Manenti - Process control.ppt [modalità compatibilità]

Optimal controlIndustrial applications

Flavio Manenti, Dept. CMIC “Giulio Natta”

Flavio Manenti, Politecnico di Milano

2‘‘Terms and conditions’’

• Acronyms and notations Advanced conventional process control Advanced process control Model predictive control

• Model predictive control Based on linear/linearized models

• Dynamic matrix control (DMC, LMPC, MPC)• Several commercial packages

Based on nonlinear models• Model predictive control (MPC, NMPC)• No commercial packages

• Features of NMPC A dynamic (convolution) model is used to foresee the future behavior of

the plant on a specific time horizon (prediction horizon, H_P) consisting of p sampling times

Receding horizon methodology (moving horizon, not rolling horizon)

Manenti, Considerations on Nonlinear Model Predictive Control Techniques, COMPUTERS & CHEMICALENGINEERING, 35(11), 2491-2509, 2011.


3Integration Pyramid

PlantManagement

Maintenanceand Production

Management

Enterprise Management

Field

Conventional Control

Advanced Control (NMPC)

Real Time Dynamic

Optimization

Scheduling

Planning

SecondsMinutes

Hours - Days

Weeks

Months - Years

1 1

2 2 2

1

min ( ) ( ) ( ) ( 1)p p pk h k h k h

SET TARy react react T c c u c c

j k l k i k

T j T F l F F i F i

AdvancedControl(NMPC)

Real TimeDynamic

Optimization


4Algorithm

• Model Predictive Control• MPC

Plant


5Receding horizon methodology

t

u1,1

u2,1

u3,1un,1

Set-point

Manipulated Variable

PLANT

MODEL

Controlled Variable

u1

HPHC

u2

u3

HD

1

HPHC

u1

u2

u3

2

HPHC

u1u2 u3

3


6Industrial viewpoint

Outlier DetectionRobust methodsLinear/nonlinear RegressionsPerformance MonitoringYield AccountingSoft sensing

DataReconciliation

MathematicalModeling

DynamicSimulation

ModelPredictive

Control

Optimization

ModelReduction

DCS, OTS, Plantwide control, Soft sensing, process transients,

grade/load changes

Solvers

PlanningScheduling

Dynamic optimizationDistributed predictive control

Nonlinear SystemsOptimizers

Differential systemsStiff systems

ODE,DAE,PDE,PDAEEfficiency

DecisionsRaw Data

ParallelComputing

UncertaintiesOptimal production

Optimal grade changesMulti-objective

Real-time optimizationHigh accuracy

Reliable process controlProduction improvement

EconomyJust in time

Market-drivenLogistics

CorporateSupply Chain


NMPCPET plant

7

Manenti, RovaglioIntegrated multilevel optimization in large-scale poly(ethylene terephthalate) plants

Industrial & Engineering Chemistry Research47(1), 92-104, 2008.


8Case Study: PET Plant


9Jacobian Matrix

• Primary esterifier• Secondary esterifier• Low polymerizer• Intermediate polymerizer• High polymerizer• Solid state polymerizer

Resulting DAE: 1356 diff. eqs. 164 alg. eqs. 15 controls 2 controlled 16 constrained


10Frequent Grade Changes

Grade A: PET as textile fibres (melt processI.V. = 0.55 ÷ 0.65 dl/g)

Grade B: PET for bottles production (bottlegrade I.V. = 0.72 ÷ 0.85 dl/g)

Grade C: PET for special fibres (tire-cordresins I.V. = 0.95 ÷ 1.05 dl/g)


11Comparison

0.440

0.445

0.450

0.455

0.460

0.465

0.470

0 500 1000 1500 2000 2500 3000

IV I

P (

dl/g

)

Time (min)

0.6000.6100.6200.6300.6400.6500.6600.6700.680

0 500 1000 1500 2000 2500 3000

IV H

P (

dl/g

)

Time (min)

0.000.200.400.600.801.001.201.40

0 500 1000 1500 2000 2500 3000

P H

P (

mm

Hg)

Time (min)

0.600.801.001.201.401.601.802.00

0 500 1000 1500 2000 2500 3000

P I

P (

mm

Hg)

Time (min)

NMPC

NMPC


12Comparison

0.6400.6500.6600.6700.6800.6900.7000.710

0 500 1000 1500 2000 2500 3000

IV P

HC

R (

dl/g

)

0.7600.7700.7800.7900.8000.8100.8200.8300.840

0 500 1000 1500 2000 2500 3000

IV S

SP (

dl/g

)

NMPC

NMPC

Time (min)

Time (min)


NMPCEthylene splitter

Eni, Italy

13


14The C2-splitter

Column design:•Total tray number : 110•Feed : tray #55 (*)

•Ethylene cut : tray #104(*)

Feed composition (**) :• C2H4 – 79%• C2H6 – 19%• Others – 2% (H2, CO,CO2, CH4, C3H8, C3H6)

(*) bottom-up numeration(**) molar basis


15Validation

Reflux flowrate change effect on overhead composition and on temperature at tray #5

0,0100,0200,0300,0400,0500,0600,0700,0800,0900,0

1000,0

89,40 89,60 89,80 90,00 90,20 90,40 90,60

Overhead im

purities [ppm

]

Reflux flowrate [ton/h]

Reflux flowrate change effects on overhead stream impurities

SimulationPlant

‐41,00

‐40,00

‐39,00

‐38,00

‐37,00

‐36,00

‐35,00

89,60 89,80 90,00 90,20 90,40 90,60

Tray 5 tem

perature [°C]

Reflux flowrate [ton/h]

Reflux flowrate change effects on temperature at tray #5

SimulationPlant

0,0

200,0

400,0

600,0

800,0

1000,0

1200,0

96,60 96,80 97,00 97,20 97,40 97,60

Overhead im

purities [ppm

]

Boil‐up flowrate [ton/h]

Boil‐up flowrate change effects on overhead stream impurities

SmulationPlant

‐41,00

‐40,00

‐39,00

‐38,00

‐37,00

‐36,00

‐35,00

‐34,00

96,60 96,80 97,00 97,20 97,40 97,60

Tray 5 te

mpe

rature [°C]

Boil‐up flowrate [ton/h]

Boil‐up flowrate change effects on temperature at tray #5

SimulationPlant

Boil-up flowrate change effect on overhead composition and on temperature at tray #5


16Servo-mechanism problem

9000

9200

9400

9600

9800

10000

10200

10400

10600

0,0 5,0 10,0 15,0 20,0 25,0 30,0

Reflu

x flo

w ra

te [lbm

ol/h]

Time [h]

Reflux flow rate

PIDMCNMPC

0,9600

0,9650

0,9700

0,9750

0,9800

0,9850

0,9900

0,0 5,0 10,0 15,0 20,0 25,0 30,0

Ethylene

molar fractio

n [‐]

Time [h]

Ethylene molar fraction in cut stream

PIDMCNMPCSP Distillate

0,8600

0,8700

0,8800

0,8900

0,9000

0,9100

0,9200

0,9300

0,9400

0,9500

0,0 5,0 10,0 15,0 20,0 25,0 30,0

Etha

ne molar fractio

n [‐]

Time [h]

Ethane molar fraction in bottom stream

PIDMCNMPCSP Bottom

43,20

43,40

43,60

43,80

44,00

44,20

44,40

44,60

44,80

45,00

0,0 10,0 20,0 30,0 40,0 50,0 60,0Rebo

iler the

rmal duty [1.E+06 BT

U/h]

Time [h]

Reboiler thermal duty

PIDMCNMPC


17Servo-mechanism problem

0,8600

0,8700

0,8800

0,8900

0,9000

0,9100

0,9200

0,9300

0,9400

0,9500

0,0 5,0 10,0 15,0 20,0 25,0 30,0

Etha

ne molar fractio

n [‐]

Time [h]


PIDMCNMPCSP Bottom

39,5040,0040,5041,0041,5042,0042,5043,0043,5044,0044,5045,00

0,0 5,0 10,0 15,0 20,0 25,0 30,0Rebo

iler the

rmal duty [1.E+06 BT

U/h]

Time [h]


PIDMCNMPC

9000

9200

9400

9600

9800

10000

10200

10400

10600

0,0 5,0 10,0 15,0 20,0 25,0 30,0

Reflu

x flo

w ra

te [lbm

ol/h]

Time [h]

Reflux flow rate

PIDMCNMPC

0,9600

0,9650

0,9700

0,9750

0,9800

0,9850

0,9900

0,0 5,0 10,0 15,0 20,0 25,0 30,0

Ethylene

molar fractio

n [‐]

Time [h]


PIDMCNMPCSP Distillate

0,9800

0,9850

0,9200

0,9250


18Regulation problem (feed composition disturbance)

0,9890

0,9895

0,9900

0,9905

0,9910

0,9915

0,9920

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Ethylene

molar fractio

n [‐]

Time [h]


PIDMCNMPCSP Cut

0,8800

0,8900

0,9000

0,9100

0,9200

0,9300

0,9400

0,9500

0,9600

0,9700

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Etha

ne molar fractio

n [‐]

Time [h]


PIDMCNMPCSP Bottom

9950,00

10000,00

10050,00

10100,00

10150,00

10200,00

10250,00

10300,00

10350,00

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Reflu

x flow ra

te [lbm

ol/h]

Time [h]

Reflux flow rate

PIDMCNMPC

43,20

43,40

43,60

43,80

44,00

44,20

44,40

44,60

44,80

45,00

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Rebo

iler the

rmal duty [BTU

/h]

Time [h]


PIDMCNMPC


19Regulation problem (feed composition disturbance)

0,9890

0,9895

0,9900

0,9905

0,9910

0,9915

0,9920

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Ethylene

molar fractio

n [‐]

Time [h]


PIDMCNMPCSP Cut

0,8800

0,8900

0,9000

0,9100

0,9200

0,9300

0,9400

0,9500

0,9600

0,9700

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Etha

ne molar fractio

n [‐]

Time [h]


PIDMCNMPCSP Bottom

9950,00

10000,00

10050,00

10100,00

10150,00

10200,00

10250,00

10300,00

10350,00

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Reflu

x flow ra

te [lbm

ol/h]

Time [h]

Reflux flow rate

PIDMCNMPC

43,20

43,40

43,60

43,80

44,00

44,20

44,40

44,60

44,80

45,00

0,0 10,0 20,0 30,0 40,0 50,0 60,0

Rebo

iler the

rmal duty [BTU

/h]

Time [h]


PIDMCNMPC

0,9900

0,9905

0,9500

0,9550

0,9500

0,9550


Self-adaptive NMPCDeisobutanizer

Mongstad Refinery, Norway

20

Dones, Manenti, Preisig, Buzzi-FerrarisNonlinear Model Predictive Control: a Self-

Adaptive ApproachIndustrial & Engineering Chemistry Research

49(10), 4782-4791, 2010


Unit 21


Spoiled Jacobian 22


Results

• Use of compartmental models• Model self-adaptation

To the problem To the dynamic To the computational effort

• Benefits Accurate when needed Fast when possible (viceversa)

23

93

93.5

94

94.5

95

95.5

0 500 1000 1500 2000 2500 3000

conc

entra

tion

iC4

top

[%]

time [s]

NMPC with full dynamic modelNMPC with 5-dynamic-trays model

ANMPC

360

365

370

375

380

385

390

395

400

0 500 1000 1500 2000 2500 3000

reflu

x st

ream

[mol

/s]

time [s]

NMPC with full dynamic modelNMPC with 5-dynamic-trays model

ANMPC

4

6

8

10

12

14

16

0 500 1000 1500 2000 2500 3000

num

ber o

f dyn

amic

tray

s us

ed in

the

mod

el

time [s]


D-RTOOlefins plant

Invensys, USA

24

Manenti et al.Process Dynamic Optimization Using ROMeo

Computer Aided Chemical Engineering29, 452-456, 2011


25Industrial perspective


DataReconciliation


DynamicSimulation

ModelPredictive

Control

Optimization

ModelReduction

DCS, OTS, Plantwide control, Soft sensing, process transients,

grade/load changes

Solvers

PlanningScheduling

Dynamic optimizationDistributed predictive control




DecisionsRaw Data

ParallelComputing





EconomyJust in time

Market-drivenLogistics

CorporateSupply Chain

Dynamic Optimization


26Tools


DataReconciliation

MATHEMATICALMODELING

DynamicSimulation

DynamicOptimization

Optimization

ModelReduction

DCS, OTS, Plantwide control, Soft sensing, process transients, grade/load changes

Solvers

Enterprise-widePlanningScheduling




DecisionsRaw Data

ParallelComputing

Supply ChainManagement





Just in timeMarket-driven

Conscious MGM


DynamicSimulation

Optimization

DYNSIM

ROMeo


27Tools


DataReconciliation

MATHEMATICALMODELING

DynamicSimulation

DynamicOptimization

Optimization

ModelReduction

DCS, OTS, Plantwide control, Soft sensing, process transients, grade/load changes

Solvers

Enterprise-widePlanningScheduling




DecisionsRaw Data

ParallelComputing

Supply ChainManagement





Just in timeMarket-driven

Conscious MGM

Is it possible?

DYNSIMMathematicalModeling

DynamicSimulation

DynamicOptimization

Optimization

ROMeo


28The Idea

• On-line Optimization

• Nonlinear MPC

• Data Reconciliation

Tmin

. . : 0; 0

R

M R M Ri i i i

i

x x x x

s t f g

x

W

x x

,min

. . : , 0

, 0

;n m

Z Profits Costs

s t f

g

R N

x b

x b

x b

x b

1 2, ,min ... ...

. . : 0; , 0

0; , 0

; ;

n

n p m

Z

s t f f

g g

R R N

x u b

x x x

x x x

x u b

• Dynamic Optimization

Tmin

. . : , 0

, 0

R SET R SETi i i i

i

x x x x

s t f

g

uW

x x

x x


29Example

• Series of three ideal CSTRs

Open-loop

Closed-loop

P-7

P-16

P-21


30Open-loop in C++

Key-component molar flow exiting the reactor:

• #1

• #2

• #3

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 1 2 3 4 5

[mol

/s]

Time


31Open-loop in DYNSIM

UAM MODELS inserted into the ICON PALETTE(C++ dynamic library)


32Open-loop in DYNSIM

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 1 2 3 4 5

[mol

/s]

Time


33Using BzzMath in DYNSIM

No changes at the DYNSIM’s interface


34Closed-loop in C++

0.398

0.4

0.402

0.404

0.406

0.408

0.41

0.412

0.414

0.416

0.418

0 10 20 30 40 50

[mol

/s]

Time

0.198

0.199

0.2

0.201

0.202

0.203

0.204

0.205

0 10 20 30 40 50

[mol

/s]

Time

0.0982

0.0984

0.0986

0.0988

0.099

0.0992

0.0994

0.0996

0.0998

0.1

0.1002

0 10 20 30 40 50

[mol

/s]

Time

CV

(SP: 0.1 mol/s)


35Full Integration (All-in-one)


36Drag & DropDYNSIM


37Drag & DropDYNSIM


38Drag & DropROMeo


39Drag & DropROMeo


40Smart Dynamic Simulation with ROMeo


41D-RTO with Multiple Shooting


42Friendly Interface for D-RTO

Possibility to give the user to enter any kind of data for D-RTO

Specific D-RTO

TAB


43Preliminary ComparisonRTO vs D-RTO

0.095

0.1

0.105

0.11

0.115

0.12

0.125

0 10 20 30 40 50 60 70 80 90

'checkdrto.ris' u 1:4'check.ris' u 1:4

Traditional approach

Two-shooting

Multiple-shooting


44Validation Case (Olefins)

• Cracking Furnace (SPYRO-based D-RTO)


45

Steam Cracking FurnaceFeed

Stack

Damper

Breeching

Convection Section

Radiant Section

Burners / Air Blowers

Coil Outlet Temperature (COT)

Steam

Transfer Line Exchanger (TLE)

High Pressure Steam

Main Factionator

>800°C 400°C

COT

Olefins

TC

FC

Fuel

Air

PV

PV

OUT

OUT

SP

Temperature Controller

Flowrate Ratio Controller

RADIANT SECTION

PV


46

D-RTO- ROMeo (OPERA) -

Software IntegrationAll-in-one tool for SPYRO-based smart dynamic simulation and optimization of olefins plant

SPYRO- FORTRAN -

• SPYRO (FORTRAN)

Mixed-language (FORTRAN-C++)

• Cracking furnace SPYRO-based dynamic model (C++)

Very performing ODE/DAE solver (BzzOde, BzzDae, BzzDaeSparse… BzzMath)

• Smart dynamic simulation (grade change, DYNSIM)

Full integration in DYNSIM

• Dynamic real-time optimization (multiple shooting, ROMeo)

Full integration and OPERA synchronization

BzzMath- C++ -

Dynamic Model- DYNSIM -


47SPYRO-based (Smart) Dynamic Simulation• C3H6/C2H4 Severity Change from 0.62 to 0.67

FUEL FLOWRATE [kg/h]

DUTY [kcal/h] CH4/C3H6 SEVERITY

COIL OUTLET TEMPERATURE (COT)

[°C]

WALL TEMPERATURE

[°C]

C3H6/C2H4 SEVERITY

Initial Severity

Arrival Severity

1.34e+007

1.35e+007

1.36e+007

1.37e+007

1.38e+007

1.39e+007

1.4e+007

0 20 40 60 80 100 120

Time [min]

0.9

0.92

0.94

0.96

0.98

1

1.02

1.04

0 20 40 60 80 100 120

Time [min]

0.61

0.62

0.63

0.64

0.65

0.66

0.67

0.68

0.69

0 20 40 60 80 100 120

Time [min]

786

788

790

792

794

796

798

800

802

0 20 40 60 80 100 120

Time [min]

1088

1090

1092

1094

1096

1098

1100

1102

1104

0 20 40 60 80 100 120

Time [min]

3450

3500

3550

3600

3650

3700

3750

3800

0 20 40 60 80 100 120

Time [min]


48

• Severity Change Convergence

3450

3500

3550

3600

3650

3700

3750

3800

0.9 0.92 0.94 0.96 0.98 1 1.02 1.04 3450

3500

3550

3600

3650

3700

3750

3800

0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69

Fuel Flowrate vsCH4/C3H6 Severity

Fuel Flowrate vsC3H6/C2H4 Severity

SPYRO-based (Smart) Dynamic Simulation


49SPYRO-based (Smart) D-RTO

As per DYNSIM, UAM inserted into the ICON PALETTE (C++ dynamic library)


508-shoots flowsheet


5132-shoots flowsheet

No changes at the ROMeo’s interface


52

2000

3000

4000

5000

6000

7000

8000

9000

0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05

Converging Path

FUEL

FLO

WR

ATE

4 SHOOTS

16, 32 SHOOTS

TRADITIONAL

STARTING POINT

OPTIMUM

C3H6/C2H4 SEVERITY

No changes at the traditional control level


53

3200

3250

3300

3350

3400

3450

3500

3550

3600

3650

3700

3750

0.6 0.65 0.7 0.75 0.8 0.85 0.9

High Benefits, Few Shoots

FUEL

FLO

WR

ATE

32 SHOOTS

16 SHOOTS

C3H6/C2H4 SEVERITY


54

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

0 20 40 60 80 100 120

Time [min]

0.5

0.6

0.7

0.8

0.9

1

1.1

0 20 40 60 80 100 120

Time [min]

Market dynamics• Market dynamics (the current market condition is a higher demand of

propylene, thus higher price) imposes a severity change in ethylene/propylene production:

TRADITIONAL

4 SHOOTS

TRADITIONAL

4 SHOOTS

CH4/C3H6C3H6/C2H4

16, 32 SHOOTS

16, 32 SHOOTS


55

2000

3000

4000

5000

6000

7000

8000

9000

0 20 40 60 80 100 120

Time [min]

700

720

740

760

780

800

820

0 20 40 60 80 100 120

Time [min]

Severity change

Coil outlet temperature [°C] of the radiant section of the cracking furnace

Fuel flowrate [kg/h] entering the cracking furnace

TRADITIONAL

4 SHOOTS

TIME [min]

TRADITIONAL

4 SHOOTS

Supposed practical upper bound16, 32 SHOOTS

16, 32 SHOOTS


56

3000

3200

3400

3600

3800

4000

0 50 100 150 200 250 300

Quantitative comparison

Traditional RTO

TIME [min]

32-shoots D-RTO

2000

2500

3000

3500

4000

4500

5000

0 50 100 150 200 250 300

D-RTO off-spec timeRTO off-spec time

TIME [min]

FUEL

FLO

WR

ATE

• To operate at the optimum conditions dictated by the market, the RTO requires more than 2h to accomplish the severity change, whereas the D-RTO requires about 1h.

• Consider that severity changes are not only imposed by market dynamics, but even by feedstock changes, load changes… As a result, frequent severity changes are required in each coil of each cracking furnace of each olefins plants

1-step traditional RTO

2-steps traditional RTO

32-shoots D-RTOFU

EL F

LOW

RA

TE


57Industrial feasibility

D-RTO with ROMeo

• Feasible

• D-RTO is more stable than RTO

• D-RTO halves off-spec periods

• On-line feasibility for the industrial scale

• Computational times are comparable

• No visible changes to the user in ROMeo environment

• No changes to the existing control scheme

• Easy-to-use when implemented (few parameters to be defined)

SEVERITY

2000

3000

4000

5000

6000

7000

8000

9000

0.5 0.6 0.7 0.8 0.9 1 1.1

Upper bound

FUEL

FLO

WR

ATE

Documents

Manenti - Process control.ppt [modalità compatibilità]