ONLINE DECISION SUPPORT FOR AN EVAPORATION NETWORK
José Luis Pi a ch, Ca los Gómez Palacín, Césa de P ada
Sys ems Enginee ing and Au oma ic Con ol Depa men , EII, Uni e sidad de Valladolid.
C/ Real de Bu gos s/n, 47011, Valladolid.{jose.pi a ch | ca los.gomez | p ada}@au om.u a.es
Ma c Kalliski
Depa men o Biochemical and Chemical Enginee ing, Technische Uni e si ä Do mund.
Emil-Figge-S . 70, 44227, Do mund Ge many. Ma c.Kalliski@bci. u-do mund.de
Ch is ian Jasch
Lenzing Ak iengesellscha , We ks aße 2, 4860 Lenzing, Aus ia. c.j[email p o ec ed]om
Abs ac
This wo k p esen s a decision-suppo ool o add ess
he model-based op imiza ion app oach o online
load alloca ion and scheduling o cleaning ope a-
ions in an e apo a ion ne wo k. The aim is imp o -
ing he esou ce e iciency by supplying he op imal
solu ion o a gi en p oduc ion goal. The app oach
includes he semi-au oma ic upda e o e apo a o
models, which is based on his o ical da a o minimal
modelling e o . The s uc u e o he p oblem is
o mula ed ia mixed-in ege p og amming and in e-
g a ed in o he plan supe ision sys ems. P oduc ion
cons ain s, conce ns abou he p ac ical implemen-
a ion and isualiza ion p e e ences a e also aken
in o accoun in he design o he p o o ypical ool.
Key Wo ds: in eg a ion, decision suppo , isualiza-
ion, su oga e model, e apo a ion ne wo k, esou ce
e iciency.
1 INTRODUCTION
The as changes in global ma ke condi ions and
inc easing en i onmen al cons ain s o ce he p o-
cess indus y o con inuously adap hei ope a ion o
keep compe i i eness. In his way, an agile plan -
wise op imiza ion o con inuous and disc e e deci-
sions is equi ed o ope a e as e icien as possible
while i ing he new condi ions [1].
To ace hese eme ging challenges, plan manage s
and ope a o s need o be p o ided wi h compu e -
based ools which guide hem o balance p oduc ion
and esou ce consump ion [2]. Special a en ion o
he e icien de elopmen o plan models needs o be
paid, as i is he basis o ad anced con ol and coo -
dina ion asks. Mo eo e , he e exis many coding
languages and al e na i es o implemen op imiza ion
algo i hms in o so wa e modules bu , in he end,
hese ools mus be in eg a ed in o he in o ma ion
echnology (IT) in as uc u e o he plan s, e.g., ia a
neu al deploymen pla o m ha connec s o di e -
en IT sys ems [3].
This pape deals wi h hese issues in he e apo a ion
ne wo k o Lenzing AG, a iscose ibe p oduc ion
ac o y loca ed in Aus ia. A desc ip ion o he ap-
p oach and a p o o ypical ool o he op imiza ion o
he alloca ion o e apo a o s o p oduc s is p esen ed,
wi h he goal o minimizing he o e all speci ic
s eam consump ion (SSC).
In addi ion, e apo a ion plan s su e om pe o -
mance deg ada ion due o ouling inside he hea
exchange s. Hence, main enance asks become nec-
essa y o eco e e iciency, bu hey in ol e a cos .
The e o e, a sui able scheduling o such cleaning
asks o e ime o each e apo a o a ises as an addi-
ional p oblem o he load alloca ion op imiza ion.
This equi es he esolu ion o an economic op imiza-
ion in ol ing disc e e and con inuous alues.
Ou app oach conside s da a d i en plan models,
able o be upda ed in a semi-au oma ic way, and
e icien mixed-in ege nonlinea p og amming
(MINLP) so wa e o sol e he p oposed op imiza-
ions online. The di e en op imiza ions we e p o-
g ammed as modules using MATLAB® and MS
Excel®, and hen linked o he PI Sys em in he plan .
The pape o ganizes as ollows. Nex , a desc ip ion
o he applica ion, sys em limi a ions and assump-
ions a e gi en. The modelling ou ine is summa ized
in Sec ion 3. Then, he op imiza ion o he load allo-
ca ion and he cleaning schedule is o mally s a ed in
Sec ion 4. The in e aces design oge he wi h he
sys em in eg a ion is p esen ed in Sec ion 5. Finally,
a summa y o he wo k oge he wi h indica ions o
he nex s eps is gi en in he las sec ion.
2 APPLICATION CASE
The p oduc ion o iscose ibe s is based on he e-
newable esou ce wood. The cellulose con ained in
he wood is chemically ea ed and con e ed o a
iscose solu ion. The key p oduc ion s ep is he e-
gene a ion o his solu ion in o ibe s, in oduced in
an acid ba h. Apa om he solid ibe s, his chemi-
XXXVIII Jo nadas de Au omá ica
575
cal ea men p oduces sodium sul a e (Na2SO4) and
wa e as side p oduc s. This deg ades he acid ba h
and, in consequence, he p oduc quali y. The e o e,
i is necessa y o cons an ly emo e wa e and sodi-
um sul a e om he ba h. A ne wo k o mul iple-
e ec e apo a ion plan s linked o a c ys alliza ion
sec ion is used o such a ask.
Figu e 1 depic s a simpli ied schema o an e apo a-
ion plan , whe e he main manipula ed a iables a e
he eci cula ion low  and he p oduc empe a u e
 a e he hea exchange s. Fo a mo e de ailed iew
o he plan he eade is e e ed o [4].
Figu e 1. Simpli ied schema o an e apo a ion plan
wi h he impo an con ol a iables F and T.
2.1 NETWORK DESCRIPTION
The e apo a ion ne wo k comp ises a o al o 23
plan s o di e en nominal capaci ies. This ne wo k
needs o p ocess 5 di e en p oduc s, so some plan s
can se e in mo e han one p oduc , bu only a single
p oduc a a ime. The changeo e om one p oduc
o ano he ge s a cos and equi es ime.
The e a e se e al ac o s which a ec he e iciency:
x Plan ype: [a] Compac wi h small capaci y, [b]
3-s age e apo a o s and [c] la ge e apo a o s.
x Ex e nal in luences: Ambien empe a u e and
ai humidi y a ec he cooling owe s.
x Fouling: Ba h impu i ies se le wi hin he hea
exchange s, educing he hea ans e .
x Ope a ing poin : Fo a desi ed e apo a ion se
poin , he con ol alues a e no uniquely de ined
The esou ce e iciency indica o (REI) [2] chosen
o his p ocess is he SSC, de ined o each plan as
he a io o esh s eam consumed pe amoun o
wa e emo ed om he p oduc . The ask o he
plan pe sonnel is o ind an op imal alloca ion o
plan s o p oduc s ha ensu es he equi ed e apo a-
ion a e pe p oduc wi h he lowes SSC. This op i-
mal ope a ion can only be achie ed by conside ing
all hese signi ican in luences on he esou ce e i-
ciency in he op imiza ion o he e apo a ion ne -
wo k. Howe e , he size o he combina o ial p ob-
lem and he amoun o in luence ac o s make he
p oblem e y challenging. Indeed, model-based op-
imiza ion app oaches ha e al eady imp o ed he
e iciency in he ope a ion o an e apo a ion plan
[4], and we s ill o esee mo e po en ial sa ings in a
be e coo dina ion o he whole ne wo k.
The e o e, compu e -aided decision suppo (DS)
ools need o be p o ided o help ope a o s in his
ask, so sui able models need o be de eloped. Fu -
he mo e, a sui able compu a ional ime is equi ed o
p o ide esul s in accep able ime, o a oid p oduc-
ion delays, and o ensu e ope a o accep ance.
2.2 PLANT MODELS
The e apo a o se -up is simila o all plan s, bu
a ies in he numbe o s ages and p oduc ion capaci-
ies. The amoun o e apo a ed wa e depends on he
ci cula ing low , he p oduc empe a u e , and
cooling wa e empe a u e  which, in u n, is
limi ed by he ou doo empe a u e. A mapping o he
e apo a ion low achie ed o di e en alues in he
manipula ed a iables can be eco ded (Figu e 2).
Figu e 2. Speci ic s eam consump ion VS e apo a-
ion se poin , ob ained o di e en con ol alues.
Ex ensi e expe imen al es s shown ha he e ec s o
 and  on he SSC, as well as on he e apo a ion
low, can be desc ibed by linea ela ionships. Addi-
ionally, i was obse ed ha he mapping in Figu e 2
is shi ed in a linea ashion wi h  and he ouling
s a e. Hence, wo linea models we e p oposed o
desc ibe he plan beha io , one o he e apo a ion
low (EF) and ano he o he SSC, as linea unc-
ions o he inpu s ,, and he ouling s a e 
( o be es ima ed):
󰇟 󰇠⋅󰇯


1󰇰 (1)
󰇟 󰇠⋅󰇯


1󰇰 (2)
Whe e 󰇝, ,,󰇞 a e cons an pa ame e s
o o line eg ession and  a e ime dependen
ones o be iden i ied online. The absolu e s eam con-
sump ion (ASC) o a plan is compu ed by mul ipli-
ca ion o (1) and (2):
XXXVIII Jo nadas de Au omá ica
576
⋅ (3)
In his way, gi en a ouling s a e  and a cooling
wa e empe a u e , he maximum and minimum
e apo a ion capaci ies, deno ed by  and  e-
spec i ely, o each plan can be compu ed by (1)
wi h he accep able ope a ing anges o  and :
,,,
,,, (4)
The goal o he selec ion o he ope a ing poin is
minimal speci ic s eam consump ion ul illing he
e apo a ion demand ( ed on in Figu e 2). A sel -
op imizing con olle (SOC) was implemen ed o
ensu e ha ope a ion always lies in his egion. The
con olle maximizes he p oduc empe a u e  o i s
uppe limi and adjus s he ci cula ing low  o
achie e he equi ed e apo a ion low [4]. Thanks o
his op imal ope a ion pa e n, we a e able o com-
pu e he con ol alues co esponding o he ed
bounda y in Figu e 2 gi en a desi ed  and an es-
ima ed s a e o ouling : indeed no e ha he  is
se o i s uppe bound and  is se o he lowe one
achie able by he cooling owe , so  can be com-
pu ed di ec ly om (1) and, hus, he SSC om (2).
3 MODELLING ROUTINE
The model iden i ica ion ask is implemen ed in
MATLAB and comp ises a da a ea men o emo e
inconsis en measu emen s, iden i ies s ep changes
and pe o ms an i e a i e i ing o he model pa-
ame e s. The equi ed da a o m he e apo a o s a e
ob ained om he PI sys em ia an OPC-connec ion
and addi ional in o ma ion such as he ime window
o iden i ica ion, ag labels o he measu emen s in
he his o ian, minimal numbe o changes in he EF,
accep able noise band in s a iona y ope a ion, la ges
ansi ion pe iod du ing s ep change, o he ime
window o alida ion is p o ided by he ope a o
wi h s anda dized Excel shee s. Finally, he quali y o
he model is assessed by a compa ison o he model
p edic ions wi h he measu ed EF and SSC.
3.1 DETECTION OF STEP CHANGES
Fo he modeling o he s a iona y pa , he con ibu-
ion o ouling mus be emo ed om he aining
se . This is achie ed du ing he model i ing p ocess
bu equi es da a om ope a ional poin s ha a e
subjec o he same deg ee o ouling. Thus, he ool
iden i ies changes in he EF, because ope a ion poin
a ies enough o iden i y he pa ame e s and we can
assume ha he ouling s a e does no a y signi i-
can ly in one day.
In ha way, he da a is scanned o s ep changes as
shown in Figu e 3, p o iding in e als a and b. S ep
changes a e iden i ied in he case ha : da a in a is a
s eady s a e (wi hin a h eshold); s ep change is la ge
han he h eshold c and; he ansi ion be ween he
wo s eady s a es is comple ed wi hin in e al b. Each
o he iden i ied s ep is eco ded and ansla ed in o a
da a pai by a e aging he measu ed alues be o e
and a e he change. O cou se, enough changes
p o oked by , and  a e equi ed o a eliable
iden i ica ion o he pa ame e s in (1) and (2).
Figu e 3. S a iona y ope a ion be o e and a e he
s ep (blue bounds), minimal s ep heigh ( ed a ow),
maximal ansi ion in e al (do ed lines).
3.2 PARAMETER ESTIMATION
Based on he assump ion ha he ouling is di e en
om s ep o s ep bu emains cons an du ing he s ep
i sel , pa ame e s , can be es ima ed by com-
pa ison o he model p edic ions (
,
󰇜 wi h he
ac ual alues (SSC, EF). The esul ing ouling ac o
is subsequen ly used o bo h ope a ing poin s ha
a e conside ed (be o e and a e s ep changes).
Hence, an i e a i e LS op imiza ion o e he o e all
da a se a ises, which is yields an in e media e se
o s eady-s a e pa ame e s , used a e wa ds o cal-
cula e new alues o , be o e sol ing an upda ed
LS op imiza ion o ind he nex model gene a ion.
These i e a ions con inue un il he esidue  (objec-
i e unc ion) does no imp o e any mo e.

󰇛,󰇜


 

 


 (5)
He e  is he se o alues o he manipula ed a ia-
bles ,  and ,  is he numbe o iden i ied s ep
changes and ,  a e no malizing ac o s. The iden-
i ica ion p ocedu e is summa ized in Algo i hm 1.
Algo i hm 1. Pa ame e es ima ion o e apo a ion plan s.
1. P o ide an ini ial guess o  and se 0.
2. A e age he measu ed s eady-s a e alues o he
SSC and EF be o e and a e he s ep change.
3. Adjus ouling ac o s  by compa ing he SSC
and EF om S ep 2 wi h he model p edic ion.
4. Minimize (5) wi h  as decision a iables o ind
he bes i o all s ep changes.
5. I  se 1 and go o S ep 3, else
he algo i hm s ops.
XXXVIII Jo nadas de Au omá ica
577
In p ac ice, o a easonable ini ial guess, he model
pa ame e s  con e ge a e a ew i e a ion s eps.
3.3 VALIDATION
The alida ion s ep in he modelling ou ine is pe -
o med o assess he quali y o he model on he basis
o an independen se o s ep changes ha has also
been ob ained acco ding o Sec ion 3.1. Fo each
iden i ied s ep change, he ouling ac o is also ad-
jus ed in he models o ma ch he a e age alues o
eco ded da a be o e he load change. The model
wi h he upda ed ouling ac o s is hen used o simu-
la e he plan o he same inpu s applied du ing
change. The esul ing absolu e e o is hen no mal-
ized wi h he heigh o he s ep change in he ASC, o
yield a ela i e measu e o he model e o .
The obse ed ela i e e o s a e ypically below 10%.
These alues we e accep able, since he ne wo k
op imiza ion is pe o med pe iodically and mis-
modelling is educed om un o un by an online
es ima ion o he ouling s a e. In some cases ela i e
e o s o up o 30% ha e been obse ed due o a poo
choice o load changes (e.g. non-s a iona y ope a ion
alsely iden i ied as s eady s a e). A manual selec ion
o s ep changes, choosing an al e na i e modeling
ho izon, o an adjus men o he modeling se ings
was su icien o imp o e he model i .
4 NETWORK OPTIMIZATION
The objec i e is he minimiza ion o he ASC o he
en i e ne wo k, gi en a desi ed e apo a ion demand.
The o e all ASC is calcula ed as he sum o (3) o
all e apo a o s. Two main ac o s which a ec he
ASC a e objec o op imiza ion: he load alloca ion
and he cleaning policy.
4.1 OPTIMAL ALLOCATION
Fi s , gi en a se o ∈ p oduc s o be p ocessed
in ∈ e apo a ion plan s, he p oblem is o allo-
ca e plan s o p oduc s and hen dis ibu e he e-
qui ed o al demand pe p oduc  in a way ha he
o e all ASC in he ne wo k is minimized. Two se s
o decision a iables a e de ined o his aim:
x : Bina y a iables which link he p oduc  o
he plan .
x : Real a iables de ining he e apo a ion
low o be achie ed in a plan  p ocessing he
p oduc .
Now, ecalling (4), assuming ha he ouling s a e 
o each plan will be es ima ed,  is measu ed and
con olled, a se o maximum and minimum capaci-
ies o each plan ∈ is p o ided. Mo eo e , ol-
lowing he op imal con ol pa e n explained in Sec-
ion 2.2 o se ing  o each plan o i s uppe limi
, om (1)-(3) we ge :

(6)







 (7)
Thus, eeding his in o ma ion, he op imal alloca ion
o p oduc s o plan s is ound by sol ing he mixed
in ege quad a ic p og amming p oblem below:
min
,

≔
∈∈ s. .: (8)
1
∈ ∀∈ (9)

∈ ∀∈ (10)
⋅ ∀∈,∀∈ (11)
⋅ ∀∈,∀∈ (12)
0 󰇛,󰇜∉

(13)
Whe e  is cons ained in (13) o ind easible
solu ions wi hin he se  o allowed connec ions
be ween plan s and p oduc s.
4.2 CLEANING SCHEDULE
A complemen a y op imiza ion is p oposed o deal
wi h he issue o ouling, which akes ad an age o
he al eady de eloped decision suppo : once op imal
e apo a ion se poin s a e compu ed o each plan ,
he idea is o sugges he nex cleaning cycle by bal-
ancing he cos s o ope a ion o e ime wi h he
cleaning cos s in an op imal ashion.
This ask equi es models o he e olu ion o he
ouling o e ime. Ex ensi e expe imen al es s ha e
been pe o med measu ing he SSC in he e apo a-
o s unning a e e ence ope a ion poin s be ween
consecu i e cleaning cycles. This allows isola ing he
e ec o ouling on he SSC inc ease, hence meas-
u emen s a e compa able. In his way, app oxima e
linea e olu ions o he ouling beha io could be
iden i ied by eg ession, see Figu e 4.
Thus, he ouling con ibu ion  in (1)-(2) becomes:
󰇛󰇜α⋅ (14)
Whe e  s ands o he ime (in days) ha a plan is in
ope a ion,  is he ini ial o cu en es ima ion o
XXXVIII Jo nadas de Au omá ica
578
he ouling s a e, and  is he slope o he linea
model. In his way, p edic ions o he u u e SSC
(hence cos s) can be compu ed gi en a desi ed .
Figu e 4. Measu ed e olu ion o he SSC ( ed) and
ou pu o he eg ession model (blue).
In o de o lump esou ces o di e en na u e (s eam,
manpowe , cleaning p oduc s, e c.) in a single e i-
ciency indica o , an agg ega ion based on cu ency is
used. Hence, using p ices and cos s o u ili ies, he
No malized A e age Cos pe Time (NAC) is de ined
as an REI, and indica es he uni a y cos (€/d) in-
cu ed o ope a e a plan be ween wo consecu i e
cleaning asks (ope a ion cycle):
≔󰇡∑󰇛󰇜⋅

 Δ⋅
Ws Wa ⋅Δ⋅⋅1.1/ (15)
He e  is he sugges ed u u e day o pe o m he
cleaning ope a ion, Δ is he ime equi ed o com-
ple e a cleaning ope a ion, and , and 
a e he cos s o he esh s eam, manpowe and was e
wa e . No e ha , once he EF o each plan is se om
(8)-(13), he 󰇛󰇜 is compu ed ia (7) and (14).
No e also ha when an e apo a o is s opped o
cleaning, i s load mus be assumed by o he s, so an
app oxima e cos ac o o a 10% inc ease o e he
nominal ope a ion cos is added in (15).
The NAC is o be minimized wi h espec o  o
each e apo a o o compu e a pe iodic “indi idually
op imal” cleaning policy, which a emp s o be a
“nea ly op imal” one o he whole ne wo k. Howe -
e an issue appea s in using (15) as objec i e unc-
ion: he cos o ope a ion is a disc e e sum which
ge s  e ms, being  unknown a p io i, as i is deci-
sion a iable. To exp ess his cos in a sui able way,
we make use o he o mula ound by Gauss in he
la e 1700’s o his ype o a i hme ic se ies [6]:
⋅123⋯⋅1⋅
2(16)
Mo eo e , he e a e wo ypes o cleaning asks,
deno ed by  (big) and  (small), eaching di e en
eco e ies , booking di e en imes Δ and
using mo e o less was e wa e . Thus, each ask will
ge di e en ixed cos s in he NAC (15) so he op i-
mize mus choose which op ion minimizes he cos s.
Thus, he p oposed economic op imiza ion o p edic
he op imal cleaning policy o one plan eads:
min
,

≔⋅|󰇛1󰇜⋅| (17)
s. .: 01; 0 (18)
He e no a ion | s ands o (15) e alua ed wi h
alues Δ,  and Ws Wa co esponding o a big
cleaning ope a ion (| is analogous o a small
cleaning). No e ha his op imiza ion o choose be-
ween disc e e al e na i es can be handled ia NLP
because (17) is mono onous w. . . , so i s minimum
is loca ed in an ex eme, ei he 0 o 1. In
his way, he bes cleaning (big o small) is chosen.
5 SYSTEM INTEGRATION
The modelling as well as he load alloca ion modules
we e implemen ed using MATLAB®. The cleaning
schedule op imiza ion was coded in di ec ly in MS
Excel. These choices a e jus i ied since he equi ed
licenses and expe ience o he enginee ing depa -
men a Lenzing AG a e a ailable o he sus ainable
main enance o he decision-suppo solu ion.
Figu e 5 depic s a schema o he eal- ime op imiza-
ion (RTO) implemen ed o cope wi h he load allo-
ca ion ask, which is execu ed each 30 min. The use
dashboa d is included as a P ocess Book in he PI
sys em. I shows he esul s and allows o manually
igge he op imiza ion in case o signi ican changes
in he e apo a ion . A e he ac i a ion, he s a ic
in o ma ion (ne wo k in o ma ion, model pa ame e s,
e c) is ead om an Excel in e ace. P oduc ion con-
s ain s change dynamically, so hey a e ei he di ec -
ly supplied by he da a his o ian o in e ed om
measu emen s. Then, an upda e o he ouling pa am-
e e s  and  is pe o med and sa ed o a ile.
The ouling pa ame e s o he inac i e equipmen a e
no upda ed in he ile.
Figu e 5. RTO concep o he e apo a ion ne wo k.
Execu ion o he op imiza ion esul s in he alloca-
ions o e apo a o s o p oduc s, he load dis ibu ion,
XXXVIII Jo nadas de Au omá ica
579

he SSC and ASC o each plan . This in o ma ion is
w i en back o he sys em using special PI-Tags
which a e displayed o he ope a o s ia dashboa d
applica ion in he PI P ocess Book. Hence, he ope a-
o s should adjus he e apo a o loads and alloca-
ions acco dingly.
Howe e , he op imiza ion o he load dis ibu ion
may some imes esul in in easibili y, since app oxi-
ma e models (1) migh sligh ly unde es ima e he
e apo a ion capaci y o some plan s. This si ua ion
migh lead o op imiza ion p oblems ha a e o e all
in easible based on he model p edic ion, e en
hough he eal plan is capable o ul il he desi ed
e apo a ion low.
5.1 HANDLING INFEASIBILITIES
I is impossible o p o ide eliable decision suppo
o he ope a o s wi hou a easible solu ion om he
op imize , because ha d cons ain s migh be iola -
ed. To a oid hese si ua ions, a easibili y check is
pe o med i s , ha e alua es whe he he cu en ly
measu ed e apo a ion low o each e apo a o can
be achie ed wi h he models unde he same ex e nal
cons ain s (wea he , cooling wa e empe a u es and
ne wo k a ailabili y). Then, o he iden i ied in ea-
sible plan s, he MIQP cons ain s (11) a e so en
wi h slack a iables ∈ as ollows:
⋅ ∀∈,∀∈ (19)
Whe e  is he se o plan s which a e iden i ied
in easible a e he easibili y check. Then, he sum
o e all slack a iables is included as a penal y e m
in o he objec i e unc ion (8) as:
min
,,

≔
∈∈ ⋅
∈ (20)
The weigh  is oughly chosen o be g ea e han
he la ges possible alue o (8), i.e., wi hou he
con ibu ion o he slack a iables. Thus, he sol e
will only p o ide he absolu ely necessa y amoun o
cons ain iola ion. No e ha he ne wo k ope a ion
will no esul in cons ain iola ions on he con ol
inpu s, since he SOC is in place o each plan , and
in easibili y is only a esul o a plan -model mis-
ma ch. Mo eo e , a wa ning can be passed o he
supe iso (plan enginee ). Thus, depending on he
se e i y o he plan -model misma ch, co ec i e
ac ions can be aken, e.g., a model upda e acco ding
o he p ocedu e in Sec ion 3.
The inal op imiza ion p oblem can be coded in
MATLAB and sol ed wi h an MILP sol e ia suc-
cessi e linea app oxima ions [7], o di ec ly wi h a
MINLP sol e like BONMIN [8] ia he open sou ce
OPTI-Toolbox, al hough his op ion migh be less
compu a ionally e icien .
5.2 DECISION-SUPPORT INTERFACES
The isualiza ion in e ace is adap ed o he al eady
exis ing concep ha was designed o gi e an o e -
iew o he e apo a ion p ocess du ing p oduc ion.
On he one hand, he ope a o s a e supplied wi h he
dashboa d depic ed in Figu e 6 ha shows he com-
pu ed op imal solu ion o he cu en ime. The e -
ical columns ep esen he 23 plan s in he ne wo k
and he ows ep esen 5 p oduc s. Ligh g ey boxes
a e he alloca ion possibili ies o plan s o p oduc s. I
a plan is assigned o one o hese possible combina-
ions, he box becomes g een. Plan s ha a e cu en -
ly assigned o a p oduc bu a e no in ope a ion (un-
de main enance o cleaning) a e shown by ed iles.
Figu e 6. In e ace o he p o o ypical ool o online
op imiza ion o he e apo a ion ne wo k.
The alloca ion plan acco ding o he op imiza ion
esul s is indica ed wi h yellow (pa ial load) o g een
ci cles ( ull load) a he co esponding posi ion in he
ma ix ep esen a ion. The op imal load dis ibu ion
o plan s is di ec ly gi en nex o he cu en alue a
he op o he ma ix. Small pic og ams show he
necessa y di ec ion o he change in e apo a ion se
poin s. On he igh hand side, he cu en and op i-
mal alues o a o al p oduc e apo a ion low and
he ACS a e lis ed. Finally, he p edic ed ne wo k-
wide sa ings po en ial is shown in € sa ed pe hou ,
in o de o c ea e an incen i e o he ope a o s o
apply he p edic ed e apo a ion se poin s o plan s.
On he o he hand, he cleaning p edic ion module o
Sec ion 4.2 has been implemen ed by an Excel-based
ool, pa ially coded in Visual Basic and using he
OpenSol e [9] add-on, whose cu en e sion in-
cludes BONMIN as op imiza ion engine. This ool
complemen s he one abo e, by ecei ing he load
alloca ion o each e apo a o as inpu da a.
The in e ace is o med by se e al shee s: one o
each plan and a gene al o e iew o he ne wo k. In
each plan shee he e is a se o alues o be se :
du a ion o cleaning asks, cos s o esou ces, ene gy
p ices, model pa ame e s and con ol se poin s
, (see Figu e 7). The ool p o ides a bu on in
each shee o igge he op imiza ion (17)-(18), dis-
playing hen when he e apo a o should be cleaned
and which ype o ope a ion is bes , as well as he
cos componen s and cu en alue o he NAC o
he sugges ed policy. Mo eo e , he ool se es also
XXXVIII Jo nadas de Au omá ica
580
as a simula o o wha -i analysis, because he use
is allowed o manually se he nex cleaning day and
he ype o cleaning. In his way, he ool in o ms he
ope a o abou he po en ial losses in €/d incu ed
wi h espec o he op imally compu ed NAC, en-
cou aging him/he o apply he sugges ions.
Figu e 7. In e ace o he p o o ypical ool o he
imp o ed scheduling o cleaning ope a ions.
6 CONCLUSIONS & OUTLOOK
The modelling, op imiza ion and isualiza ion con-
cep s p esen ed in his pape suppo he ope a o s o
ake be e decisions in eal ime o imp o e he ne -
wo k ope a ion. The modelling ool execu es an au-
oma ized model upda e based on his o ical da a and
use inpu s. The esul ing models a e inco po a ed in
he RTO scheme ha sol es a MIQP p oblem acco d-
ing o he cu en p oduc ion cons ain s and he
plan s ouling s a es. The esul s a e isualized in he
daily p oduc ion en i onmen , including p edic ions
o he po en ial mone a y sa ings, incen i izing hus
he ope a o s o apply he ecommenda ions.
Models o long- e m ouling e ec s we e iden i ied
by ex ensi e expe imen a ion, o be hen used in an
economic op imiza ion. The inco po a ion o such
unc ionali y allows inding he bes cleaning policy
o each plan . This p o ides addi ional bene i s in
e ms o ene gy and cos s associa ed o he cleaning.
The de eloped DS ools a e cu en ly unde e alua-
ion a Lenzing AG: he implemen a ion in o he
exis ing sys ems and ope a ional policies is pe -
o med s ep by s ep o ge expe ience in li e es ing
and o ensu e accep abili y om he plan pe sonnel
Abou a yea o no mal ope a ion is equi ed o as-
sess he impac , bu p elimina y es s wi h his o ical
da a e ealed a ound 10% ASC po en ial sa ings.
Fu he imp o emen s in he modelling app oach a e
expec ed i di e en plan models a e used o he
summe and win e pe iods. I he impac assessmen
shows su icien imp o emen , o he heu is ics and
decomposi ions o he op imiza ion p oblems will be
e alua ed o ake in o accoun unce ain y in model
pa ame e s and/o ex e nal ac o s.
Acknowledgemen
This esea ch is unded by he Eu opean Union’s
Ho izon 2020 p og am, unde g an nº 723575, and
by he MINECO/FEDER (DPI2015-70975-P).
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XXXVIII Jo nadas de Au omá ica
581