Proceedirgs
oftir€ l3th SymposilmofMaldysiAnChefricalEnglnee.s
DATA VALIDATION FOR REAL TIME
OPTIMISATION CYCLE
H. w. L€e' and A. Ahmad
Laboratoryof PrccessContol, Fac lty of ChemicalandNatural ResourcesEngineering,
Udveffiry TeknologiMalaysia,81310Sludai,Johor,Malaysia.
'Corespondingaurhor.Phone:+607'5535877.
Fax:+607'5581463
Email:leehw_78@hotrnail.com
ABSTRACT
for RTO implementation,
daiacollectedfrom a processplant mustbe
As a prerequisite
lrcated by three importanldata validationstages,i.e.. steadystate detection,data
andgrosserrordetection
to eliminaleerro$ andinconsistencres.
rcconciliation
This paper
discusses
thed€velopment
of tbedatavalidationsiagefor realtine optimizationin a fatty
acid fraciionation process.To evaluatethe proposeddala validation sysl€m,single and
multiple gross errols in the fom1 of leakagesin processstream were imposed to the
prccess.The resultsproved the capabilityof the proposedlool in identifyingand
reciirying all the grossenors introducedto the process.
Ke)ryords: Data Validation,SteadyStateDetection,Data Reconciliation,
GrossElTor
Detection.
1 INRODUCTION
Processindustneshave beell undergoingsubstantialchangesin order to cop€ with new
challenges fesultiq from high en€rgy and manpower costs, slrict safety and
enviromrenral regulaiions, siringenr product specification and scarcity of reduced
as well as stiffcompetitionfrom new players. Due to thesereasons,
variationfeedstock
real time optimisation (RTO) has been bfought forward for chemical industdes as
potentialsolutionsto the increasinglyinlenseproduction challenge.
Genichi et al. (1994) proposesthat the second stage jn the real iime optimisation
(RTO)cycleis datavalidation.Datavalidationis consisted
of threemajorcomponents,
which are steadystated€tection,datareconciliationand grosserror detection.
RTO moves processesfrom one steady siate operaling condilion to anothersetting
that are more profiiable. Therefore,st$dy siate detectionis required to detenninethe
conditionof the proc€ssand the tlme requiredfof an optimisationcycle. RTO is only
implemeniedif the plant is al sleadystateandthe plant must thereforebe allowedto settle
so thatthe desiredconditionjs obtained.DarbyandWlite (1988)proposed
thatrhetime
period betweentwo real time optimisationexecutionsmust be much longerthan the
processsettling time to ensurethat the processr€tums to steady state operationbefore
optimisation
is conducted
agaln.
Measurcmentsof process variables such as flow lates, pressurcs,levels,
concentations
andtemperatures
in a chemicalplocessare subjectto enor, bothrandom
and gross. These enors are ofien umvoidable and they may foul signals during
processingand lransmissionstages. Randomand grossenors can leadto
measurements,
deteriomtionin the performanceofihe control syst€ms. At the Limil larger eross error
canevennullify gainsachievable
throughprocessopiimisation.Theseenoneous
datacan
evendrlvethe processto unecoromicor unsafeoperatingrcgime. As a result,m€asures
to eliminate lhese effects must therefore be in place to ensure the successof any
optimisation tasks. Data Leconciliation and gross error detection were requir€d to
accomplishlheseimportanttasks.
Data reconciliation is developed to improve the accuracy of measurementsby
reducing the efect of elTorin the daia. Meanwhile, thesefteasurementsafe adjustedto
satisfylhe conservation
laws and rhe balanceconst.ainis(e.g.energy,mass,etc.).This
3- 168
Engineers
Chemical
of Malavsi.n
of theI8thSymposiun
Proceedings
lo .enove any glosseror lrom
by a grosselaordetectionmechanism
was accompanied
2 DATA VALIDATION
2,1STEADY STATEDETECTIONMETHOD
A number of methodshave been proposedto certify lhal the required steady state
conditionis alfeadyachievedpnor to the executionof the oplimisationcvcle ln this
re.tprooosed
re.rdri'ricdr
,nd\.r,'or.ehoasr.Fcorsde,Fd lhefif'r sas 'hecornDo'
qa'
rca'
r'e
m"
hern'
|
hc
1/
r/
lo8or
-heorlol etidence
ui \ar".rrnla,
'econc
"1o
(1q8-)
a/
Nalas]mhan
bl
{MTE)slrsaested
"/
c de'jned
I rn';per' Iendrfer rar'(dl'heoqo e\idFnLe'MTI-''r'ed I\evlf
in rhefollowinge$ationl
'
(1 )
(5'+s )
where ri, is the randon variableswhich obevingthe Hotteling's/ distributionwith
degrcesof freedom2'V-2 A level of
numeratordegreesof freedom1, anddenominator
{x is chosen,and 1i:"-. (o) is calculaiedto be lhe uppervalue of the C
significance,
to be at sleadvslalewith respectto the
distribution.lf tir:7ir,\ r, tireplant is assumed
variablei. If /ir>Ii,r ,, theplantis takenasnot beingal a steadyslatecondiiion'
2.2DATA RECONCILIATION
Data reconciliation is developedto improve the accuracyof measu€menlsby leduclng
areadjustedio satisfythe
the effectof elror in the dala Meanwhile,thesemeasurenents
(eg
rnass) Nonr1al1v,dala
energv,
conse ation laws and the balanceconstraints
that
reconciliaiion
tvpe
ofdata
problen
The
minimisalion
is a conslrained
reconclliation
process
when
only
process
units
involved
is requir€d dependson the problem and
flowates are ieconciled, a linear data reconciliation probiem is applied On the other
alongwith
arereconciled
measur€ments
or pressure
temp€rature
hand,whencomposilion,
nonlinear-data
this
case,
requir€d
In
is
scheme
data
r€conciliation
nowiates,a noniinear
reconciliationis requiredbecauseit is dealing wilh processnonlinearitvsuch as
thermodynamicequilibrium relationshipand complex correlation for the thermodvnamlc
problemcan be
andotherphysicaiproperties.The generalnonlineardatareconclliation
2000):
rbnnulatei asa weightealeastsquareminimisaiion (Narasimlan,
Mi" v(r, i)r t-r(.v - x)
(2J
Subjectto
f (x) =t
c6)<o
.ec'oro equalrl cor'LrJiflsg i( q \ \ecro ofireaualrycon\rmrrht
$fere/isn\
n:,rix,.t i' n r | \elror ol mea'uea !afiable'J' i( n \ I
;. n
16r'ff6..qsrdriance
^'n
measurementsof variables r and t' is n x 1 vector oi
vailles
of
vector of m€asured
of variablest
of
measurements
weighting factor
2.3 GROSSERRORDETECTION
The most comnonly usedmelhod for detectinggrosserror is statisticalhpothesis tesling
a sul.(li. lor'he te'Lwith 'r kno$'roi'r ibtron jnd pe{orndrce
Lh.rreoui.e,e.ec.ine
q
.e5lJaIr'r;Le\ceed'J cri' cal
il he comprrreo
ch,j'acterinic.. gioqse-or 's oec,dred
the statisticalhypoihesistests
others'
Among
value selectedfton a table of disldbuiion
l- r69
Pro.e€dings
oflhe l8tbSymposlun
ofMalarsidChenlcllEnsimers
likelihoodratio test
test (NT), generalized
includeglobaltest (CT), nodalor consrrain!
testO{I),
(GLR),boundedCenetalized
likelihoodmrio (BGLR)merhod,measurements
(MIMT)
test
iterativetneasuremenltesr(IMT) and modified ilerative measuremeni
ii is suitablefor
In this work, the Measwemen!
Test(MT) methodwasusedbecause
(3)
below:
RTO application.Thetestslatisticis asgivenin Equalion
Equation(3) alsocanbe *ritten as:
erors, tris ihe
enors,er is the measurement
measurenenl
Here,:.! is the standafdised
data
and
irr
is
the
measuremenls.
is
the
reconciled
ofmeasurementsJ
deviatiotr
standard
I'
3 PI,ANT SIMULATION
C6,c,i, Cj
L,C
Figure 1. SchemaricdiBgralnof a fany acid fractionationplant
The fatty acid fractionation (FAF) plant considercdin this paper is consistedof tl(ee
of the columnsis illustratedin
packeddistillationcolun s. The sequence
connecting
throughlhree columns,
into
its
components
fatty
acids
arc
separated
Figure L Here, clude
i.e..Pre-cutColumn(PC).Light-culColumn(LC) andMiddle-cutColumn(MC). ln t\e
pi€-cul column, light products(C6,Cs and Cro falry acids) are recoveredjn the overheadThe botlom strealnis then led to the lightcut columr whererhe Cr, falty acid is separated
from ihe rest ofthe fatq?acids. The botlom producl ofthis colurnnthen entersthe middlecut colnn wherethe Crr fatty acid is recovercdleaving$e Cr6and Cls fatty acidsas
by a dynamicsimulationmodel
In this study, the FAF plant was represented
purpose
the
FoI
the
ofRTO inplementarion,
simulaior.
developed
usingHYSYS.PlantrM
The
ibrmer
iook
using
the
same
simulalor.
model
llas
also
developed
requled steadyslale
conditionsif the plant
the role ofthe actuaiplantwhilst the latlerprovidedthe expected
was actllally at a sieadystare.At the sane tiine, the steadystatemodel neededlo provide
sysrenatic search for processoptimisation especially in data reconciliation. Since the
qualiiy ofboth nodels playspivotal role in producingthe jtrtended€nd-results,the models
mnst thereforebe accurateandrobust.
To ensurethe consistencyof the model output,rhe steadystatemodel must therefore
be validated against the dynamic model. The results reveal€d lhat close agreement
betweenthe modelswith an overall diifercnce of lessthan3% lvas obtainedandiherefore
consideredadequarefor this study.
4 EFFf,CT OF GROSSERROR
in this section,$oss elrors were createdby simulating processleak in pipeiines. Ii *as
implem€ntedby le3king the flowrate for the distillale stream at 60 minutes after the
processin steadystate condition. The goss elror was detectedas the disiillate flowrate
was one ofthe variables in d1edala r€conciliation. The amo nts of th€ leakagewere
3-t?0
Elgrneen
Chemical
ofMalavsian
Proceedings
of thel3thSynposium
highly dependenton the dislillate flowrate During implementation'carewas takennot to
letthe disiillateflowrateio dry out. Theamountsofleakagewer€shownintablebelow:
strerm
Table L.Tfe smorntofrhe Leakage
(Ks/h)
41.6519
301.1?80
t24.16-t0
MC L.akase
Ihere weretwo waysof implementalionsThe first involvedcreatinga singLegross
eror by leaking the distillate stream either in PC, LC oL MC colunln The second
approachintroducedmlLitipl€ gross errors by lealdng all the distillate stueamsin the PC,
oflhese bothcasesweresimilar,eitherin
Lt anrtMC columns.The dyrlamicresponses
2 illusfates the dlnamic rcsponseofthe
gross
Figurc
errors situation
single or multiple
1othepla t
gloss
were
introduced
erroN
proaess
whenmuftipie
ofleakageanddistillatesteams
Figure2. The dlaurnicrcsponse
,1.I STEADYSTATEDETECTION
were selected,ie' tbe llowrateof lhe PC distillate
ln this case,6 key measurements
stread, PC botlom siream, LC distillate stream,LC bottom slr€am' MC distillate stream
was collectedover a pedodof30 minutes
and MC bottomsiream. Eachmeasuremenl
theory of evidencemethodwas
mathenatical
The
(dynamic
model)
from the plant oodel
was
used to iest the difference
Hotteling
t'
and
the
iask
Lr"
i"tended
*"J to Ju..v o"t
at 0 05% and the
specified
d
was
significance.
leveL
of
The
the
two
m€ans.
beiween
of r,l, for each
1.
The
value
Equation
iom
r;.--? (a) is 47.04 as determined
is lowerthanthe 7l', , (o). FAf processachievedsleadvstatecondition
measurem€nt
wilhin th€ period of 30 minutes. Therefore' a l0 mi11ltesor bigger cycle time can
thereforeus€tuIfor RTO.
4.2 DATA RECONCILIATION
It is important io reitera!€here tliat the purposeof data reconciliation in this stldy is to
supportthe RTO implementationwhere measureddatamust be reconciledto satisli mass
is at a tue steadystalecoMition
This is doneonly whentheprocess
andenergybalaaces.
be
and any;djustmentrequir€dto the process&!a would madeas smallas possibl€to
3- l l l
Prcce.dings
ofthe1StISyi'tosinmofMalaysian
Chenicet
Elgineeis
guaranieethat lhe reconciledconditionsarc still accuraretyrepresentingrhe rfte operating
condilion.Resultsofthis inplemertarionar€shownin Tabte2.
Table2. Theresultofleakagestudybeibreandafrerdatareconciliation
BeforeDataReconciliarion
402.E6
]l 64.]0
t 78.92
l 952.ti
Alter DataReconciliation
16:1.04
t071.77
816.03
i ri65.t 0
The valueof the objectiveftnction beforedatarcconcitiarion
$.ashightydepenitent
to lhe amountof the distillareiost. It was provenfrom l|e rable 2. The value of the
objectivetunction was large when all d1edisti[ate steamswere ]eakedbefore data
reconciliaiion
is cafied our Ir is thenfollowedby the teat(age
of rheLc_disrinatesteanr.
It is dueto rheamountof rhedisrillaretostwerc grearerifconpared to the losrofpc and
MC distillatestreans.The d-ata
reconciliarion
problem\l,assolved sins the Successive
OrdorarrcP ogrrmn-nCr\QP, dtgo.irhmo o\ ded o\ H\ Sys ;p imi.e". Ddrd
reconciliation
was ableto feducerhesegrossenors and the detectionol the qrcsseffor
wascaniedout by grosselTorderector.
1.3 GROSSERRORDETECTION
Thesame6 measurements
fiar usedin thesreadysatederedionwereadoDted
in thissrudv
.gr n. Tl e pla"rld. ruo f- e.J'rofrnedlrare.oncrtdr:on$ereconpdredba.edor
rr;
"
cnticaivalue C The critical value,C was deteminedbasedon the overa[ siqnificarl!
Ievel,o whichwasspecifiedas 0.05(e.g.95%ot confidentialinte al). andthe v;he was
2.8044from the standardnomal disrribulionwirh accumrtated
probabilityar 0.9989.If
the value test staiistic, ] z, j I exceedsthe citicat value C, then rhis measurementis said
to contain gross €1ror. Ofieruise, this measufemen!is consideredftee of gross en'or.
Measuremenrs
containing
srosserrorarenufkedunderline.
LC-Le.kaee
(Ke/h)
PCBottom
PCDistillate
fC Bo$om
LC Distiuate
MC Bottom
MC Distillate
9400.81
9i3998
r,89.91
9t00.8i
9139.98
61753
446t.71
589.91
448i .59
4904.59
6175l
4461.71
4890.82
29E2.89
l:r94.16
)961.+9
9:100.83
9186.46
622.39
4 4 3 i .i 9
4904.59
2 9 8 2 .8 9
i 4 9 4 .3 6
4890.81
2967.19
r174.77
1414.77
MC-L€akAse
(Kdh)
PCBoton
PCDistdlJte
LC Boftom
lC D i s ti l l a t€
9400.81
637.59
448i.49
4 9 0 4 .1 0
MC Bottod
2983.42
MC Distillate
.lll!.04
9i86.46
622.39
4175.74
4826.07
2959.09
r47|.62
637.59
4181.49
490410
293102
4475.71
1826.07
2959.09
1.170.04
147t.62
ln thePc,leakage
case,one€uor,i.e.,thepC distillateflowrarewasdeteded.On the
otherhand.LC distillateflowratelvasdetecred
containingelTorin the Lcleakage case.
Similarly,fie lvlc distiilateflowratewasidentifiedin Mcjeakage case.On the contrary,
all the distiilareslreamswere detededto containgrosserror whenthese
*"i"
"tthe
"u-s
subJected
to leakage.By compadryto the threesingiegrosserror cases,
distillate
sireamsafter leakagewere the sameas the caseof muliiple grosserror.Alt the gross
enorsin distlllatestreams
wercdetected
eitherin sineleor in D ltjole situarions.
Tle din ll. e
01
e-ch.oll|mn
srs ioer.fied.o, d,r;nggjo". enoruher rhe
'uedn
leakagevalveofthe colunrnwasopened.As menrioned
earti€r,whenihe leakag€vaLve
3,172
ofMalaysian
ftoceedings
ofde lSthSymposium
ChenicaL
Engineers
was opened,th€ leakage strcam started to flow causing lhe redlrction in the distitlate
in the distillat€
flowrat€. The effectof$oss enor causedby the leakagewas detecred
5 CONCLUSIONS
Data validation is crucial to the succ€ssof the rca1 time optimisation. Steady state
detection
hadbe€nappliedto detectthepfocesscondirionbasedon 6 keysneasuements.
hadbeenapplied
Wtrenlhe processachievedits steadysiatecondition,datareconciliation
in orderto tulfil massand energybalances.
to adjustand reconcilethe measurcments
Gross elTor detection was used to identify ihe ristlng of the goss elTor jn th€
measuf€ments.As expected,gross errof was deiectedin the distillate streamwhen the
leakageof the distillationsteam was occrmed.Theseresultsexposedthe abillty of the
dalar€conciliationand grossenors in the neasurements.
ACKNOWLEDGMENTS
This project is tundedby the Ministy of Science,Technologyand the Environment
ttrougl IRPA researchgrant. Our headiest appreciaiionsare for everybody who has
to thesuccess
ofthisproject.
directlyor indirectlycontribute
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