PREDICTIVE FUNCTIONAL CONTROL. J.Richalet SCILAB 2014

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1 PREDICTIVE FUNCTIONAL CONTROL J.Richalet SCILAB 2014

2 WHO INVENTED PREDICTIVE CONTROL?

3 ERGONOMY OF FLIGHT CONTROL n In the fifties : n How the pilot manages his aircraft? n What operating image? n Comparison between: n Human control and Automatic control : SURPRISE!.

4 Jean Piaget (1896 / 1980) n Swiss Grand Father of Cognitive Psychology n How the child learns and controls his environment. Four phases.!

5 4 PIAGET BASIC PRINCIPLES OPERATING IMAGE -INTERNAL MODEL TARGET Sub TARGET -REFERENCE TRAJECTORY ACTION -STRUCTURATION OF THE MV COMPARISON BETWEEN PREDICTED AND ACHIEVED RESULT -ERROR COMPENSATION NATURAL CONTROL : YOU DO NOT DRIVE YOUR CAR WITH A PID SCHEME

6 PREDICTIVE CONTROL IS NOT AN INVENTION BUT A DISCOVERY.!

7 FIRST INDUSTRIAL APPLICATIONS OF P.F.C. DEFENCE Mine hunter Ariane 5 attitude control Missile autopilot Laser guided missile (t=62 microsecond ) Gun turrets Radar antennas Infra Red camera High speed Infra Red Missile launch Camera mount Radar antennas Laser mirror Tank turret (T. 55) Mine sweeper auto-pilot Aircraft carrier auto-pilot AUTOMOTIVE Gear box test bench Dynamic test bench engine Fuel injection Idle fuel injection Clutch antistroke Gear box (tank) Hybrid car (electric-fuel) Air conditioning METALLURGICAL INDUSTRIES Coating lines aluminium Thickness control - Roll eccentricity Mono-multi-stands Rolling mills Continuous casting (slab) Push-ovens Coke furnace Hot/cold/Thin rolling mills Steam generators Level, temperature, pressure ; MISCELLANEOUS (Chem reactors and distillation excluded) Bioreactor Melissa ESA Plastic extrusion robot (high speed) Temperature control of gas furnace Temperature control of TGV train carriage River dam level control (T=1 hour ) Powder milk dryer Electric furnace brazing etc. FEATURES the oldest MBPC 1968 A few thousands applications in many fields

8 ELEMENTARY TUTORIAL EXAMPLE G e-θ s P = = M = 1 + Ts y u y(n) = α y(n-1) + (1- α). u(n-1-r). G Processus with delay y Pr (n): θ = Tsamp. r -Tsamp α = e T = y P (n) + y M (n) - y M (n-r) Target : Δ P (n+h) = (C - y Pr ) (1 - λ H ) Trajectory expo. : λ 1 coincidence point: H 1 Base function: step Model : Free mode Forced mode (Liebniz 1674) Control equation : y M(n) αh u(n). GM 1 - α H Model increment : Increment = Free(n+h) + Forced(n+h)- ymodel(n) The only mathematical problem for trainees! ΔP(n +H) = ΔM(n +H) (C - H H H y (n)) (1 - ) = (n) + u(n) GM(1 - ) - (n) P y ym r λ M α α (C - H y (n)) (1 - ) Pr λ ym (n) u(n) = + GM(1 - H α ) GM

9 d TEMPERATURE DE MASSE Tmax= Tinit=42 20 min Figure 4

10 ON LINE ESTIMATOR of DISTURBANCES Pert B Cons1 R1 MV1 + + P + + SP Cons2=SP R2 MV2 M P SP* - + Pert*

11 120 ESTIMATION DE L EXOTHERMICITE 100 Tmasse/ Tmasse estimée EXOTHERMIC ITE 20 TS 0 TS- TE min Figure 3

12 31 T2 Reactant Flow Steam Cooler T3 T4 Heat Exchanger T1 Reactor BASF

13 B A S F

14

15 4 CONTROL STRATEGIES OF BATCH REACTORS R R Reagent ρ. Δ ρ. ρ T M F i, T i T e. q ( F i ) T M + T M = T i 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 15

16 Batch Reactor : MV=Qf / CV= TM DEGUSSA EVONIK TM Qf Tof Tif

17 Control PID Degussa / EVONIK +/- 2 C temperature [ C] 39 different batches with PID time Datum Name der Präsentation Seite 17

18 Control PFC Degussa / EVONIK 32 different batches with PPC temperature [ C] +/- 0,2 C time Datum Name der Präsentation Seite 18

19

20 CONTINUOUS CASTING

21 Steel ladle 335 t, 1550 C Setpoint weight Stopper A PFC t SP=70% PFC position detection PI A nozzle Weight detection tundish 60 t capteur Steel level Mould mm, Cool jacket rolling

22 IDENTIFICATION - Ultra-low level test signals!!. «I do want to see your test signal on the level..!«- Set of harmonics signals Eigen function - High level Parallel filtering.- Different metal alloys

23 COMPLEX ALGEBRA PREDICTIVE CONTROL?! BULGING COMPLEX CONTROL without with STOPPER Amp sinus 0 Bmp cosinus -10 Frequency reference

24 Bench test of pump gaskets - Flow : m3/h.!? - Pressure : MPa - Temperature : 330 d

25 CONTROL ENVIRONMENT Pressuriser Steam generator REACTOR PUMP PFC controller

26 : " Size : 10 m " Mass : 100 tons " Flow: ~35000 m3/h Engine Pump Support PFC control joints

27 Bâche tampon ROO1 Température T0 T 31 Vanne Chaud VC Transmetteur Débit T 30 Vanne Froid VF Température T51 Banc d essai Transmetteur Pression Transmetteur Débit Surpresseur Température T5

28 Prise en tendance de la température d entrée F(T0) Convexité Echangeur Chaud T5 T0 T31 Débit source chaude T0 TEQ = L x T31 + ( 1 L ) x T0 Consigne de température Convexité Echangeur Froid PFC CHAUD PFC FROID Logique de choix de l action (Chaud ou Froid) Régulation débit chaud Régulation débit froid T31 T30 T51 TEQ = L x T30 + ( 1 L ) x T51 T5 T5 T51 T30 Débit source froide Prise en tendance de la température d entrée F(T51)

29 PID 65,0 55,0 Température ( C) position vanne (%) Essai A2si du 17 avril 2012 (673) 45,0 35,0 25,0 15,0 Temps (heures) 18:14:24 18:21:36 18:28:48 18:36:00 18:43:12 18:50:24 18:57:36 19:04:48

30 PFC 100,0 80,0 Température ( C) position vanne (%) Essai A2si du 18 avril 2012 (676) 60,0 40,0 T Inj 20,0 Consigne retard +3 mn 0,0 Temps (heures) 14:31:12 14:45:36 15:00:00 15:14:24 15:28:48 15:43:12 15:57:36 16:12:00 16:26:24

31 VERTOLAY SANOFI VITRY sur SEINE ARAMON MONTPELLIER KÖLN ELBEUF Training of staff: Transfer of Know How

32

33

34 SCILAB Process PFC program Control

35 CODE SCILAB // com_elem_kindergarten tf= 1500; w=1:1:tf; // temps // initialisation u=zeros(1,tf); tech=1; MV=u; CV=u; sm=u; DV=u; SP=u; r=200; // dynamiques du modèle et du processus, // constante de temps, gain : taum=35; am=exp(-tech/taum); bm=1-am; Km=1.7; taup=35; ap=exp(-tech/taup); bp=1-ap; Kp=1.7; TRBF=45; lh=1-exp(-tech*3/trbf); // for ii=2+r:1:tf, if ii<700 then DV(ii)= 0; else DV(ii)=20; end // perturbation SP(ii)=100; // point de consigne CV(ii)=ap*CV(ii-1)+bp*(Kp*MV(ii-1-r)+DV(ii)); // processus sm(ii)=am*sm(ii-1)+bm*km*mv(ii-1); // modèle spred=cv(ii)+(sm(ii)-sm(ii-r))*1; // prédiction d=(sp(ii)-spred)*lh+sm(ii)*bm; MV(ii)=d/(Km*bm); // variable manipulée // contraintes if MV(ii)>70 then MV(ii)=70; end if MV(ii)>MV(ii-1)+4 then MV(ii)=MV(ii-1)+4; end if MV(ii)<MV(ii-1)-4 then MV(ii)=MV(ii-1)-4; end end // scf(1); clf; plot(w,mv,'b',w,cv,'r',w,sp,'r',w,dv,'m'); a=gca(); a.grid=[1,1]; a.tight_limits="on"; a.data_bounds=[1,0;1500,150]; xlabel('sec'); xtitle('mv b, CV et SP r, DV m, R=200!,.. contraintes : abs=60 / vitesse = 4');

36 Conclusion n Dissemination? : where is the problem?: n The technical and economic efficiency of is clearly demonstrated on many different processes n TEACHING PFC: n Continuous education of : n n n Teachers of technical schools Industrial operators PFC Implementation of PFC in all new PLC s is to be continued

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