a non-linear mpc strategy for conversion targeting in a...

PSE April 2007PSE April 2007

A nonA non--linear MPC strategy for conversion targeting in linear MPC strategy for conversion targeting in a FCC pilot plant a FCC pilot plant –– Development and online Development and online

implementationimplementation

Chemical Process Engineering Research InstituteChemical Process Engineering Research InstituteCEntrCEntree ffor Research and Technology Hellasor Research and Technology Hellas

Dr. S.S. Voutetakis, CPERI/CERTHDDrr. . SS..S.S. VoutetakisVoutetakis, , CPERI/CERTHCPERI/CERTH

G.M. Bollas, I. Anastasiou, C. ZiogouG.M. BollasG.M. Bollas, I, I. A. Anastasiounastasiou, , C. C. ZiogouZiogou

Department of Automation,Department of Automation,Alexander Technological Educational Institute of Alexander Technological Educational Institute of ThessalonikiThessaloniki

S.A. PapadopoulouS.AS.A.. PapadopoulouPapadopoulou

Department of Mechanical Engineering,Department of Mechanical Engineering,Aristotle University of Aristotle University of ThesssalonikiThesssaloniki

P. SeferlisP. SeferlisP. Seferlis

FCCmodeling & simulation

MPC on FCCexperimental reality

MPC on FCCsimulation study

MPCTheory pros & cons

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FCCindustry & laboratory

CPERI/CERTHCPERI/CERTH

Conclusionsfuture work

Control StructureCommunication

CONTENTSCONTENTS

Fluid Catalytic Cracking Unit Fluid Catalytic Cracking Unit –– Industry & LaboratoryIndustry & Laboratory

FCC Pilot Plant of CPERI FCC Pilot Plant of CPERI –– Modeling & SimulationModeling & Simulation

Model Model –– Based Predictive Control Based Predictive Control –– Pros & ConsPros & Cons

MPC on FCC MPC on FCC –– Simulation StudySimulation Study

MPC on the FCC Pilot Plant MPC on the FCC Pilot Plant –– Experimental RealityExperimental Reality

Control Implementation Control Implementation ––Distributed Distributed \\ InterInter--application Communicationapplication Communication

Conclusions Conclusions –– Future WorkFuture Work


FLUID CATALYTIC CRACKINGFLUID CATALYTIC CRACKINGINDUSTRY & LABORATORYINDUSTRY & LABORATORY

Model Predictive Control of a Fluid Catalytic Model Predictive Control of a Fluid Catalytic Cracking Pilot PlantCracking Pilot Plant





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FeedstocksFeedstocks from from straight run distillates straight run distillates and vacuum gasand vacuum gas--oils to oils to heavy residuesheavy residuesZeolithicZeolithic Catalysts Catalysts extremely activeextremely activeRiser residence times Riser residence times of a few secondsof a few secondsProducts of high value Products of high value (gasoline, LPG, diesel)(gasoline, LPG, diesel)Products with high Products with high concentrations in concentrations in pollutantspollutants

The Workhorse of RefineryThe Workhorse of Refinery

FCCindustry & laboratoryFCCindustry & laboratory

FCC PROCESSFCC PROCESS

Economic Interest:Economic Interest: ►► OptimizationOptimization ►► Process ControlProcess Control

Research Interest:Research Interest: ►► SimulationSimulation ►► ComplexityComplexity

▼▼High interest High interest

in optimizationin optimization





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The Focus in ProfitThe Focus in ProfitFCC CONTROLFCC CONTROL

FCC still rules the FCC still rules the refinery worldrefinery worldFCC control means FCC control means increasing the profitincreasing the profit

Economic Interest:Economic Interest: ►► OptimizationOptimization ►► Steady State OptimalitySteady State Optimality

ProcessProcess InterestInterest:: ►► OperabilityOperability ►► StabilityStability


Easy? NO!Easy? NO!▼▼

High uncertaintyHigh uncertaintyNonNon--linear dynamicslinear dynamicsMany disturbances Many disturbances Large number of Large number of objectivesobjectivesDifficulty in developing Difficulty in developing accurate modelsaccurate models

Crude ATMDist

ThermalProcessing

ResidualUpgrading

FCC

ReformingPolymerization

HF Alkylation

HDT

HDT

VacuumDist

HGO

HDT

Lube OilProcessing

Naphtha

LVGO

HVGO

Coke

Asphalt

Lube Oils

FG & Coker Gasoline

HC Mid Distillates

HC Gasoline

C1-C4

ASOAtmospheric Jet

Atmospheric Diesel

FCC Gasoline

CSO

Trea

tmen

t & B

lend

ing

Refinery

LPG

Jet Fuel

Diesel

Unleaded

Fuel Oil

Gasoline

Fuel Gas

Hydrocracking

PolyGasolineAlkylate

IsomerizationIsomerate

Reformate





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FCC MODELINGFCC MODELINGHalf a Century and still EvolvingHalf a Century and still Evolving

Different types of UnitsDifferent types of UnitsMass Mass -- Energy Energy --Pressure BalancedPressure BalancedMultiMulti--reactor recycled reactor recycled systemsystemCatalytic process Catalytic process --diffusion phenomenadiffusion phenomenaComplex fluid Complex fluid dynamicsdynamicsUnknown Unknown -- empirical empirical reaction kinetics reaction kinetics NonNon--linear dynamicslinear dynamics

Steady State:Steady State: ►► Product SlateProduct Slate ►► Unknown kineticsUnknown kinetics

Dynamic OperationDynamic Operation:: ►► Non linearNon linear ►► ComplexComplex






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FCC PILOT PLANTFCC PILOT PLANTResearch ToolResearch Tool

Accurate simulation of Accurate simulation of industrial processesindustrial processesOperation at constant Operation at constant feedstock or catalystfeedstock or catalystOperation within large Operation within large operating spansoperating spansStudy kineticsStudy kineticsStudy dynamicsStudy dynamicsCatalyst BenchmarkingCatalyst BenchmarkingFeedstock Feedstock BenchmarkingBenchmarkingLarge experimental Large experimental database database

Steady State:Steady State: ►► CatalystCatalyst ►► FeedstockFeedstock

Dynamic OperationDynamic Operation:: ►► Wide rangesWide ranges ►► Process recordingProcess recording

GC

STABILIZERREGENERATOR

RISER

STRIPPER

S.V.-301

S.V.-101

F-501

H.E.-501

D-502

PCV-501

C-501H.E.-601

BPR-601

GC

V-501

FEED VESSEL P-51

V-602V-603

LCV-2

LCV-1

V-604

PRODUCT VESSEL

WTM-1

WTM-2

PCV-601

LC-2 LI-2

BPR-501

D-601

LI-1 LC-1

PC-601 PDT-601 PDT-501 PC-501

F(T1,T2,T3) TR

PDT-301

DPC-301

D-601FCCindustry & laboratoryFCCindustry & laboratory





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IdiosyncrasyIdiosyncrasyFCC PILOT PLANTFCC PILOT PLANT

Online measurement of temperatures and flue gas Online measurement of temperatures and flue gas compositioncompositionAnalytical data for initial and final steady states for Analytical data for initial and final steady states for evaluation of the simulator steady state evaluation of the simulator steady state performance performance Use of thermal zones to control riserUse of thermal zones to control riser--regenerator regenerator temperaturetemperaturePseudoPseudo--isothermal operation of riserisothermal operation of riserPseudoPseudo--adiabatic operation of regeneratoradiabatic operation of regeneratorInclude thermal load in the energy balances Include thermal load in the energy balances

Examine unit responses in openExamine unit responses in open--loop operationloop operationExamine unit responses in closedExamine unit responses in closed--loop operationloop operationStep change in feed rateStep change in feed rateStep change in feed preheat temperatureStep change in feed preheat temperature

Pilot plant characteristics:

Study of pilot plant dynamic behavior:






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FCC Group: Catalyst Evaluation FCC Group: Catalyst Evaluation FCC PILOT PLANTFCC PILOT PLANT


FCCPILOT PLANT

STEAMERPILOT PLANT

CPSPILOT PLANT

CATALYSTCHARACTERIZATION

PILOTPLANTS/

UNITS

PILOTPLANTS/

UNITS

Gasoline yield at 65% conversion

4041424344454647484950

1 2 3 4 56 7

Catalyst%

wt g

asol

ine

SCT-MAT Results, T=560°C

EXAMPLE:EXAMPLE: CATALYST ACTIVITY IN FCC PILOT PLANTCATALYST ACTIVITY IN FCC PILOT PLANT

50

55

60

65

70

75

80

0 2 4 6 8 10 12 14 16

C/O

Con

vers

ion,

%w

tCat. ACat. BCat. C



FLUID CATALYTIC CRACKINGFLUID CATALYTIC CRACKINGINDUSTRY & LABORATORYINDUSTRY & LABORATORY

üü FCC is the workhorse of refineryFCC is the workhorse of refineryüü High economic importanceHigh economic importanceüü High interest in optimizationHigh interest in optimizationüü FCC pilot plants serve as research toolsFCC pilot plants serve as research toolsüü Need to improve pilot plant efficiencyNeed to improve pilot plant efficiency


FLUID CATALYTIC CRACKINGFLUID CATALYTIC CRACKINGMODELING MODELING -- SIMULATIONSIMULATION






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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTRiser Reactor KineticsRiser Reactor Kinetics

( ) ( ) :RS exp100

xnx x xC

x RX

y k EC catalyst type F feed quality ty WHSV RT

−= −

( ) ( ) :RSexp c

x

nc cc c c C

RX

k Ey C catalyst F feed tWHSV RT

−=

( ) ( ) ( )( ):RS :RS

:RS:RS:RS :RS :RS :RS :RS :RS

3600, 3600 1 1 1

F FC

Cp D D C C F F

WHSV W WtWHSV Wρ V ε V ε V ε

= =− + − + −

& &

&

üü Final correlation for the prediction of feed conversion (2Final correlation for the prediction of feed conversion (2ndnd order kinetics):order kinetics):

üü Final correlation for the prediction of the catalytic coke yieldFinal correlation for the prediction of the catalytic coke yield::

üü Expressions for the calculation of the Weight Hourly Space VelocExpressions for the calculation of the Weight Hourly Space Velocity, the ity, the catalyst residence time and the riser temperature:catalyst residence time and the riser temperature:

î Assumption of pseudo-plug flow conditionsî Assumption of pseudo-isothermal riser operation

FCCmodeling & simulationFCCmodeling & simulation





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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTRiser Reactor Fluid DynamicsRiser Reactor Fluid Dynamics

Averaged top voidage:

Slip factor correlation:

üü In highIn high--density CFB a "Dense Suspension density CFB a "Dense Suspension UpflowUpflow" " regime is observed:regime is observed:

üü The slip factor is a function of the riser diameter, The slip factor is a function of the riser diameter, gas volumetric flow and solids terminal velocity:gas volumetric flow and solids terminal velocity:

: :RS :RS:RS

:RS :RS : :RS :RS

g F p FF

F C g D p F

εu ρ A

y W u ρ A=

+&

0.41:RS : :RS2

: :RS

5.61 0.47F t Fg F

y FrFr

= + +

1/: :RS

:RS:RS

zg D

Dt

uu

ε

=

Averaged bottom voidage:

üü The The voidagevoidage of the riserof the riser--bottom (mixing) section bottom (mixing) section estimated by the correlation ofestimated by the correlation of Richardson Richardson && ZakiZaki::

Hb

Ht

Hc

feedstock

nitrogen

regenerated catalyst

ΔP






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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTRiser Reactor Heat BalanceRiser Reactor Heat Balance

( ) ( )2 21 2 3 1 2 3ln

100x

crack RX RX F RX RX Fx

yH a T a T a MW bT b T b MWy

∆ = + + + + + −

üü Main contributors to the overall heat balance in an FCC riser:Main contributors to the overall heat balance in an FCC riser:þ The enthalpy of cracking ΔHcrackþ The enthalpy of vaporization of the feedstock ΔHvapþ The enthalpy content of various process streams

üü The heat of cracking is a function of:The heat of cracking is a function of:þ Conversion (yx)þ Feed Molecular Weight (MWF)þ Riser Temperature (TRX)


- 0crack vap gas oil cat LossH H H H H∆ + ∆ + ∆ + ∆ + ∆ =





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bubblephase

emulsionphase

freeboard

gasinterchange

gas tofreeboard

entrainedsolids

combustion air

cokedcatalyst

excess gas

cyclones

regeneratedcatalyst

TWO PHASE MODELTWO PHASE MODELMODEL ASSUMPTIONSMODEL ASSUMPTIONS

MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTRegenerator ModelRegenerator Model

üü Two phase flowTwo phase flow: : 11) ) dilutedilute phasephase22) ) dense phasedense phase

üü Two phases in Two phases in dense beddense bed: : aa) ) emulsionemulsionbb) ) bubblesbubbles

üü EmulsionEmulsion:: CSTR CSTR HeterogeneousHeterogeneous

üü BubblesBubbles: : PFR PFR HomogeneousHomogeneous

üü Dilute phaseDilute phase: : PFRPFRHeterogeneousHeterogeneous

The dense bed includesThe dense bed includesa bubble and an emulsion phasea bubble and an emulsion phase

Emulsion Phase Emulsion Phase èè Fully MixedFully MixedHomogeneousHomogeneous--HeterogeneousHeterogeneous ReactionsReactions

Dilute Phase Dilute Phase èè Plug FlowPlug FlowHomogeneousHomogeneous--HeterogeneousHeterogeneous ReactionsReactions

Bubbles Phase Bubbles Phase èè Plug FlowPlug FlowHomogeneousHomogeneous ReactionsReactions






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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTRegenerator ModelRegenerator Model

Dilute phase:

( ):RG

:RG

1

1

homoib

Mi b ij RjbjD D

homob

H b Rj RjbjD D

dF K f a KV dl

dQ K f H KV dl

= − +

= − + −∆

∑

∑

( ) ( )

( )

( )( ) ( ) ( ) ( )

( )

( )

0 10

:RG 0

11 1 1:RG :CY

:RG :RG

1

1 1

1

D D

FD D F

l l homo hetegeie ie ie

e e Mi D e e ij Rje e e ij Rjej jge D

ll l l heteif ieie C ie ie C

e e e e ij Rjejp D p D

solids

e e ie ie ei

Wdc c cf K dl f a K f a Kdt V

c cdc W c c Wf f a Kdt V V

f c cp f

ε ε ερ

ε ερ ρ

ε

= =

== = =

−= + + + −

−−− = + + −

− +

∑ ∑∫

∑

∑

&

& &

( )

( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ( )

:RG

1 0 1 0 0 1:RG :RG :CY :CY

1

:RG :RG :RG0

1

D D F F D D

gasD e

e ie iei

l l l l l lC C C C ge ge loss

homo hete

D H D e e D Rj Rje e e D Rj Rjej j

d V Tc cp

dt

Q Q Q Q Q Q Q

V K dl f V H K f V H K

ε

ε ε

= = = = = =

=

− + − + − − +

+ −∆ + − −∆

∑

∑ ∑∫

( )

( )

( ) ( ) ( )

:RG

:RG

:RG

1 1

1 1

1 1

homo heteif

f ij Rjf f ij Rjfj jF F

heteif

f ij RjfjF F

homo hetef

f Rj Rjf f Rj Rjfj jF F

dFK K

V dldF

KV dl

dQH K H K

V dl

ε α ε α

ε α

ε ε

= + −

= −

= −∆ + − −∆

∑ ∑

∑

∑ ∑

Emulsion phase:

Bubbles phase:


emulsion bubble

freeboard

(t) (l)

(l)





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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTStripper ModelStripper Model

Mass balance:

stripper

disengager

( ) ( )

( )( ) ( )( )

( )

1 0:ST :ST :ST

1 1:ST :ST :ST:ST

:ST

1

1

D D

D D

l lD C C

p mf

l lC i ii

p mf D

dV W Wdt

W c cdcdt V

ρ ε

ρ ε

= =

= =

−=

−

−=

−

& &

&

üü Perfectly mixed reactorPerfectly mixed reactorüü Minimum fluidization conditionsMinimum fluidization conditionsüü Stripping efficiency 100%Stripping efficiency 100%üü Stripper temperature is controlled by heatersStripper temperature is controlled by heaters ST

spT →






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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTSimulator StructureSimulator Structure

ü All dynamic performance is attributed to regenerator and stripperü Ability to simulate the process under open- or closed- loop operationü The regenerator and riser models are responsible for simulator accuracy

ü All dynamic performance is attributed to regenerator and stripperü Ability to simulate the process under open- or closed- loop operationü The regenerator and riser models are responsible for simulator accuracy

regeneratorregenerator riserriser

regenerated catalyst flow update






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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTModels IntegrationModels Integration

ü Assumption of pseudo-steady state operation of riser

ü Assumption of pseudo-steady state operation of liftline and standpipe

ü Dynamic behavior driven by the operation of the regenerator

ü Dynamic behavior includes dynamic performance of the stripper

ü Assumption of pseudo-steady state operation of riser

ü Assumption of pseudo-steady state operation of liftline and standpipe

ü Dynamic behavior driven by the operation of the regenerator

ü Dynamic behavior includes dynamic performance of the stripper


Closed loop operation

òRegenerator slide valve controls riser temperature

Closed loop operation

òRegenerator slide valve controls riser temperature

Open loop operation

òRegenerator slide valve set to constant opening

Open loop operation

òRegenerator slide valve set to constant opening





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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTDynamic Simulation Dynamic Simulation –– Open LoopOpen Loop

î Accurate simulation of riser and regenerator temperatureî Accurate prediction of regenerator excess gas compositionî Accurate simulation of riser and regenerator temperatureî Accurate prediction of regenerator excess gas composition

ØØ Lower consumption of energy Lower consumption of energy for feed vaporizationfor feed vaporization

Higher riser temperatureHigher riser temperature

ØØ Lower coke rate entering the Lower coke rate entering the regenerator regenerator

Less combustion, lower Less combustion, lower regenerator temperatureregenerator temperature

Open-loop operation:15% reduction in feed rate:


simulation n n n experiment





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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTDynamic Simulation Dynamic Simulation –– Open LoopOpen Loop

ì Satisfactory agreement between simulated and experimental resultsí Error in the prediction of regenerator temperatureì Satisfactory agreement between simulated and experimental resultsí Error in the prediction of regenerator temperature

Open-loop operation:130% increase in feed preheat temperature:

ØØ Higher sensible heat in the Higher sensible heat in the feed streamfeed stream

Higher riser temperatureHigher riser temperature

ØØ Small changes in the Small changes in the regenerator input variablesregenerator input variables

Constant operation of Constant operation of regeneratorregenerator







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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTDynamic Simulation Dynamic Simulation –– Closed LoopClosed Loop

î In the closed loop operation changes in riser input variables lead to:ê Oscillation of the unit é Faster responses predicted by the simulator

î In the closed loop operation changes in riser input variables lead to:ê Oscillation of the unit é Faster responses predicted by the simulator

Closed-loop operation:15% reduction in feed rate:

ØØ Control for constant riser Control for constant riser temperature temperature

Reduction in catalyst Reduction in catalyst circulation ratecirculation rate

ØØ Lower coke rate and catalyst Lower coke rate and catalyst rate entering the regeneratorrate entering the regenerator

Less combustionLess combustion, , higher higher catalyst residence times, catalyst residence times, lower regenerator temperaturelower regenerator temperature







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MODELING FCC PILOT PLANTMODELING FCC PILOT PLANTDynamic Simulation Dynamic Simulation –– Closed LoopClosed Loop

î Conventional control robustness shows sluggish behaviorî More robust control can enhance the steadiness in the operationî Conventional control robustness shows sluggish behaviorî More robust control can enhance the steadiness in the operation

Closed-loop operation:130% increase in feed preheat temperature:

ØØ Control for constant riser Control for constant riser temperaturetemperature

Reduction in catalyst Reduction in catalyst circulation ratecirculation rate

ØØ Higher catalyst residence time Higher catalyst residence time in the regeneratorin the regenerator

Increase in the regenerator Increase in the regenerator temperaturetemperature





FLUID CATALYTIC CRACKINGFLUID CATALYTIC CRACKINGMODELING MODELING -- SIMULATIONSIMULATION

üü Experimental database of FCC pilot plant Experimental database of FCC pilot plant üü Steady state riser modelSteady state riser modelüü Dynamic regenerator modelDynamic regenerator modelüü Integrated FCC dynamic simulatorIntegrated FCC dynamic simulatorüü Accuracy verified with dynamic experimentsAccuracy verified with dynamic experiments


MODEL MODEL -- BASED PREDICTIVE CONTROLBASED PREDICTIVE CONTROLTHEORY THEORY –– PROS & CONSPROS & CONS






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MODEL PREDICTIVE CONTROLMODEL PREDICTIVE CONTROLBlock DiagramBlock Diagram

MPCtheory pros & consMPCtheory pros & cons

ü Integral action achieved through disturbance modelü Integral action achieved through disturbance model

MPC PROCESS

MODEL

manipulatedvariables

(u)

measuredoutputs(yi

meas)

predicted outputs(yi

pred)

desired trajectory

(yisp)

disturbance(dk)

error(ek)ˆ iyx θ

ü Dynamic model improves control actions within specified horizonü Dynamic model improves control actions within specified horizon

ü The estimator manages to improve future predictions of the modelü The estimator manages to improve future predictions of the model





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MODEL PREDICTIVE CONTROLMODEL PREDICTIVE CONTROLMPC PrinciplesMPC Principles


Desired trajectory

Model prediction

error, ek+1

tk+1 tk+2 tk+3tktk-1tk-2

rolling control horizon

uk+1 uk+2 uk+3ukuk-1uk-2

Future control actionsPast control actions

ü Past and present control actions affect the future response of the processü Minimize the difference between desired trajectory and predictionsü Long prediction horizon compensates for slower dynamics but…ü Short control horizon leads to aggressive control actions

ü Past and present control actions affect the future response of the processü Minimize the difference between desired trajectory and predictionsü Long prediction horizon compensates for slower dynamics but…ü Short control horizon leads to aggressive control actions





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MODEL PREDICTIVE CONTROLMODEL PREDICTIVE CONTROLDynamic ProgrammingDynamic Programming


ü Performance index:ü Performance index:

( )( )

( )

( ) ( )

1 11

2 2 2

1 1 11 1 1

1 1

1

1

∆+ − + −++ −

+ + + − + − + −= = =

+ − + −

+ + + −

+ −

= − + + −

= −

= +

≤ ≤

= − ∆ = − ∆

∑ ∑ ∑

&

J ˆmin

subject to:

ˆ

/ , /

C CP

y u uk j k jk jk j

N NNsp ss

MPC k j k j k j k j k jj j j

meas predk j k j

predk j k j k j

l uk j

C C k C P P k PN T T t N T T t

w w wuy y Δu u u

x = f x,uy = g x,u

e y y

y y e

u u u

û controlled variablesû move suppression factor

û steady state optimalityü Piecewise additive disturbance model compensates for model errorü Integral action guarantees for zero controller offsetü Piecewise additive disturbance model compensates for model errorü Integral action guarantees for zero controller offset



MODEL MODEL -- BASED PREDICTIVE CONTROLBASED PREDICTIVE CONTROLTHEORY THEORY –– PROS & CONSPROS & CONS

üü MPC improves operability and efficiencyMPC improves operability and efficiencyüü Follow desired trajectory within specified horizonFollow desired trajectory within specified horizonüü Move suppression factor for operabilityMove suppression factor for operability--stabilitystabilityüü Steady state optimality factor for process optimizationSteady state optimality factor for process optimizationüü Need an accurate dynamic modelNeed an accurate dynamic model


MPC on FCCMPC on FCCSIMULATION STUDYSIMULATION STUDY






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MPC on FCCMPC on FCCThe Problem in IndustryThe Problem in Industry

Objectives:Objectives:

Maximum ProfitMaximum ProfitMaximum CapacityMaximum CapacityMaximum ConversionMaximum ConversionConstant TemperatureConstant TemperatureDesired SelectivityDesired SelectivityProduct specificationsProduct specificationsEnvironmental restrictionsEnvironmental restrictionsUnit StabilityUnit Stability

Disturbances:Disturbances:

FeedstockFeedstockCatalystCatalyst

MPC on FCCsimulation studyMPC on FCCsimulation study





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MPC on FCCMPC on FCCThe Pilot Plant Control ProblemThe Pilot Plant Control Problem

Objectives:Objectives:

Reduce Redundant ExperimentsReduce Redundant ExperimentsCatalyst BenchmarkingCatalyst BenchmarkingFeedstock BenchmarkingFeedstock BenchmarkingConstant ConversionConstant ConversionConstant Riser TemperatureConstant Riser TemperatureExamine Catalyst SelectivityExamine Catalyst SelectivityEnvironmental restrictionsEnvironmental restrictionsUnit StabilityUnit Stability

Disturbances:Disturbances:

FeedstockFeedstockCatalystCatalyst






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MPC on FCCMPC on FCCObjective FunctionObjective Function

( ) ( )( ) ( )122

2

/ 11 1 1

ˆˆˆ1 1

y u uk

k

tn n ni kiy u u

k i i i k k isp ssi i ii it

u ty tJ w w u t w

y u

+∆

−= = =

= − + ∆ + −

∑ ∑ ∑∫

Move suppression factor:Move suppression factor: ►► Control robustnessControl robustness

Steady state optimalitySteady state optimality:: ►► Desired steady stateDesired steady state


üü Control horizon: 10 minControl horizon: 10 minüü Prediction horizon: 20 minPrediction horizon: 20 min

Controlled Variables:Controlled Variables: ►► ConversionConversion ►► Riser TemperatureRiser Temperature

Manipulated Variables:Manipulated Variables: ►► Catalyst CirculationCatalyst Circulation ►► Feed PreheatFeed Preheat

Disturbances:Disturbances: ►► Kinetic constantsKinetic constants





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MPC on FCCMPC on FCCThe FCC MPC StructureThe FCC MPC Structure


ü Two instances of the model a “VP” and a “SIM”ü “VP” was depicted by a flawless version of the modelü Significant amount of mismatch in the reaction kinetics used in the “SIM”ü Equivalent to the control problem in the real pilot process level

ü Two instances of the model a “VP” and a “SIM”ü “VP” was depicted by a flawless version of the modelü Significant amount of mismatch in the reaction kinetics used in the “SIM”ü Equivalent to the control problem in the real pilot process level

ˆ iy





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MPC on FCCMPC on FCCProblem DescriptionProblem Description


üü Disturbance:Disturbance: unknown catalyst quality unknown catalyst quality different kinetic constantsdifferent kinetic constants

Conventional PID Control

òManipulate catalyst circulation rate to Control riser

temperature

Conventional PID Control

òManipulate catalyst circulation rate to Control riser

temperature

Model Predictive Control

òManipulate catalyst circulation rate and feed preheat

temperature to Control riser temperature and conversion

Model Predictive Control

òManipulate catalyst circulation rate and feed preheat

temperature to Control riser temperature and conversion





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MPC on FCCMPC on FCCResults on Simulation Basis Results on Simulation Basis –– control actionscontrol actions


ØØ The MPC lowered the The MPC lowered the catalyst circulation rate and catalyst circulation rate and increased the feed preheat increased the feed preheat temperaturetemperature

Solution of the dynamic Solution of the dynamic problemproblem

ØØ The MPC led the The MPC led the ““VPVP”” to a to a state of lower riser state of lower riser temperature and then waited temperature and then waited for the dynamics of the for the dynamics of the process, while making small process, while making small control actions to control actions to compensate for the compensate for the mismatch between the mismatch between the ““VPVP””and the and the ““SIMSIM””

Use of the information of the Use of the information of the prediction horizonprediction horizon





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MPC on FCCMPC on FCCResults on Simulation Basis Results on Simulation Basis –– controlled variablescontrolled variables


←

←

spxy

spRXT

ØØ 1% higher feed conversion, 1% higher feed conversion, riser temperature 1% above riser temperature 1% above its set pointits set point

Initial states mismatch Initial states mismatch between between ““VPVP”” and and ““SIMSIM””, , because of the disturbance because of the disturbance introducedintroduced

ØØ Control actions for 30 min. Control actions for 30 min. In the final steady state both In the final steady state both the feed conversion and the the feed conversion and the riser temperature criteria riser temperature criteria were fully satisfiedwere fully satisfied

Controller robustness.Controller robustness.Solution times close to the Solution times close to the real experimental timesreal experimental times

simulatorvirtual process





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MPC on FCCMPC on FCCResults on Simulation Basis Results on Simulation Basis –– process dynamicsprocess dynamics


ØØ The lower catalyst circulation The lower catalyst circulation rate led to lower coke yield, rate led to lower coke yield, but higher overall ratio of but higher overall ratio of coke rate over catalyst rate coke rate over catalyst rate entering the regeneratorentering the regenerator

Less coke mass and more Less coke mass and more cold catalyst enter the cold catalyst enter the regeneratorregenerator

ØØ Increase in the regenerator Increase in the regenerator temperature and eventually temperature and eventually the riser temperaturethe riser temperature

FCC is a recycled FCC is a recycled interdependent systeminterdependent system






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MPC on FCCMPC on FCCResults on Simulation Basis Results on Simulation Basis –– flue gasflue gas


ØØ The regenerator flue gas was The regenerator flue gas was not significantly influenced by not significantly influenced by the sequence of control the sequence of control actions actions

Large excess air supplyLarge excess air supply

ØØ Practically zilch CO and very Practically zilch CO and very low SOlow SO22 emissionsemissions

Operation in full combustion Operation in full combustion modemode




MPC on FCCMPC on FCCSIMULATION STUDYSIMULATION STUDY

üü Control riser temperature and feed conversionControl riser temperature and feed conversionüü Equivalent to the pilot process control problemEquivalent to the pilot process control problemüü Robust control schemeRobust control schemeüü MPC established the desired steady state in 40 minMPC established the desired steady state in 40 minüü Improve pilot plant efficiencyImprove pilot plant efficiency


MPC on the FCC PILOT PLANTMPC on the FCC PILOT PLANTEXPERIMENTAL REALITYEXPERIMENTAL REALITY






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MPC on the FCC PILOT PLANTMPC on the FCC PILOT PLANTObjective and meansObjective and means

MPC on FCCexperimental realityMPC on FCCexperimental reality

PP FCC

Processing Processing StationStation

ReportsReports

UserUserUserUser

LIMSLIMS

Collection System

Analytical Instrument

GCGC

MPCconventional

control





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MPC on the FCC PILOT PLANTMPC on the FCC PILOT PLANTEKF for model state and parameter estimationEKF for model state and parameter estimation

MPC on FCCexperimental realityMPC on FCCexperimental reality

PP FCC

Analytical Instrument

GCGC

MPCEKF

î Extended Kalman Filter î Extended Kalman Filter

î Use Analytical Instrument Data î Use Analytical Instrument Data

Improve model accuracy

Process optimization

On-line Optimization


CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONDISTRIBUTED / INTERDISTRIBUTED / INTER--

APPLICATION COMMUNICATIONAPPLICATION COMMUNICATION






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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONInfrastructure and Communication ArchitectureInfrastructure and Communication Architecture

RealReal--Time Control Framework :Time Control Framework :ü Key concept : Automated real-time and flexible control schemeü Multi level framework that handles/transfers data from distributed

applicationsü Include technical aspect as well as communication issues

ü Key concept : Automated real-time and flexible control schemeü Multi level framework that handles/transfers data from distributed

applicationsü Include technical aspect as well as communication issues

Control StructureApp CommunicationControl StructureApp Communication

Procedure requirements :Procedure requirements :ü Main focus : shift from a static user required environment to a

dynamic runtime behaviorü Improve the procedure to minimize the time spent for repeated user

actionsü Independent software interconnection and Data sharing through

networkü Time specific actions synchronization and handling of unpredicted

model execution timeü Proper Procedure steps interpretation in action that could be coded

ü Main focus : shift from a static user required environment to a dynamic runtime behavior

ü Improve the procedure to minimize the time spent for repeated user actions

ü Independent software interconnection and Data sharing through network

ü Time specific actions synchronization and handling of unpredicted model execution time

ü Proper Procedure steps interpretation in action that could be coded





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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONDrives for developmentDrives for development

Software Specific limitations:Software Specific limitations:ü User requirements were beyond the initial design/scope of the each

software ü Unable to parameterize the gProms software Human Computer

Interface

ü User requirements were beyond the initial design/scope of the each software

ü Unable to parameterize the gProms software Human Computer Interface


Structure and interoperability requirements:Structure and interoperability requirements:ü Embed to framework security considerations that were necessary for

process control network, due to integration with business networkü Reduce the amount of time that was required to gather and setup the

environment

ü Embed to framework security considerations that were necessary for process control network, due to integration with business network

ü Reduce the amount of time that was required to gather and setup the environment





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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONDistributed & Distributed & InterapplicationInterapplication CommunicationCommunication

ü gPROMS : develop dynamic model and MPC systemü Matlab : lineralised state space model used in the state and

parameter estimation ü Microsoft Office – Excel : processing and management of the

process data

ü Plant SCADA system (GE iFix/Fix32)ü Process Information Management System (OSISoft PI) used for data

archiving

ü gPROMS : develop dynamic model and MPC systemü Matlab : lineralised state space model used in the state and

parameter estimation ü Microsoft Office – Excel : processing and management of the

process data

ü Plant SCADA system (GE iFix/Fix32)ü Process Information Management System (OSISoft PI) used for data

archiving


Software / System Used in the Framework :Software / System Used in the Framework :

Communication specifics :Communication specifics :

ü Plant / Framework Communication : NetDDE (Net Dynamic Data Exchange)

ü Interapplication data transfer using Windows OLE standard (Excel –Matlab, gPROMS - Excel)

ü Plant / Framework Communication : NetDDE (Net Dynamic Data Exchange)

ü Interapplication data transfer using Windows OLE standard (Excel –Matlab, gPROMS - Excel)





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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONFramework developmentFramework development


Control framework development stages :Control framework development stages :ü Requirement gathering & Architecture definitionü Identify existing assets & component developmentü Software system integration & Testing

üStandalone - SimulationüNetworked – Plant interaction

ü Requirement gathering & Architecture definitionü Identify existing assets & component developmentü Software system integration & Testing

üStandalone - SimulationüNetworked – Plant interaction





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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONInterapplicationInterapplication Control Software Control Software

Functionality :Functionality :ü Provides interoperability between different software products ü Unattended execution of the whole procedure (copy, paste data,

search and replace, communication with the plant)

ü Provides interoperability between different software products ü Unattended execution of the whole procedure (copy, paste data,

search and replace, communication with the plant)


Features :Features :

ü Handles keyboard keystrokes & mouse movementü Searches dynamically generated resultsü Extracts data according to predetermined criteriaü Modifies the models in gProms (source code)ü Keeps intermediate simulation and optimization resultsü Flexible, distributed and real-time process handling

ü Handles keyboard keystrokes & mouse movementü Searches dynamically generated resultsü Extracts data according to predetermined criteriaü Modifies the models in gProms (source code)ü Keeps intermediate simulation and optimization resultsü Flexible, distributed and real-time process handling

ü Custom developed application using C++ programming languageü Use OOP (Object Oriented Programming) methodology to easily

extend base object to suit different interaction schemes

ü Custom developed application using C++ programming languageü Use OOP (Object Oriented Programming) methodology to easily

extend base object to suit different interaction schemes





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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATION

Initialization Procedure Initialization Procedure ü Matlab : Prepare/Build a COM Object to communicate with Excel

(code restructure)ü gPROMS : Initial Process execution (initial simulation and plant

model) ü Excel : Open files and reset valuesü Control scheme Software : Parameter and settings

• Optimization, Simulation, Linear Time• Update Delay• Number of Iterations

ü Matlab : Prepare/Build a COM Object to communicate with Excel (code restructure)

ü gPROMS : Initial Process execution (initial simulation and plant model)

ü Excel : Open files and reset valuesü Control scheme Software : Parameter and settings

• Optimization, Simulation, Linear Time• Update Delay• Number of Iterations


Data Flow Data Flow –– Procedure stepsProcedure steps

Main Procedure Main Procedure ü gPROMS : Optimization Stageü gPROMS : Simulation Stage

üMathematic modelüProcess Model (with noise/disturbance)üLinear Model

ü MATLAB : Kalman Filter

ü gPROMS : Optimization Stageü gPROMS : Simulation Stage

üMathematic modelüProcess Model (with noise/disturbance)üLinear Model

ü MATLAB : Kalman Filter





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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONData Flow Data Flow –– Framework AreaFramework Area

Control Scheme AreaControl Scheme Area


gPROMS

Data Files

SCADA System

PI

MATLAB

User

BBB

AAA

CCC

DDD

ü Simulation model execution (A)ü Results to Excel (A à B)ü Plant simulation model or Plant system execution (A)ü Results to Excel (AàB)ü Linearisation model (A)ü Data Files generation (AàC)ü Update flag status – Trigger condition (AàB)ü Matlab model run (Kalman Filter) (CàD, D)ü Results to Excel (DàB)

ü Simulation model execution (A)ü Results to Excel (A à B)ü Plant simulation model or Plant system execution (A)ü Results to Excel (AàB)ü Linearisation model (A)ü Data Files generation (AàC)ü Update flag status – Trigger condition (AàB)ü Matlab model run (Kalman Filter) (CàD, D)ü Results to Excel (DàB)

Procedure steps :Procedure steps :





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CONTROL IMPLEMENTATIONCONTROL IMPLEMENTATIONFuture improvements / potentialsFuture improvements / potentials


Further improvements :Further improvements :ü Define rules for the interface in order to interoperate with CAPE able

applications (Computer-Aided Process Engineering standard)ü Parametric model execution via software interfaceü Provide the ability to change the order of the sequence actions ü Direct interaction with LIMS & PIMS system to retrieve data used in

the procedureü Develop an OPC server tο communicate with the Plantü Develop a software component that utilizes COM/DCOM to distribute

data to Windows Applications

ü Define rules for the interface in order to interoperate with CAPE able applications (Computer-Aided Process Engineering standard)

ü Parametric model execution via software interfaceü Provide the ability to change the order of the sequence actions ü Direct interaction with LIMS & PIMS system to retrieve data used in

the procedureü Develop an OPC server tο communicate with the Plantü Develop a software component that utilizes COM/DCOM to distribute

data to Windows Applications


CONCLUSIONSCONCLUSIONS






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CONCLUSIONSCONCLUSIONSMathematical modeling for process optimizationMathematical modeling for process optimization

Conclusionsfuture workConclusionsfuture work

Fluid Catalytic Cracking process:Fluid Catalytic Cracking process:ü Workhorse of refinery - High economic incentivesü Complex, non-linear, uncertain, constrained processü High interest in simulation and optimization

ü Workhorse of refinery - High economic incentivesü Complex, non-linear, uncertain, constrained processü High interest in simulation and optimization

Fluid Catalytic CrackingFluid Catalytic Cracking pilot plant:pilot plant:ü Research toolü Catalyst benchmarkingü Need to improve efficiency

ü Research toolü Catalyst benchmarkingü Need to improve efficiency

DDynamic simulator ynamic simulator ofof the FCC the FCC pilot plant:pilot plant:ü Steady state operation of the pilot FCC riser, liftline and standpipeü Dynamic behavior of the FCC regenerator and stripperü The dynamic simulator was verified with pilot experiments

ü Steady state operation of the pilot FCC riser, liftline and standpipeü Dynamic behavior of the FCC regenerator and stripperü The dynamic simulator was verified with pilot experiments





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CONCLUSIONSCONCLUSIONSMPC towards operability & profitabilityMPC towards operability & profitability

Model Predictive Control:Model Predictive Control:ü Use of the dynamic model for online optimizationü Control horizon - Robust control structureü High interest in simulation and optimization

ü Use of the dynamic model for online optimizationü Control horizon - Robust control structureü High interest in simulation and optimization

MPC on the pilot FCC:MPC on the pilot FCC:ü Control the process with unknown catalyst ü Robustness in operationü Rapid guidance to desired states

ü Control the process with unknown catalyst ü Robustness in operationü Rapid guidance to desired states

Future Work:Future Work:ü States and parameters estimationü Implementation to the real processü Process automation

ü States and parameters estimationü Implementation to the real processü Process automationConclusions

future workConclusionsfuture work


AKNOWLEGMENTSAKNOWLEGMENTS


üü European Social Fund & National Resources European Social Fund & National Resources -- EPEAEK II EPEAEK II –– ARCHIMEDESARCHIMEDES

üü CPERI CPERI –– LEFH PersonnelLEFH Personnel



THANK YOUTHANK YOU

Dr. S.S. Voutetakis, CPERI/CERTHDDrr. . SS..S.S. VoutetakisVoutetakis, , CPERI/CERTHCPERI/CERTH

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