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TRANSCRIPT
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
140REFERENCE TRAJECTORY at ti=60
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
140REFERENCE TRAJECTORY at ti=100
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
140REFERENCE TRAJECTORY at ti=150
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
140REFERENCE TRAJECTORY at ti=200
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
140REFERENCE TRAJECTORY at ti=300
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
04. April 2006 Dr.-Ing. Wolfgang Deis (S-TE-VT-
A-I)
99
Anwendungsbeispiel 1:
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*
04. April 2006 Dr.-Ing. Wolfgang Deis (S-TE-VT-
A-I)
101
Anwendungsbeispiel 1:
Exotherme Batchreaktion
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 [%]
Anwendungsbeispiel 1:
Exotherme Batchreaktion
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)
Anwendungsbeispiel 2:
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…