∗∗∗
Gab iela Cemb ano ∗,∗∗ Jo di Mesegue ∗∗ Vicenc¸ Puig ∗Xa ie Li ico ∗∗∗
∗Ad anced Con ol Sys ems G oup a he Ins i u de Rob`o ica i In o m`a ica
Indus ial (CSIC-UPC), Llo ens i A igas, 4-6, 08028 Ba celona, Spain,
(e-mail: [email p o ec ed]).
∗∗ CETaqua, Wa e Technology Cen e, C a. d’Esplugues 75, Co nell`a de
Llob ega , 08940 Ba celona, Spain, (e-mail: [email p o ec ed]).
∗∗∗ LyRE, Lyonnaise Resea ch Cen e , Domaine du hau -Ca ´e, Bˆc −C4,
33400 Talence, F ance, (e-mail: [email p o ec ed]).
Abs ac : U ban d ainage ne wo ks (UDN) ca y u ban was ewa e o was ewa e ea men plan s
(WWTP) in o de o egene a e i be o e eleasing i o he en i onmen . Combined UDN (CUDN)
ca y bo h ain and was ewa e oge he , which can o e load he UDN and p oduce combined sewe
o e lows (CSO) ha pollu e he en i onmen . Managemen o CUDN is ecei ing inc easing a en ion
om bo h esea che s and wa e manage s, in o de o mee he high quali y s anda ds equi ed o
wa e and en i onmen acco ding o EU Wa e F amewo k Di ec i e. Due o he complex dynamics
o wa e quali y, in eg a ed con ol o CUDN and WWTP conside ing bo h lows and quali y o he
con eyed was ewa e is a di icul p oblem. In o de o design a eal- ime con ol (RTC) aking in o
accoun hyd aulic and quali y a iables, he use o concep ual quali y models is conside ed as a sui able
op ion. This pape mainly p esen s a simpli ied concep ual quali y modelling app oach o ep esen
he dynamics o suspended solid in sewe s o CUDN o ien ed o eal- ime con ol. A sewe simula o
implemen ed in SWMM (S o m Wa e Managemen Model) in eg a ed wi h a lumped concep ual model
o o al suspended solid (TSS) is used o calib a ion and alida ion. A eal example o Pe ino sewe
ne wo k is used as a case s udy. Discussions abou RTC implemen a ion in CUDN a e also p o ided
in his pape , whe e Model P edic i e Con ol (MPC) is p oposed as he sui able me hod o con ol he
in eg a ed wa e and quali y models in CUDN as u u e mo i a ion.
Keywo ds: CUDN, WWTP, CSO, RTC, concep ual quali y model, SWMM, MPC.
1. INTRODUCTION
Combined u ban d ainage ne wo ks (CUDN) a e gene ally de-
signed o con ey was ewa e lows o ea men acili ies in d y
wea he . Du ing hea y- ain e en s, as soon as capaci ies o u -
ban d ainage ne wo ks (UDN) and Was ewa e T ea men Plan
(WWTP) a e exceeded, mixed wa e is by-passed o ecei ing
bodies p oducing combined sewe o e lows (CSO), which a e
iden i ied as pollu ing haza dous o biological species and eco-
logical s a us as explained in Becouze e al. (2009); Gaspe i
e al. (2008). In o de o op imize o e all objec i es o he
comple e u ban d ainage sys em, including he CUDN, he
WWTP and he ecei ing en i onmen , and p e en pollu ion
o he ecei ing wa e s, an in eg a ed con ol o bo h UDN and
WWTP sys ems is equi ed.
Since 1970s, he po en ial o using eal- ime con ol (RTC)
wi hin CUDNs has been discussed in Bu le e al. (2005);
Schilling (1989). Re e ences ha e p o ed ha RTC is a eliable
and cos -e ec i e solu ion o CUDNs, which can imp o e he
pe o mance o CUDNs minimizing looding and CSO ol-
umes, hus p o ec ing he en i onmen as p esen ed in Fu e al.
(2010); Bul e e al. (2010); Bu le e al. (2005); Cemb ano
?This esea ch is unded by EU unding o he p ojec LIFE EFFIDRAIN
LIFE14 ENV/ES/00080
e al. (2004); Xu e al. (2013); Beeneken e al. (2013); Ga c´
ıa
e al. (2015). Among eal- ime con ol me hods, model p edic-
i e con ol (MPC), which can compu e op imal con ol ac ions
aking in o accoun no only he cu en measu emen s bu also
p edic i e beha io s in a ce ain ho izon, has been success ully
es ed in wa e supply and also in he con ex o ad anced
u ban d ainage ne wo ks by Cemb ano e al. (2004); Puig e al.
(2009); Bu le e al. (2005); Pleau e al. (2005).
Ne e heless, he majo i y o RTC applied in CUDN ha e only
ocused on hyd aulic model and con ol objec i es wi hou
conside ing he pollu ing quali y load inside he ca ied wa-
e , e.g. Cemb ano e al. (2004). The ew e e ences which
conside quali y models du ing he con ol p ocess mos ly use
simula ion ools o o e come dealing wi h he complexi y o
modelling wa e quali y di ec ly. In Ra hnayake (2015), con ol
o CUDNs minimizing CSO is achie ed by using a non-so ed
gene ic algo i hm (NSGA) linked wi h s o m wa e manage-
men model (SWMM) 5.0. Bu le e al. (2005) op imizes in-
eg a ed modelling o he ope a ion and con ol o an in eg a ed
u ban d ainage sys em by de eloping a simula ion package
SYNOPSIS which in eg a es he sewe sys em (simula ed by
KOSIM), ea men plan (simula ed by IAWPRC) and i e
model (simula ed by DUFLOW) using di e en so wa es.
Fo he pu pose o ope a ing RTC o in eg a ed u ban d ainage
sys ems aking in o accoun CSO pollu ion, app op ia e quali y
models a e needed. Such models allow o e alua ion o wa e s
acco ding o wa e quali y c i e ia as in Ahye e e al. (1998).
Because o he inpu da a unce ain y and di icul y in calib a-
ion, modelling he gene a ion and anspo a ion o pollu ion
in CUDN du ing a s o m e en is complex. The e indeed exis
some physically-based models which can p esen quali y dy-
namics in CUDN, bu he ma hema ical equa ions a e di icul
o be implemen ed wi hin RTC as shown in Rouse (1937); Van
Rijn (1984); Macke (1980); Acke s and Whi e (1973).
In o de o con on hese challenges, a simpli ied concep ual
quali y modelling app oach o ien ed o RTC o dynamics o
o al suspended solid (TSS) is p esen ed. The SWMM so -
wa e (Rossman (2015)) in eg a ed wi h a lumped concep ual
TSS model which depends indi ec ly on physical pa ame e s
h ough low and compu a ion o Sain -Venan model, is used
o calib a e and alida e he modelling app oach. A eal sewe
in Bo deaux, Pe ino sewe ne wo k is used as a case s udy.
The emainde o his pape is o ganized as ollows: Sec ion 2
p esen s he SWMM simula o in eg a ed wi h a lumped con-
cep ual TSS model, which ep esen s cha ac e is ics o solid
dynamics in he sewe . Sec ion 3 p esen s he simpli ied con-
cep ual quali y modelling app oach o TSS in sewe s, a discus-
sion abou RTC applied in CUDN is also p o ided in he end o
Sec ion 3 as mo i a ion o u u e wo k. In Sec ion 4, calib a ion
and alida ion o he p oposed simpli ied modelling app oach
using SWMM-TSS and he eal-li e case Pe ino sewe ne wo k
is p esen ed. Sec ion 5 p esen s he conclusion o his pape .
2. SWMM-TSS BASED ON LUMPED CONCEPTUAL
MODEL
As a ep esen a i e example o sewe pollu an s, models o
solid concen a ion and load a e p esen ed in his pape ,
which gene ally p esen h ee dynamic beha io s by Be and-
K ajewski (2006); Rossman (2015):
•Accumula ion o solid sedimen s o e u ban ca chmen ;
•Washo o solid sedimen s by ain all;
•T ans e , e osion, deposi ion o solids in sewe ne wo ks
and e en ion anks.
2.1 Concep o SWMM-TSS
Since solids in sewe a e highly d i en by hyd aulics, a new
quali y model has been de eloped based on he SWMM 5 sim-
ula o ( e sion SWMM5.1.011) ha al eady includes a de ailed
desc ip ion o hyd aulics using ull Ba ´
e de Sain Venan equa-
ions. The model lib a y has been modi ied by adding equa ions
desc ibing solids beha io o he ollowing phenomena:
•Hyd ology: The exponen ial build-up model on he ca ch-
men was modi ied o ake in o accoun only he impe i-
ous a ea a he han he o al a ea. The washo model was
eplaced by he one p oposed by M´
e adie (2011);
•Sewe : Accumula ion and e osion phenomena a e de-
sc ibed using Wiu (1985) ene gy balance based model
used by Be and-K ajewski (1993);
•S o age uni : Mixing and se ling p ocesses a e inspi ed
om he wo k o B ia (1995); Ma u´
ejouls e al. (2012).
Fig. 1. Modi ica ions made in he SWMM 5 lib a y model
Figu e 1 is he scheme illus a ing he modi ica ions made in he
SWMM 5 lib a y model. Whi e boxes a e o exis ing modules
in SWMM 5 and g ey boxes a e o added quali y module.
2.2 Sewe Equa ions in SWMM-TSS
As p e iously explained, he main addi ion in he model lib a y
a e ega ding he sewe accumula ion and e osion model. This
model is based on Wiu (1985) ha calcula es a anspo ca-
paci y o he low CT depending on he wa e ρeand pa icle
densi ies ρn, he wa e eloci y Umand pipe slope I, he pa -
icles’ se ling eloci ies Wnacco ding o pa icles’ ac ions
Fpn o each pa icle classes and he yield coe icien ηndi ec ly
dependen on hyd aulics cha ac e is ics.
CT =ρeUmI
PN
n=1(Fpn(ρn−ρe)Wn
ρnηn)
(1)
The CT is compa ed wi h he TSS a he pipe inle o calcula e
he quan i y o TSS ha can se le. The e osion model is based
on a i s o de equa ion dependen on he mass o sedimen and
a speci ic e osion coe icien o each pa icle classes.
3. SIMPLIFIED CONCEPTUAL MODELLING APPROACH
Lumped complex hyd ology models wo k well o simula ing
eal dynamics o TSS in sewe ne wo ks, bu o he RTC pu -
pose o CUDN, a simple model s uc u e should be p esen ed
acco ding o he ollowing p inciples p esen ed by No eys and
Cluckie (1997); Cluckie e al. (1999); Puig e al. (2009):
•Rep esen a i eness o he main dynamics;
•Simplici y, lexibili y, expendabili y and speed;
•A ailabili y o on-line calib a ion and op imiza ion.
The modelling app oaches p esen ed in his pape a e designed
o be used by RTC o p edic he e olu ion o TSS pa ame e s
o e a sho ho izon, which p e en he limi a ion o classical
modelling app oaches o empo al measu emen campaign.
In o de o e alua e TSS discha ges o he ecei ing wa e ,
pe o ming measu emen s a he ou le o sewe is he mos
sui able s a egy. The simpli ied dynamic model o TSS in
CUDNs will conside TSS in sewe s and mass balance equa ion
in he junc ions. TSS beha io s in de en ion anks, wei s and
also WWTP will be pa o u u e esea ch. The leas squa e
unc ion is used o measu e pe o mance o hese app oaches.
3.1 Simpli ied Modelling App oaches o Sewe
O igina ing om he hyd aulic model o CUDNs as in Cem-
b ano e al. (2004); Puig e al. (2009), a sewe unk in a CUDN
can be assumed as a wa e ank con aine wi h capaci y o
collec wa e based on he di e en be ween ups eam (Qin:
m3/s) and downs eam (Qou :m3/s) lows as in Figu e 2. The
anspo model o wa e olume (V) inspi ed om linea ank
model o hyd aulic anspo will be exp essed as:
V(k+1) =V(k)+ ∆ (Qin(k)−cV(k)) (2a)
Qou (k)=cV(k) (2b)
V
Qin, TSSin Qou , TSSou
Fig. 2. Tank model o a sewe
In a sewe unk, mass conse a ion does no necessa ily apply
o he solids because o solid se lemen and e osion. In o de
o gene alize anspo model o TSS, an in e media e a iable
Xwhich has no di ec physical meanings is used and he in-ou
ela ion o TSS can be de ined as:
X(k+1) =X(k)+ ∆ (TS S in(k)−cX(k)) (3a)
TS S ou (k)=cX(k) (3b)
whe e
TS S in : TSS (g/m3) en e in o a sewe
TS S ou : TSS (g/m3) ou o a sewe
k: he cu en ime
∆ : sampling ime
c: pa ame e s need calib a ion
A e combining he equa ion (3a) and (3b), he ollowing
dynamic model o TSS in a sewe is p oduced:
Model 1
TS S ou (k+1) =(1 −c)TS S ou (k)+cTS S in(k) (4)
By le ing he wo coe icien s o TS S ou and TS S in be inde-
penden , a gene aliza ion o Model 1 eadily comes o mind,
leading o:
Model 2
TS S ou (k+1) =c1TS S ou (k)+c2TS S in(k) (5)
Fu he mo e, om he physical cha ac e is ics, in a sewe , he
dynamic o TSS is a ec ed by he TSS sedimen , e osion and
also ime delays (Figu e 3). A e ha , a new linea delayed
exp essions o TSS and wa e olume can be de ined as:
Model 3
TS S ou (k)=c cT S S in(k−d)+ep(6a)
Qou (k)= cQin(k−d0) (6b)
whe e
d: delay o TSS inside a sewe
d0: delay o low inside a sewe
c c, c,ep: pa ame e s need calib a ion
Fig. 3. TSS model in a sewe
Using hese modelling me hodologies, simpli ied models o
TSS inside a sewe a e c ea ed, which allow on-line model
calib a ions and eal- ime con ol o sewe ne wo ks. Fo each
sewe , he pa ame e s c,c1,c2,c c,d, c and epneed o
be calib a ed by GAMS using his o ic o eal- ime da a om
eleme y sys ems. Emphasized on TSS modelling app oach, c,
c1,c2,c c,dand epa e will be calib a ed.
3.2 Simpli ied Modelling App oaches o Junc ion
Junc ions in a sewe ne wo k co espond o he poin s whe e
wa e lows and also TSS a e me ged o spli . These elemen s
i mass balance ela ions. When TSS comes in o a junc ion in
he sewe ne wo k, he dynamics o TSS is modelled as equali y
cons ain s ela ed o ups eam (TSS mass en e s in o his
junc ion) and downs eam (TSS mass exi s om his junc ion).
Fo di e en junc ions, he e could exis di e en numbe s o
b anches (mo e han wo). Take a h ee b anches junc ion as
an example (as shown in Figu e 4), he exp ession o he mass
conse a ion o TSS can be w i en as (i is conside ed ha he
junc ion does no add a signi ican delay):
TS S in3(k)Qin3(k)=TS S ou 1(k)Qou 1(k)+TS S ou 2(k)Qou 2(k)
(7)
Fig. 4. TSS model in a junc ion
3.3 Model Pe o mance E alua ion
In o de o alida e and compa e implemen a ions o hese p o-
posed modelling app oaches, he leas squa e unc ion is used
o e alua e model pe o mance o calib a ed alue using hese
simpli ied model compa ing wi h simula ed esul s p oduced by
lumped model o SWMM-TSS.
FC =
K
X
k=1
(V(k)−R(k))2(8)
whe e
V: calib a ed alue using simpli ied model
R: simula ed alue using SWMM-TSS
K: ime s eps
Besides ha , he i ing a e be ween he calib a ed alue wi h
simpli ied model and he simula ed alue wi h SWMM-TSS
is de ined using Nash Su cli e model e iciency coe icien as
explained in Nash (1970):
V=PK
k=1V(k)
PK
k=1k(9a)
FCTS S =100 











1−qPK
k=1(R(k)−V(k))2
qPK
k=1(R(k)−V(k))2












(9b)
3.4 Real- ime con ol o CUDN
While s a e-o - he-a implemen a ions o RTC in UDN a e
based only on hyd aulic a iables, he in eg a ed con ol o
CUDNs and WWTP mus ake in o accoun quali y a iables
in o de o minimize he o e all pollu ing load o he ecei ing
en i onmen . The main ideas behind his in eg a ed con ol a e:
•con olling he wa e de en ion and di e sion in he u ban
d ainage ne wo k, so as o minimize TSS in he e luen s
o he sys ems;
• aking in o accoun he a iabili y in WWTP capaci y,
acco ding o he in luen low and TSS concen a ion.
The p ocess o CUDN con ol is also highly dependen on
ain all scena ios which equi e ain p edic ions. Conside ing
hese cha ac e is ic o CUDN con ol, MPC has been accep ed
o ha e ad anced ad an ages in con olling u ban d ainage
wa e sys ems as in Cemb ano e al. (2004); Xu e al. (2013).
MPC can gene a e op imal con ol ac ions by op imizing he
objec i e unc ion a e e y con ol s ep. Besides ha , MPC
can p edic he u u e beha io o he sys em h ough using an
in e nal model o e a ini e p edic ion ho izon. In CUDN, s a e
o space o MPC can be p esen ed as in Xu e al. (2013):
x(k+1) = (x(k),u(k),d(k)) (10)
whe e xis a ec o o ne wo k s a es (e.g. wa e olume and
TSS mass in a ank); uis a ec o o con ol a iables such as
low ac oss a commanded ga e; dis a ec o o dis u bances
ela ed o ain in ensi y and uno .
Con inued e o s a e ocused on he in eg a ed con ol o
CUDN which in ol es wa e quan i y and quali y using easible
modelling app oaches o imp o ing pe o mance o CUDN
like in Bu le e al. (2005); Van olleghem e al. (2005).
4. MODEL CALIBRATION AND VALIDATION
The eal case s udy o he sewe ne wo k o Pe ino in Louis
Fa gue ca chmen o Bo deaux Me opole co e s a o al a ea
o 260 ha ha is mainly esiden ial. The sewe leng h is 3
km wi h an a e age slope o 0.007, qui e cons an o e he
whole ca chmen . I includes a e en ion ank sepa a ed in h ee
hyd aulically connec ed bodies o a o al s o age olume o
35000 m3. E en i he slope is gene ally low, he e is no
sedimen issues on he sewe epo ed om he ope a o s.
Rain scena ios used o calib a ion and alida ion come om
he eal ain all measu ed a F ance in he yea o 2007 as show
in Figu e 6. Rain scena ios T1, T2 in di e en ime s age o he
yea 2007 which ep esen di e en ain densi ies a e selec ed
o p oduce TSS aining da a using SWMM-TSS in o de
o calib a e he p oposed simpli ied models. A e achie ing
calib a ed TSS models, ano he wo di e en ain scena ios
V1, V2 a e applied in SWMM-TSS o p oduce TSS da a o
alida ing he wo king o hese p oduced simpli ied models.
Fig. 5. SWMM con igu a ion o he Pe ino case s udy
Fig. 6. Rain Scena io o Pe ino in he yea o 2007
The selec ed ain scena ios a e he ain all in he ollowing
ime s ages (whe e he ime use o ma MM/DD/YYYY HH :
MM :S S ):
T1 : 10/10/2007 00:00:00-10/11/2007 00:00:00
T2 : 12/02/2007 00:00:00-12/03/2007 00:00:00
V1 : 02/10/2007 00:00:00-02/11/2007 00:00:00
V2 : 07/08/2007 00:00:00-07/09/2007 00:00:00
4.1 Model Calib a ion
Fo each elemen s in CUDN, on-line calib a ion is needed. Se
he sampling and epo ime o SWMM-TSS as 5 minu es,
an eceden d y days as 10. A e applying T1, T2 ain scena ios
in o Pe ino case, he TSS beha io s du ing hese ain all a e
p oduced, which is he aining da a o calib a e he concep ual
models.
Taken condui APERI2.1 (as shown in Figu e 5) in he ups eam
o Pe ino ne wo k as an example, whe e TS S in ep esen s TSS
en e s in o condui APERI2.1, TS S ou ep esen s TSS ou o
his elemen . Table 1 p o ides de ail pa ame e s calib a ed o
Table 1. Calib a ed Pa ame e s
c c1c2c c epd
T1 0.26 0.73 0.26 0.87 45.11 2
T2 0.20 0.80 0.20 0.80 72.77 2
he h ee models o condui APERI2.1 in T1 and T2 ain sce-
na ios. Figu e 7 and Figu e 8 show he pe o mances compa -
isons be ween calib a ed models (TS S ou Model in he igu es)
and simula ed alue using SWMM-TSS(TS S ou S WMM in he
igu es) in T1 and T2 ain all scena ios. These esul s con i m
ha , all he models ha e conside able pe o mance. Model 2
wo ks be e han o he wo models. Model 3 has low i ing
pe o mance bu he changing ends o TSS is simila wi h ha
in SWMM-TSS, which has meaning du ing he con ol p ocess.
0 50 100 150 200 250
200
300
400
500
600
700
Time S eps [5min]
TSS Concen a ion [mg/l]
Model 1 i ing = 93.3504%, Model 2 i ing = 93.4646%,Model 3 i ing = 62.1974%
TSSin SWMM
TSSou SWMM
TSSou Model 1
TSSou Model 2
TSSou Model 3
Fig. 7. Calib a ion pe o mance o T1
0 50 100 150 200 250
200
250
300
350
400
450
500
Time S eps [5min]
TSS Concen a ion [mg/l]
Model 1 i ing = 98.0939%, Model 2 i ing = 98.2986%,Model 3 i ing = 89.0995%
TSSin SWMM
TSSou SWMM
TSSou Model 1
TSSou Model 2
TSSou Model 3
Fig. 8. Calib a ion pe o mance o T2
4.2 Model Valida ion
In o de o alida e u he he p oposed calib a ed models, ain
scena ios V1 and V2 a e applied o he models calib a ed in
T1 and T2 sepa a ely. Figu e 9, 10, 11 and 12 p o ide he
alida ion pe o mance using V1 and V2 ain scena ios o he
p oduced models by T1 and T2. The i ing compa isons p o-
ided om hese igu es shows ha , compa ing wi h simula ion
esul s om SWMM-TSS, all he p oposed models a e wo king
well using he calib a ed models.
Table 2 wi h de ailed i ing pe o mances o bo h he calib a-
ion and alida ion scena ios con i m hese conclusions.
5. CONCLUSIONS
This pape p oposed simpli ied concep ual modelling ap-
p oaches o ep esen ing dynamic beha io s o TSS in sewe
ne wo ks, which a e o ien ed o be in ol ed in o he in eg a ed
0 50 100 150 200 250
200
300
400
500
600
700
800
900
Time S eps [5min]
TSS Concen a ion [mg/l]
Model 1 i ing = 96.5059%, Model 2 i ing = 96.4982%,Model 3 i ing = 91.213%
TSSin SWMM
TSSou SWMM
TSSou Model 1
TSSou Model 2
TSSou Model 3
Fig. 9. V1 alida ion pe o mance o T1
0 50 100 150 200 250
500
1000
1500
2000
2500
3000
3500
4000
Time S eps [5min]
TSS Concen a ion [mg/l]
Model 1 i ing = 65.5939%, Model 2 i ing = 65.4938%,Model 3 i ing = 54.6964%
TSSin SWMM
TSSou SWMM
TSSou Model 1
TSSou Model 2
TSSou Model 3
Fig. 10. V2 alida ion pe o mance o T1
0 50 100 150 200 250
200
300
400
500
600
700
800
900
Time S eps [5min]
TSS Concen a ion [mg/l]
Model 1 i ing = 95.5693%, Model 2 i ing = 95.5597%,Model 3 i ing = 90.8331%
TSSin SWMM
TSSou SWMM
TSSou Model 1
TSSou Model 2
TSSou Model 3
Fig. 11. V1 alida ion pe o mance o T2
0 50 100 150 200 250
500
1000
1500
2000
2500
3000
3500
4000
Time S eps [5min]
TSS Concen a ion [mg/l]
Model 1 i ing = 63.6791%, Model 2 i ing = 63.6013%,Model 3 i ing = 44.358%
TSSin SWMM
TSSou SWMM
TSSou Model 1
TSSou Model 2
TSSou Model 3
Fig. 12. V2 alida ion pe o mance o T2
RTC con olle o CUDN and WWTP o en i onmen al p o-
ec ion. The SWMM is used o p oduce ealis ic simula ed
da a o calib a ion and alida ion. The Pe ino sewe ne wo k,
which is a eal li e example, is used as a case s udy. The
applica ion and alida ion esul s o he p oposed modelling
app oaches ha e p o ed ha , he simpli ied concep ual model
P ep in s o he 20 h IFAC Wo ld Cong ess
Toulouse, F ance, July 9-14, 2017

Table 2. Pe o mance Compa isons
Tes /Valida e Scena ios Model 1 Model 2 Model 3
T1 93.35% 93.46% 62.20%
V1 96.51% 96.50% 91.21%
V2 65.59% 65.49% 54.70%
T2 98.09% 98.30% 89.10%
V1 95.57% 95.56% 90.83%
V2 63.68% 63.60% 44.36%
can cap u e he main cha ac e is ics o TSS e olu ion in sewe
ne wo ks wi h equa ions ha a e simple enough o be used
o in eg a ed RTC con ol o CUDN. This wo k is pa o a
esea ch p ojec aimed o design Model P edic i e Con ol o
bo h wa e quan i y and quali y models in CUDN.
ACKNOWLEDGEMENTS
The au ho s wish o hank he suppo ecei ed by he Eu o-
pean Commission esea ch g an o p ojec LIFE EFFIDRAIN
(LIFE14 ENV/ES/000860), hank Aig¨
ues de Ba celona o he
inancial and echnical suppo . Au ho s also hank Bo deaux
Me opole and SGAC o echnical suppo and inancial sup-
po h ough he esea ch con en ion signed wi h he LyRE.
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