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Raw Mix Preparation
IdustrilIT
Solutios or the Cemet Idustry
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OptimizeIT Rw Mix Preprtio
OptimizeIT Raw
Mix Preparation
is ABBs answer to
feed quality pro-
blems. Our whole
experience and
know-in the field
of cement produc-
tion and advanced
process control
has been merged
to create real solu-
tions for our cus-
tomers.
Icresed proits (5%10%)
Icresed productio (3%10%)
Eergy svigs (3%7%)
More stable product quality (10%20%)
BEnEfITS
OPTIMIZED PROCESSOPTIMIZED PROCESS
Application Support
Training
Product Support
OptimizeIT
Raw Mix
Preparation
Pre-blending Optimization
Knowledge Based
Solutions Technology
Application
Commissioning
Configured
Strategy
Package
Raw Mix
Preparation
Raw Mix Optimization
Raw Mill Optimization
Wht is OptimizeIT Rw Mix Preprtio?
Theconsequencesofpoorlypreparedrawmeal
arewellknown.Highlimecausesmealtobe
burnedharderandrefractorylifedrops.High
alkalinesmaycausecycloneblockageandrestrict
theuseofthecementproduced.Moisturecontent
risesandsodoesenergyconsumption.Andof
course,oversizemealbringslowreactivityand
burnability.
Asleadersinkilncontrolandoptimizationusing
OptimizeITExpertOptimizer,ABBunderstands
thewoesofill-preparedrawmealenteringthe
kilnandthejoysofwell-preparedmeal.Fluctu-
ationsinthechemicalcompositionofexcavated
rawmaterialsareunavoidableatthestartofthe
manufacturingprocess.However,ifundetected
orleftuncorrected,stablekilnoperationbecomes
difficult.
ThatiswhyABBhasdevelopedOptimizeIT Raw
Mix Preparation (RMP):toofferrawmixquality
assurancetotheleadersofthecementindustry.
OptimizeITRawMixPreparation(RMP)depictsa
comprehensivesetofsoftwaresolutionsthatcover
allstagesoftherawmixblending,fromthequar-
rytoitsgrinding,makingsurethatyourquality
targetsarereachedatthelowestpossiblecost.
RMPisafullyintegratedsolutioninABBsCPM
cementportfolio,consistingofKnowledge
ManagementSystems,LaboratoryInformation
ManagementSystems,AutoLabandofcourseour
solutionsforkilnandmilloptimization.ThusRMP
iscreatingthebasisforthemodulargrowthand
developmentofyoursystem,adaptedtoyour
plantsneeds.
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Three optimizati-
on modules work
in concert to help
the cement plants
to achieve their
desired quality,
cost and safety
targets.
OnlineMeasurement
Composition Control
Limestone/Clay
Composition Control
OnlineMeasurement
RawMill
HomogenizingSilo
Kiln FeedPre-blending
Optimization ModuleRaw Mix
Optimization ModuleRaw Mill
Optimization Module
Sampler
OfflineXRF
How does OptimizeIT Rw Mix Preprtio
work?
RMPachievesthegoalofminimizationoffeed
chemistryfluctuationsatthelowestpossiblecost
byconcatenatingthreestrongfunctionalmodules.
Thesemodulesproducevaluetoourcustomers
asstandalonesolutions,butthemaximumbene-
fitsandsynergiesarereachedwhendeployed
together.Theyconformauniquesolutioninits
strength,performanceandcompleteness.
Pre-bledig Optimiztio
Thequarryfluctuationsaresmoothedearlyin
therawmealpreparationprocess,namelyatthe
pre-blendingbeds.Optimumproportioningof
thedifferentrawmaterialsonthecombinedpre-
blendingbedisachievedwiththeABBPre-blen-
dingOptimizationModule.ModelBasedControl
technologyisusedtoitsfullstrengthinorderto
copewiththechallengesposedbythematerial
propertiesvariabilityandthetimedelaysinherent
tothesystem.
Rw Mix Optimiztio
TheRawMixOptimizationModulereduces
andcontrolsshort-termfluctuationstothetarget
valuesbyoptimizingandcontrollingtherawmeal
materialproportionsintherawmillfeed.Asin
theformermodule,ModelBasedControltechno-
logyplayshereacrucialroletoattainthedesired
qualitytargets.Withthehelpofmathematical
modelsthismoduleisabletoforeseecomingqua-
litydeviationsinthemillorsilos,orforinstance
findremedytofeedersmalfunctions.Thispermits
implementationofpredictiveactionsratherthan
reactiveones.
Rw Mill OptimiztioTheRawMillOptimizationModuleachievesstable
milloperationatthemaximumeconomicproduc-
tionrateforthefineness,moisture,andchemical
compositionrequired.Short-termfluctuationsare
dampened.Theoptimizationthereforesupports
boththeoperatorsandthoseresponsiblefor
qualityalike..
Tirelessly supervises desired
process prmeters
Uchlleged rectio speed
Cosistetly tkes the best decisio
Executes my smll chges s
opposed to ew lrge chges
Immeditely recogizes
borml coditios d cts
ccordigly
BEnEfITS
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Pre-bledig Optimiztio Module
ThePre-blendingProportioningModulebalan-
cestheanalysisvalueswiththecorresponding
quantityvaluesofthecombinedpre-blending
bed.TargetvaluesareusuallyCaOand/orAI 2O3.
Thecrushedmaterialsareanalyzedusinganon-
lineanalyzer,oralternatively,samplesregularly
taken,automaticallyprocessedandanalyzed.In
automaticmodethefeedersreceivecalculatedset-
pointsvalues.
ThePre-blendingModellingModuletracksthe
rawmaterialflow.Amathematicalmodelisbuilt
usingthechemicalcompositionandthelocation
oftherawmaterialinthepre-homogenization
bed.Duringthereclaimingprocess,themodule
deliversthechemicalcompositionofthereclai-
medrawmaterialtorefinetheperformanceofthe
RawMealProportioningModule.
Thecontrolalgorithmsaredesignedtodealwith
longtermdisturbancesmakingsurethatmostof
theproblemscanbecorrectedattheirorigin.On
theotherhand,thesolutionissuchthatamaxi-
mumofrobustnessandreliabilityisguaranteed
atalltimes.
Thismoduleisthefirststeptowardshomogeniza-
tionofthematerialchemistry.Itsaimistoreduce
mediumtermfluctuationsofthematerialproper-
tiesandtopreparethegroundforfurtherimpro-
vementsusingthesubsequentmodulesavailable
inthesystem.
Erly smoothig o log- d
medium-term compositios
Mthemticl modellig o
pre-bleded structures
Correltio with modellig whe
reclimig
BEnEfITS
The Pre-blending
Optimization
Module tackles
quality problems
very early at the
root cause by hel-
ping to achieve
the best possible
bed of raw mate-
rials.
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TheRawMixOptimizationModuleaimsforthe
lowestpossibledeviationsfromthequalitytargets
attheconveyorbelts,themillandhomogenizati-
onsilos.Thisisachievedviaonlinecontrolofthe
weighfeederratesactiveattheplant.
Theoptimizationisadaptedtoproducestable
rawmealcharacteristicsenteringthekiln,using
regularlytakensamplesfromlaboratoryoronline
analysers,feedingadigitalcontrolalgorithm.
Therawmixchemicalcompositioncorresponds
tothequalityrequirementsexpressedeitherby
thespecificlimestandard(LS),silicamodule(SM)
andaluminamodule(AM),orbythepotential
clinkerphasesC2S,C3S,C4AF,C3A.Bothgroups
ofmagnitudescanbederivedfromthemainraw
mixoxidesCaO,SiO2,AI2O3andFe2O3.
Rw Mix Optimiztio Module
Thecontrolalgorithmisbasedonthelatest
controltechnologieslikeModelPredictiveControl
(MPC)usingMixedLogicalDynamic(MLD)
processingandgraphicalmodelbuilding.This
allowsexplicitconsiderationoftimedelays,
actuatordynamics,planttopology,etc.Theresult
isthebesteversolutioninthemarketplace.
Theoptimizationallowstheprioritizationand
tuningofdifferentgoalslikerawmaterialcost
optimizationandachievementofdesiredquality
targets.Italsoallowsyoutoreducethesensitivity
tomeasurementnoise,specifyfeedervariability,etc.
Optimiztio o rw mix chemicl
compositios
Miimize rw mteril costs
Reduce mucturig costs
dowstrem
Itertiolly recogized qulity
stdrds
BEnEfITS
The Raw Mix
Optimization
Module executes
online control of
the weigh feeders
in order to gua-
rantee the optimal
trade-off between
deviations from
quality targets and
material costs.
Based on state-
of-the-art control
technology, it
offers optimal
results and high-
est robustness.
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Rw Mill Optimiztio Module
TheRawMillOptimizationoptioncontrolsboth
thetemperature,thefeedratetothemillandthe
separatorspeedinordertoachievetherequired
throughputforkiln.Wherestartingthemill
requiresdamperstobemoved,tochangegas
flowpaths,themodulewillalsorespondtothese
effectstokeepthesystemstable.
TheRawMillOptimizationModulestabilizesmill
operationandthencontinuouslyoptimizesits
mainprocessvariablesofthroughput,particle
sizeandenergyconsumedrelievingoperators
oftediouscorrectiveactions.Stabilitycontroluses
afeedcontrolstrategytoobtainastablegrinding
process.Freshfeedoptimizationdeterminesthe
millpowerconsumptionsetpointthatgivesthe
highestfreshfeedrate.Finenessandmoisture
controlareincluded.
Theprinciplebywhichthismoduleprovides
benefitsisasfollows.First,stabilizationofthe
keyprocessparametersisachieved.Notethat
themoduleimplementssmallactionsfrequently,as
opposedtotheinfrequentlargeactionstypicalof
humanoperator,theresultisamoresmoothope-
ration,largerproductivity,lesswearandtear,etc.
Inasecondstep,theMillOptimizationModule
movestheprocesstowardsitsconstraints,seeking
optimalsetpointsintheeconomicsensewhilestill
meetingalltheconstraintsoftheprocess.
Processsafetyissuesaretakenintoaccountauto-
maticallymakingsurethattheplanttechnicaland
humanassetsarenotjeopardizedatanypointin
time.
Stble opertio o rw mills
Mximum ecoomic productio rtes
fie tuig o prticle size d mois-
ture cotet
Opertor Support d Triig
BEnEfITS
Rely on the Raw
Mill Optimization
Module in order
to obtain maxi-
mum operational
stability, highest
throughput and
safety.
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RMP Cotrol Techology
Pre-blendingbed
Feeders Raw mill Homogenizationsilo
Quarry
Long termfluctuations
Kiln
Quality
Middle termfluctuations
Short termfluctuations
ZeroShort termfluctuations
Pre-blendingOptimization
Module
Raw MixOptimization
Module
Raw MillOptimization
Module
RMPisbasedonthemostmoderncontroltechno-
logiesavailable.Thesystem
Usesamathematicalmodelsoffeeders,con-
veyorbelts,mills,andsilos,etctopredictinto
thefuturetheeffectofdifferentcontrolmoves
pickstheoptimalonesforapplicationinthe
plant.
Forcreationofthemathematicalmodelalibrary
ofcomponents(feeders,conveyorbelts,silos,
mills)isavailabletoconfigurethecustomerappli-
cation.Thisisdoneusinghighlyefficientgraphi-
caltoolsthatviadraganddropoperationscreate
theoverallplantlayout.Generationoftheoverall
processmodel,optimizationproblemsolvingand
simulationofresultsistakenoverbythesoftware!
RMP sotwre key cts
RMPisbasedonthemostmodernsoftware
technologiesavailable:webservers,thinclients,
graphicalmodelbuilding,OPC,latestWindows
version,etc.Thisensuresmaximumperformance
andlowestpossibleownershipcosts.
DataacquisitionandStorage
Standardinterafacestoprocessandonline
analyzers
IndustryspecificOracledatabasestructure
Databackupandrestorefunctions
Control
Rawmillcontrol
Closedloopcontroloffeedersetpoints
Costminimization
Constraintsatisfaction
HumanMachineInterface
Latestwebtechnology:server-thinclient
architecture
Basicsetofstandardreports,processdis-plays,trendsandmenus
OptimizeIT is
an outstanding
robust solution for
quality issues at the
cement plant. It puts
the most modern
software and control
technology at the
service of our
customers.
Ehced process stbility
Better respose to disturbces
Compestio or delys i
coveyor belts
Hdlig o delys i smplig,
X-Ry lysis, etc.
Recogitio d correctio o weigh
eeder errors
Predictio o moduli vlues i the
mill d the silos
fEaTURES
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3B
HS211545ZAB
E01/Rev
A(07
.06500Pomca
ny
s)
stablecoatinginthekilnwithstablerawmealfedtothekiln
formationoffavourableclinkerphasesgrownfromraw
mealwithconsistentproperties
kilnoptimizationhasfewerfluctuationstocopewith
cementisgroundtohighqualityfromconsistentclinker
qualitywithwell-balancedphases
ABBassuresqualitywithacomprehensivesetofsolutions
fortheautomationandoptimizationofrawmealpreparation.
OptimizeIT Raw Mix Preparation
Rawmixpreparationisthequalitykeycontrolparameter
upstreamforstable,continuousmanufactureofhighqua-
lityclinkerandcement.Downstreamqualityandupto5%
productionincreasesorsavingsoriginatefromABBsqua-
lityassurancesystemOptimizeITRawMixPreparation.The
reasonsareclear:
ConsultABBonhowtooptimizeyourupstreamoperations.
aBB Switzerld LtdCH-0 Baden DttwilSwitzerlandPhone: +1 (0)8 8 8 Fax: +1 (0)8 8 [email protected]
www.abb.com/cement