oxford2 · 2013-08-21 · cv2 p1 mv1 superviseur m2 m1 r1 contrainte cv2 (a) (b) cons1 cv1 sm2 sm1...

INDUSTRIAL APPLICATIONS

OF

PREDICTIVE FUNCTIONAL CONTROL

to our dear friend : David CLARKE !

Oxford 09/01/2009 Jacques Richalet

jacques. richalet @ wanadoo.fr

End of the Sixties:

- ORIGIN OF MODEL BASED PREDICTIVE CONTROL

- PETROLEUM INDUSTRY / DEFENCE INDUSTRY

.Refineries :

How to handle constraints on MV’s and CV’s ?

How to control a multivariable process ?

.Defence :

How to control a process with a non stationary set point

with no lag error (follow-up servo) ?

PROFIT

OPTIMIZATION

CONSTRAINTS*

PREDICTION

MODEL

M.B.P.C / PREDICTIVE CONTROL

WHY MPC ?WHY MPC ?

Gasoil

Residue

Crude oil

CONTROL ON CONSTRAINTCONTROL ON CONSTRAINT

Viscosity

of residue

LIMIT !

t

Ko

Ok

1975:Total Normandy Refinery. D11column 30.000

T/day!

O

D

H

Tracking servo systemTracking servo system

P

Heading

Target

trajectory

t

X

NO LAG ERRORNO LAG ERROR

εΤ = 0 !

….the past…

1968 : Basic principles of Predictive Control

1973 : 1st version of PFC/ IDCOM

1974 : 1st industrial applications of PFC/IDCOM

Steam Generator/ Reactor/ Distillation

1978 : 1st contribution MPC “Automatica “ ( problems..!)

1980-85 : HIECON with Set Point ( Houston)

1989 : Extension from PFC to PPC:

Parametric Predictive Control (control by reactor flow..)

1998 :1st implementation of an MPC in the native library of a

PLC : Modicon. Schneider-Electric.Momentum.Concept

FEATURES

…the oldest MBPC… 1968

A “few” thousand applications in many fields

DEFENCE

ü Mine hunter

ü Ariane 5 attitude control

ü Missile autopilot

ü Laser guided missile (t=62 microsecond…)

ü Gun turrets

ü Radar antennas

ü Infra Red camera

ü High speed Infra Red

ü Missile launch

ü Camera mount

ü Radar antennas

ü Laser mirror

ü Tank turret (T. 55)

ü Mine sweeper auto-pilot

ü Aircraft carrier auto-pilot

METALLURGICAL INDUSTRIES

ü Coating lines aluminium

ü Thickness control - Roll eccentricity

ü Mono-multi-stands Rolling mills

ü Continuous casting (slab)

ü Push-ovens

ü Coke furnace

ü Hot/cold/Thin rolling mills

ü Steam generators

Level, temperature, pressure……;

MISCELLANEOUS (Chem reactors and

distillation excluded)

ü Bioreactor Melissa ESAü Plastic extrusion robot (high speed)

ü Temperature control of gas furnace

ü Temperature control of TGV train carriage

ü River dam level control (T=1 hour )

ü Powder milk dryer

ü Electric furnace brazing etc….

AUTOMOTIVE

ü Gear box test bench

ü Dynamic test bench engine

ü Fuel injection

ü Idle fuel injection

ü Clutch antistroke

ü Gear box (tank)

ü Hybrid car (electric-fuel)

ü Air conditioning

FIRST INDUSTRIAL APPLICATIONS OF P.F.C.FIRST INDUSTRIAL APPLICATIONS OF P.F.C.EXOTIC APPLICATIONS…

North : Molde (Norway)

South : Plaza Huincul ( Patagonia-Argentina)

East : Hokaido ( Japan)

West : Mobile ( USA.DEGUSSA)

High : Eiffel Tower elevator !

Space : European Space Agency : Mars project bioreactors ( 2029 !)

Depth : Mine hunter boat

Speed : Temperature cabin ( TGV : 574.8 km/h W.record )

Fast : Laser guided bomb ( Tsampling : 65 � s )

Slow : River dam level ( Tsampling : 1 hour)

Ecology: Diester from colza

Animal : Dog food pellet dryer

Plants : Greenhouses

etc…

AT WHAT LEVEL DO WE OPERATE ?

Back to Basics:

• Level 0 : Ancillary processes e.g: FRC/ Pid

(the valve is the nightmare of control !)

• Level 1 : Dynamic control with constraints

• Level 2 : Optimization of working conditions

• Level 3 : Production planning

.Level N is operative if level N-1 works well…!

. »There are no technical problems, but only technical

aspects of economic problems … »0 50 100 150 200 250 300 350 400 450

0

0.5

1

1.5

2

QUALITY

N Histogram

Q1

Sigma 1

OFF

SPEC

0 50 100 150 200 250 300 350 400 4500

0.5

1

1.5

2

OFF

SPEC

QUALITY

N Histogram

Q1

Sigma 1

Sigma 2

LEVEL 0 / LEVEL 1

Sigma 1 -- Sigma 2

0 50 100 150 200 250 300 350 400 4500

0.5

1

1.5

2

OFF

SPEC

QUALITY

N Histogram

Q1

Sigma 1

Q2

Sigma 2

LEVEL 0 / LEVEL 1

Sigma 1 -- Sigma 2

LEVEL 2

Q1 -- Q2

0 50 100 150 200 250 300 350 400 4500

0.5

1

1.5

2

OFF

SPEC

QUALITY

N Histogram

Q1

Sigma 1

Q2

Sigma 2

D ELTA C OST (W2-W1)

LEVEL 0 / LEVEL 1

S igma 1 -- S igma 2

LEVEL 2

Q1 -- Q2

W1

W2

C OST FUNCTION

« SQUEEZE AND SHIFT »

4 BASIC PRINCIPLES OF PFC : J. PIAGET4 BASIC PRINCIPLES OF PFC : J. PIAGET

– Operating Image

– Target – Sub Target

– Action

– Comparison :

– Predicted / Actual

- Internal independant Model

– Reference Trajectory

– Solver. Functional basis

Structured future MV

– Error compensator

• Natural Control : “You would not drive your car using a PID scheme”

STRATEGY…

. Control of industrial processes : PI(D) ?

but PID cannot solve all control problems,

but any candidate controller should « look like a PId »:

EASY TO UNDERSTAND, TO IMPLEMENT, TO TUNE

.. consistent with floor instrumentists’ habits

so:

. Tuning parameters should have a clear, physical interpretation.

. Elementary mathematics.

. No explicit integrator in the loop.

. No matrix calculation.

. No quadratic minimization on-line.

. No iterative computation on-line.

. Open technology…

TARGETED PERFORMANCES

. Control « all » types of processes: time delay, unstable,

non-minimum phase, some non-linear

. Handles all constraints on the MV and on internal variables of the process CV.

. No lag error on dynamic set points with no integrator

Industrial settings:

. Transparent and Cascade Control with transfer of constraints from inner loop to outer controller (« Back Calculation »)

. True feed-forward

. Split-range control with different dynamics (reactors!)

. Control with 2 cooperative MV’s, e.g., big valve / small valve

. Extendable to 2 MV/ 2 CV…..

. Dual control: total elimination of harmonic disturbances

. Full Robustness analysis: easy trade-off ,

. Immediate tuning / Open technology

REFERENCE

TRAJECTORY

REFERENCE

TRAJECTORY

Past n Future TRBF

Set point

Process output

ε (n)

Predicted output

C(n+H)

Model output

∆

∆

Manipulated variable

H 1 H 2

MV i

MV

Prediction

horizon

yP

Reference

trajectory yref

yM

Coïncidence

horizon

yP

ε (n+H)

H

M

2 T Y P E S O F M O D E L SIndependent models are selected:

. Input /Output models valid for all math. structures

. No permanent errors in steady-state modes!

( never mix process variables with model parameters..)

Re-aligned Model

sp

sm

e Process

Model

perturbation

+

+e

sm

spProcess

Model

perturbation

+

+

Independent Model

Solver

• The future MV is projected on a Functional Basis:

– e.g. Taylor expansion / polynomials

• Thus, the MV is restricted a priori and not damped afterwards….

• MV(n+i) = Σ � j. Uj(i)

• U0(i) = 1, U1(i) = i U2(i)= i2 etc …

• Find the � j such that:

– At a finite number of points, the predicted model output coincides with the desired reference trajectory

• (i.e., coincidence points)

Why structuring the future MV?

• Determing all future MVs is of little benefit:

– many projects lead to the same future behaviour

• (low-pass process)

• Computation time increases ( PLC !)

• Introduction of a damping term on the speed

of the MV a priori is difficult to tune !

• Projection on « Eigen » functions of linear

dynamic processes insures no lag error on

dynamic set points

0 100 200 300 400 500 600 700 800 900-20

0

20

40

60

80

100

120

140OPEN LOOP

dCV

CV

MV

exp( 20 ).(1 15 )( )

(1 25 )(1 55 )(1 80 )

s sH s

s s s

− −− −− −− −====+ + ++ + ++ + ++ + +

0 100 200 300 400 500 600 700 800 900

0

20

40

60

80

100

120

140

time

ClOSED LOOP WITH CONSTRAINT

MV CONSTRAINT

SETPOINT

CV CLTR DESIRED =410

h=125

CLTR ACTUAL =424

100 200 300 400 500 600 700 800 900-20

0

20

40

60

80

100

120

140REFERENCE TRAJECTORY at ti=20

Setpoint=100

REF TRAJ.(green)

CV PREDICTED(black)

CV PASSED(red)

MV

tinit=20

100 200 300 400 500 600 700 800 900-20

0

20

40

60

80

100

120


Setpoint=100

REF TRAJ.(green)

CV PREDICTED(black)

CV PASSED(red)

MV

tinit=60

100 200 300 400 500 600 700 800 900-20

0

20

40

60

80

100

120


Setpoint=100

REF TRAJ.(green)

CV PREDICTED(black)

CV PASSED(red)

MV

tinit=100

100 200 300 400 500 600 700 800 900-20

0

20

40

60

80

100

120


Setpoint=100

REF TRAJ.(green)

CV PREDICTED(black)

CV PASSED(red)

MV

tinit=150

100 200 300 400 500 600 700 800 900-20

0

20

40

60

80

100

120


Setpoint=100

REF TRAJ.(green)

CV PREDICTED(black)

CV PASSED(red)

MV

tinit=200

100 200 300 400 500 600 700 800 900-20

0

20

40

60

80

100

120


Setpoint=100

REF TRAJ.(green)

CV PREDICTED(black)

CV PASSED(red)

MV

tinit=300

ACCURACY

• Basic Eigen Function property:

• If the set point can be projected on a polynomial basis

of order N, and if the non integrative model is of order

N, then there is no lag error

• Accuracy depends on the choice of the functional basis

and not on the gain of the controller….

PrH(C - (n)) (1 - ) (n )yy Mu(n) +

H G MG M (1 - )

λ

α=

P = G - se

1 + Ts M

y

u

θ= =

y(n) = α y(n+1) + (1- α) . u(n-1-r) . G α = e -Tsamp

T

θ = Tsamp . r

ELEMENTARY TUTORIAL EXAMPLEELEMENTARY TUTORIAL EXAMPLE

Trajectory expo. : λλλλ

1 coincidence point: H

1 Base function: step

Target : ∆P(n+H) = (C - yPr) (1 - λH)

Processus with delay :yPr (n) = yP (n) + yM (n) - yM(n-r)

Model :

Free mode

Forced mode

:

My (n) HαHu(n) . GM 1 - α

Model increment :

M MPH H H(C - (n ) ) (1 - ) (n ) + u (n ) G M (1 - ) - (n )y y yr λ α α=

Control equation : P(n + H) M(n + H)∆ ∆=

Increment = Free(n+h) + Forced(n+h)- ymodel(n)

The only mathematical problem for trainees …!

DUAL CONTROL

• Problem:

– Classical control:

• Constant set-point + harmonic disturbance rejection

. Complex algebra controller : much simpler !

• Examples:

– Helicopter : 20mm artillery / vibration damping

– Continuous casting steel industry :

oscillation of the mould and roll swell effect

– Wave Energy Converter etc..

-6.5 -6 -5.5 -5 -4.5 -4 -3.5 -3 -2.5 -2-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4SENSITIVITY / CLTR

LOG(omega)

LOG(CV/DV)

CLTR=30

CLTR=300

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.80

0.05

0.1

0.15

0.2

0.25 Mod|CV/DV| b withoutDual / r with Dualcontrol

tauprocess=50

omeg=1 // tau =150)

without dual with dual

wave pulsation 1=2pi/150

damping tauprocess=50

CLTR=200

tauprocess=50

omeg=1 // tau =150)

without dual with dual

wave pulsation 1=2pi/150

damping tauprocess=50

CLTR=200

T a i rP e l e c T i n tTemperature control with internal variable constraints

Tsurf

Constraints on internal variables:

the multiple controller approach !

Extension to different dynamic constraints …

Cons2=CV2max

Cons1

P2R2

MV2

CV2

P1MV1

Superviseur

M2

M1

R1

contrainte

CV2

(a)

(b)

Cons1

CV1

SM2

SM1

t

t

CV1

Cons2=CV2max

Cons1

P2R2

MV2

CV2

P1MV1

Supervisor

M2

M1

R1

constraint

CV2

(a)

(b)

Cons1

CV1

SM2

SM1

t

t

CV1

100 200 300 400 500 600 700 800

-20

0

20

40

60

80

100

120

140

160

SETPOINT CV1=100 / CONSTRAINT CV2 = 129

CV1

CV2

MV2 active

MV1 active

MV1

MV2

CONSTRAINT CV2

PERT

20 40 60 80 100 120 140 1604

4.5

5

5.5

6

6.5

7

OLTR CLTR

GAIN MARGIN

GAIN MARGIN / CLTR TUNING

Dynamics

Robustness

Accuracy 2 0 0

Base

Function CLTR Horizon

0 2 1-

0 1+ 2

Goodwin ?

Types of model:

1) Black Box

• Physical analysis . Choice of model ?

• Application of a Test signal protocol ?.

• Optimisation of the protocol?

• TOUGH CONSTRAINTS from THE PRODUCER.!

• Pseudo-random binary noise to be avoided in practice …

• Action on-site and off-site : Cost / Man Hours

• No trade ! : to be repeated on all processes

• Weak capitalisation.

• BUT: no investment, easy to perform….

Identification of Industrial Processes

The target is not to minimize the quadratic error

between the process output yp(n) and the model

output ym(n):

Cstate = ∑ ( yp(n)-ym(n) )2

but to minimize the Structural Distance between the

process parameters Pp(j) and the model parameters

Pm(j) ( Lyapunof function !..)

Cstructure= ∑ ( Pp(j)-Pm(j) )2

because Cstate depends on the input of the process

and can be small with wrong parameters so:

• Thus the problem is :

• How to define the most pertinent input signal

leading to the minimum uncertainty of the

parameters taking into account the industrial

constraints ? :

• Limited duration of test protocole: Hmax

• Limited amplitude of test signals : Amax

• Limited frequency spectrum : ω < ω max

• The design of the « best protocol» becomes, mainly,

deterministic : iterative learning procedure !0 10 20 30 40 50 60 70 80 90 100

0.4

0.6

0.8

1

1.2

1.4

1.6

H=50

Taum

GA

IN

H=150

H=400

H=2000

H=50

ISO-DISTANCE GAIN / TAU

H=150

H=400

H=2000

2) First principles models

: knowledge based model

• Physical analysis of the process:

• « Brain juice »:– Tough - expensive – risky ...

– Expertise needed in Physics, Chemical engineering ,Mechanics, etc.

• but ….– Generic. Fewer calendar days and man-hours– PHYSICAL ADAPTATION OF THE CONTROLLER

– Strong capitalisation

the future………

Transfer / Training

• Vendors :

– SET POINT (USA Houston)

– P.Latour / P.Grosdidier / Tom Badgwell / Greg Martin

– . YOKOGAWA (Japon. Mitaka)

– . SOTEICA (Argentina / USA)

– . SAMSUNG (Korea)

– . REPSOL (Spain)

– . JGC (Japon / Yokohama)

– . SCHNEIDER ELECTRIC (France )

– . INSTITUT TECH NAGOYA (Japan)

– . UNIV . of Hanghzou (China)

Users….

• AMOCO ( CAN)

• MOBIL OIL ( USA / F)

• TOTAL ( F)

• ATO CHEM ( F)

• LUBRIZOL ( USA. F)

• SOFIPROTEOL( B. F)

• BASF ( B. D)

• DEGUSSA. EVONIK.( D )

• PECHINEY/ALCAN (F )

• ARCELOR-MITTAL (L )

• SANOFI- AVENTIS ( F.D )

• IRA (F) etc….

• VEOLIA (F)

• ..and others..

PID + LOGIC

PFC without Logic

Péchiney Aluminium 1988

Cold Rolling Mills : 16 / 18

Thickness control Ts =5ms

Flatness control Ts=20ms

F.P. models : Self.tuning = speed/ thickness/ metal

+ Low level on line identification of model (lubrification !)

Speed Stdev

PID 250 m/mn 4.5 µm

PFC 1450 m/mn 0.7 µm

Pay Out Time ?

Fichier nivocc1.mat

7000 8000 9000 10000 11000 12000 13000

60

65

70

75

80

85

nivocc1 MV(k) CV(r) Cons(b)

sec

ident. 1

ident. 2

0 0.05 0.1 0.15 0.2 0.25 0.3 0.354.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

5.4

5.5

R=11

nivocc1 ISO-D

tau

K

R=11

ISO-DISTANCES IN THE PARAMETRIC SPACE

Ident. 2

Ident. 1

Comparaison PID / PFC

Mesure du niveau d’acier (70%)

Mesure position du vérin ~88 mm

Mesure de la vitesse ~1.3 m/mn

39

La centrale : une chaudière

1 2 3 4

TA 3

32 MW

4 x 215 t/h

80 bars et 500 °C

TA 4

32 MW

TA 2

52 MW

TA 1

52 MWTS

70 t/hTS

70 t/h

HF HF

VENT270 000 M3/H

VENT270 000 M3/H

Utilitésusine

Dé

ten

te 1

Dé

ten

te 2

pré

lève

me

nt

pré

lève

me

nt

Vapeur moyenne pression13 bars 250 °C

Gaz de haut fourneau enrichi

Gaz de four à coke

Fioul lourd

Goudron

40

4 Steam Generators

500 1000 1500 2000 2500 3000 3500 4000 4500 5000

362

364

366

368

370

372

374

376

378

t30095a-Constdes1(k) Tdes1(r) tsur1(m) Qdes1+360(b)

sec

Qdes t/h

Tdes °C

Set Point Protocol for Tdes (1GV) Superheater Temperature - Cascade Control

PFC2

kd

PFC1.pT1 1+

1

pT1

G.e 3.pR-

.3+

tdes tvapint

Tsur (°C)

Ctdes

gdes

Transf. de contraintes

Constvap

500 °C

Procédé Tdes Procédé Tvap

Qvap (t/h)

++

Tp+1

1 -

nov 05

300°C

410°C

d/dt=50/2 °C/s

0-40 t/h

d/dt=20 t/h/s

T1=21.5s

Le FRC est supposé parfait :Kp=1.35Ti=100s.

gdes~1

PFC1

TRBF1=150sh=2 sTech=2s

kd=a-b*qvap+c*qvap²

Contraintes

xCqdes

Régulateurs

FRC

Cqdes*

rcpyrcpy

+

Charge calorifiqueCharg (%)

MI :K=1 T=5s

pRcheGch .. −

PFC2

h =(Tm1+Tm2+Tm3)*0.3 (=30.3 s)Tech=2s

Zone : largeur = ±5 °C

TRBF2 L = 35 sTRBF2 H =(Tm1+Tm2+Tm3)*3+retard (=350 s)MI du 3 ème ordre

Tdes en boucle fermée : cte de temps = T2

0 1000 2000 3000 4000 5000 6000 7000 8000200

300

400

500

600

sec

tvap (

°C)

et

Qvap (

t/h)

GV-TEMPERATURE VAPEUR en PFC- Consigne:b - Tvap:r - Qvap*2:k t22043a.mat

-30 -20 -10 0 10 20 300

2

4

6

8

10Histogramme de l'écart TVAP - t22043a

Fre

quence e

n %

ECART TVAP en °C

Tvapmoyen=499.7504ecart-type=2.352Min/maxTvap=492.328 507.983tmin/tmax (s)=3000 7000

PFC Histogram

0 1000 2000 3000 4000 5000 6000 7000 8000300

350

400

450

500

550

sec

tvap (

°C)

et

Qvap (

t/h)

GV-TEMPERATURE VAPEUR en PID- Consigne:b - Tvap:r - Qvap*2:k t22043a.mat

-30 -20 -10 0 10 20 300

2

4

6

8

10Histogramme de l'écart TVAP - t22043a

Fre

quence e

n %

ECART TVAP en °C

Tvapmoyen=488.2418ecart-type=3.7752Min/maxTvap=478.784 500.757tmin/tmax (s)=3000 7000

PID Histogram

ECONOMIC EVALUATION

Efficiency of turbines increases with steam temperature

Above 519 °C the blades of the front compressor starts

Creeping ……. and hit the case

Classical problem : « 2 S’s »

« Squeeze the variance and Shift the target »

3°C -------------- 1M€

Original set point : 493°C -- Today: 506°C

Pay Out Time : meaningless…!

• Heating / Ventilation / Air-conditioning

• Europe : 10% increase 2006 - 2007

• Market : 14 MM€

• Energy saving / Global warming

• Ease of implementation

4.1 Moyens mis en oeuvre

OVERHEATING

HP

CLASSICAL COOLING PROCESS: Mollier diagram

LOAD CHANGE PID

-10

-5

0

5

10

15

9:36 10:04 10:33 11:02 11:31 12:00 12:28 12:57

Temps

T°C

0

20

40

60

80

100

120

140

kW

e P

late

lec

Surchauffe Consigne Puissance Electrique P Platelec

!Danger !!!!

LOAD CHANGEPFC

-10,00

-5,00

0,00

5,00

10,00

15,00

10:48 11:16 11:45 12:14 12:43 13:12 13:40 14:09

Temps

T°C

0,00

20,00

40,00

60,00

80,00

100,00

120,00

140,00

kW

e P

late

lec

Surchauffe Consigne Puissance Electrique P Platelec

Danger!

Model Predictive Control and Real-time Optimization Implementation

Policy and Best Practice( from ARC )

97 Increasing

40 Staying Constant

2 Decreasing

Do you see your use of MPC accelerating, staying constant, or declining?

100806040200

Increasing

Staying Constant

Decreasing

69.8 %

28.8 %

1.4 %

112 In-house Implementation and Support

28 Third Party Please specify

Is it your company’s objective to implement and support your MPC yourself or outsource?

In-house Implementation and Support Third Party Please specify

120

110

100

90

80

70

60

50

40

30

20

10

0

80.0 %

20.0 %

33 Refining

28 Chemicals

21 Oil & Gas

15 Other

7 Pulp & Paper

7 Power

7 Electronics

4 Plastics & Rubber

3 Metals

3 Cement & Glass

2 Mining

2 Automotive

1 Aerospace

1 Food & Beverage

1 Machinery

1 Pharmaceuticals

Vertical

35302520151050

Refining

Chemicals

Oil & Gas

Other

Pulp & Paper

Power

Electronics

Plastics & Rubber

Metals

Cement & Glass

Mining

Automotive

Aerospace

Food & Beverage

Machinery

Pharmaceuticals

24.3 %

20.6 %

15.4 %

11.0 %

5.1 %

5.1 %

5.1 %

2.9 %

2.2 %

2.2 %

1.5 %

1.5 %

0.7 %

0.7 %

0.7 %

0.7 % PFC/PPC

in the

Chemical Industry

or : « heat exchange based industry »

a) The higher the added value: the lower the interest in production improvement.!

b) Batch reactor specific problem:

Cooling fluid unstable but Mass Temp. stable !

c) Gap between :

Chemical Engineering / Automatic Control

« School reproduces Industry / Industry reproduces School »

Why Chemistry lags behind Petroleum ?

.. the trend…!

Constraints :

– Oil barrel price is uncertain.

– Lethal Asian competition.

– Quality norms (FDA)

– Ecology

But fortunately :

– Controlers and software are more accessible

– Incoming of a new generation of personnel

– The « crisis » will force Industry to improve!

75

R

TM

Fi, Ti Te

R Reagent

∞

ρM

CP

M VM

dTM

dt

= UA Te

- TM

+ ∆Hx

ρe CPe Ve

dTe

dt= ρe Fi Ti - Te + UA TM - Te

θ(Fi) TM+ TM= Ti

.

1) Fi =ct /MV= Ti CV=TM / level 0 =Ti ? :PFC

2) Ti =ct (!) MV=Fi Parametric Control non linear :PPC

3) MV : Ti and Fi : Enthaplic control (power) :PPC+

4) MV : Pressure of reactor :PFC

4 CONTROL STRATEGIES OF BATCH REACTORS

F1, T1

F2, T2

F, T

F.T =F1.T1+F2.T2 T=λλλλ.T1 +(1- λλλλ).T2

λλλλ = F1/(F1+F2) 0 ≤≤≤≤ λλλλ ≤≤≤≤ 1

Convexity Therorem

Equivalent Temperature Procedure

• . dT/dt +T = T1+( 1 - ) T2

• . dT/dt +T = TEQ

• PFC computes TEQsol ( sol)

• sol is connected to the physical MV by

a non-linear relation

• Non-linear solver outside the dynamics!

tsf

qf, tef

qp, teptsp

)]11

(.exp[1

)]11

(.exp[1

)(

FfFpAU

Ff

Fp

FfFpAU

Qf

−−−

−−−=Γ

Fp, Ff : Thermal flows

Fp = (ρρρρ.Cp)p.Qp Ff = (ρρρρ.Cp)f.Qf.

Physical Model: Heat Exchanger/ Reactor

• Mass balance/ Enthalpy balance

• Parameters : Geometry / Fluid

Characteristics / Flows and Temp

• Heat exchange coeff. U : Wm2/K as a

function of Reynolds/ Prandtl/ Nusselt

• Function of temperature and flows

NATURAL PHYSICAL SELF ADAPTATION OF

THE INTERNAL MODELS OF PFC

0 100 200 300 400 500 600 70010

15

20

25

30

35

40

45

50

55

60

TSP d°C

QP L/H/5

QL L/H/5

TEP d°C

0 20 40 60 80 100 1200

20

40

60

80

100

120

TEMPERATURE DE MASSE

d°

min

92°

Tmax=108.4°

Tinit=42°

Figure 4

EXOTHERMICITY ESTIMATOR

by INVERSE PFC

Pert

R1

R2

Cons1 MV1P SP

SP*M≡≡≡≡PMV2

Cons2=SP

+

+

B

+

+

Pert*

-

+

STRUCTURE and STATE DISTURBANCES

• On line estimator: an example!

DV (n) = actual disturbance

DV*(n) = estimated disturbance

Gp = 1/ gain of process

Gm = 1/ gain of model (same dynamics…)

DV* (n)= DV(n) +Setpoint.( Gm-Gp )

State and structure mismatches in the same

equation !

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0 E S T IM A T IO N D E L ’E X O T H E R M IC IT E

T m a s s e / T m a s s ee s t im é e

T S -T E

T S

E X O T H E R M ICIT E

m in

Figure 331

Rea cta ntFlow

T1 Reactor

Cooler

Steam

T2

T4

T3Heat

Exchanger

BASFBASFBASF

B A S FB A S F

B A S F

DEGUSSA. EVONIK

World n°1 Specialty Chemistry

n° 3 German Chemical industry

49.000 employees

> 100 production sites WW

Technical Center Hanau ( Hesse D.)

Adopts Predictive Control PFC in 2003

Training of Control Staff

Standardization of software and procedures

PFC / PPC at DEGUSSA• Batch reactors:Heat exchangers

• Esterification : Batch time reduction : approx. 10%

• Polymerization: Temperature control error / 10

• Continuous reactors

� Polymerization . Improved reproductibility. Quality

� Other Units:

� Increase of production . Less energy consumption

-----------------------------------------------------------------------

1/01/2009 : > 15 sites WW / 22 workshops / >180 PFC/PPC

8 engineers : work load..?!

Control Systems

INVENSYS PLS 80E (ex Eckhart)

FOXBORO IAS ( HLBL..!)

SIEMENS PLCS7

ABB Freelance 2000

YOKOGAWA DCS 3000

EMERSON DeltaV

In all DCS PFC/PPC is implemented with a generic procedure

: « Structured Text » but mainly by « Block programming »

(IEC1131.3) compatible with Simulink !

- Increase of production

- No more off-specification products

- Economy : Energy / Solvant

- Maintenance cost lower

- Operators : happy : less work load !

-Conclusion : large dissemination…

-------------------------

-But where are the problems ?.......;

- Safety . Repeatability

ECONOMIC IMPACT…

Pay out time : « very short »

04. April 2006 Dr.-Ing. Wolfgang Deis (S-TE-VT-

A-I)

97

Anwendungsbeispiel 1:

Exotherme Batchreaktion

FC

TI

TI

Standard-Regler

für Kühlwasser-

menge

MPC-Regler

für TemperaturNeue Reglerstruktur:

TC


A-I)

99


Exotherme BatchreaktionEntwurf, Simulation und Vorparametrierung in Matlab

(Auszug):

Control structure

PFC PPC plantQQQQf,Stpf,Stpf,Stpf,Stp = f(= f(= f(= f(ττττ)))) PIDTM,Stp

Qf

VTeq ττττ TMQf,Stp

f

f

Q

Qba ⋅+=τ

trbfPFC = trbfPPC / 3 : Back calculation of constraint

ττττ* = f-1(Qf)

Teq*


A-I)

101



0 20 40 60 80 100 12065

70

75

80

85

Zeit [min]

Tem

per

atu

r [°

C] +/- 0,2 °C

neue Fahrweise (MPC-Regler)

hier liegen 35 Batches übereinander !

Fortschritt [%]



0 20 40 60 80 100 12065

70

75

80

85

Zeit [min]

Tem

per

atu

r [°

C]

Fortschritt [%]

+/- 2 °C

alte Fahrweise (PI-Regler)


Endotherme Batchreaktion

0 0.2 0.4 0. 6 0.8 1 1.2 1.4

0

20

40

60

80

100

120

P ID: Reaktortemperatur [% des Sollwertes]

MPC: Reaktor temperatur [ % des Sollwertes]

0 0.2 0.4 0. 6 0.8 1 1.2 1.4

0

20

40

60

80

100

120

P ID: Dampfventil [%]

MPC: Dampf ventil [% ]

PID

0 50 100 150 200 250 300 350 40087

88

89

[%]

T REACTOR SETPOINT

T REACTOR [%]

0 50 100 150 200 250 300 350 40060

80

100

120

[%]

MV [%]

0 50 100 150 200 250 300 350 40072

74

76

78

80

82

time [%]

[%]

LOAD [%]

• Identification

0 50 100 150 200 250 300 35079

80

81

82

mass [%

]

LOAD

0 50 100 150 200 250 300 35060

80

100

T [°C

] PUM

PSIG

NAL [%

]

T INPUT REACTOR [%]

MV [%]

0 50 100 150 200 250 300 35087

88

89

90

Zeit [min.]

T [%

]

T REACTOR [%]

T MODEL [%]

PFC

145 150 155 160 165 170 175 180 185 19087

88

89

[%]

T REACTOR SETPOINT

T REACTOR [%]

145 150 155 160 165 170 175 180 185 19060

80

100

120

[%]

MV [%]

145 150 155 160 165 170 175 180 185 19072

74

76

78

80

82

time [%]

[%]

LOAD [%]

Enthalpic Control

Reaching the temp. plateau SANOFI-AVENTIS

• Pharmaceutical industry

• World n°3 / Continental Europe n°1

• World n°1 :Vaccines

• Personnel > 140.000

• >100 production sites

• Turnover : 29MM euros

• Drugs, vaccines…

SANOFI AVENTIS

PREDICTIVE CONTROL

• Target:

– Improvement of quality of product and repeatability

of recipes. Energy consumption.

– Start : November 2005 .Training of personnel:PFC

- Flexible reactor of the IRA institute in ARLES

Controllers:

- Premium Unity Schneider-Electric

-PFC implemented in native Control Library

Identification Mass Temperature

0 20 40 60 80 100 120 14030

40

50

60

70

80

90

100

min

Tmasse /mod°/ k

Tmasse /processus° /r

Temp.sortie ca lo estimée !?/g

Temp entrée calo° /b

PRE MIE RE ID ENTIFCA TION TMA S S E

20

25

30

35

40

45

50

Endothermicity Estimator and Feed-forward

Internal temperature

Jacket input temp

Endothermicity estimator

0 1 2 3 4 5 6 7 8 9

x 104

0

10

20

30

40

50

60

70

80

90

100Commande en pression à TEC constante

TEC

TMASSE

PRESSION/10

TSC

YES BUT: …..!

« Ok .. But my problem is more complex and very specific… »

Industrial resistance ?

- lack of basic training in modelling

- no clear understanding of hierarchical control

- a priori year budget and no « Pay Out Time » approach

No conflict with PID : both techniques are used:

If PID works well (e.g, FIC) - use it

If not, use another tool…

ASSESSMENT ?

- MATURE TECHNIQUE

- CONTROLERS and TECHNOLOGY : OK !

- Validated by numerous world-wide applications in most

industrial domains

- Short PAY OUT TIME…..

- but…

85% of the work and 95% of the adrenalin

DYNAMIC FP MODELLING

Who is going to Implement

Predictive Control ?Since the main problem lies in Modelling

: The expert in Modelling ? ( Education ?)

- The local industrial Control Instrumentist ?

- The DCS/ PLC vendor ?

- An Engineering Company ?

- -------------------------------------------------------------

..since Control is made « simple », the Control expert has eliminated himself !

Industrial Prospective

Integrated design:

To take into account the posibilities of

Advanced Control in the early design of the

process (e.g: CCV in Aircraft design)

: less steel, less concrete….

: more « brain juice »

Example in Chemical Engineering !

BATCH REACTOR: Enthalpic Control

TIT3

TIT2

TI1

Heating rod : P

Temperature source : ts

SIC2

1 Pump

1 Variator

2 Temp. sensors

2030€

Teaching control in Technical schools

- 39 Technical schools in France

- ≠ 660 Young technicians per year

- PFC was taught to 23 profs of 4 academia

- Lectures and laboratory works on PLCs

and laboratories processes.

- The first 125 young technicians went out

to industry in 2008, completely at ease with

PFC…

To be continued….

Thank you for your attention….

jacques. richalet @ wanadoo.fr

There is a post-script…!

-----------------

ACADEMIC CHALLENGE.!

A little more difficult ?

Time delay Non minimum phase

Integrative Unstable pole

Pure oscillator Time constant

Set point : ramp with no lag error

Constraints on MV and a state variable CV

H(s)= K.exp(-T1.s).(1-T2.s) / s.(1-T3.s).(s2+ω 2).(1+T4.s)

bon appétit…

oxford2 · 2013-08-21 · cv2 p1 mv1 superviseur m2 m1 r1 contrainte cv2 (a) (b) cons1 cv1 sm2 sm1...

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