O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.

ScilabTEC 2015 - J. Richalet

2.549 visualizações

Publicada em

"Predictive Functional Control"
By Jacques Richalet for ScilabTEC 2015

  • Seja o primeiro a comentar

  • Seja a primeira pessoa a gostar disto

ScilabTEC 2015 - J. Richalet

  1. 1. PREDICTIVE FUNCTIONAL CONTROL                                            J.Richalet 2015
  2. 2. WHO INVENTED PREDICTIVE CONTROL ?
  3. 3.  ERGONOMY OF FLIGHT CONTROL •  In the fifties : •  How the pilot manages his aircraft ? •  What operating image ? •  Comparison between: •  Human control and Automatic control : SURPRISE ! ….    
  4. 4.  Jean Piaget (1896 / 1980) •  Swiss Grand Father of Cognitive Psychology •  How the child learns and controls his environment . Four phases ….!      
  5. 5. 4 PIAGET BASIC PRINCIPLES –  OPERATING IMAGE –  TARGET – Sub TARGET –  ACTION – COMPARISON BETWEEN PREDICTED AND ACHIEVED RESULT – INTERNAL MODEL – REFERENCE TRAJECTORY – STRUCTURATION OF THE MV –  ERROR COMPENSATION • NATURAL CONTROL : “ YOU DO NOT DRIVE YOUR CAR WITH A PID SCHEME”
  6. 6. PREDICTIVE CONTROL IS NOT AN INVENTION BUT A DISCOVERY ….!
  7. 7. Pr H(C - (n)) (1 - ) (n)yy Mu(n) + H GMGM(1 - ) λ α = P = G - se 1 + Ts M y u θ = = y(n) = α y(n-1) + (1- α) . u(n-1-r) . Gα = e -Tsamp T θ = Tsamp . r ELEMENTARY TUTORIAL EXAMPLE Trajectory expo. : λ 1 coincidence point: H 1 Base function: step Target : ΔP(n+H) = (C - yPr) (1 - λH) Processus with delay :yPr (n) = yP (n) + yM (n) - yM(n-r) Model : Free mode Forced mode (Liebniz 1674) My (n) Hα Hu(n) . GM 1 - α ⎛ ⎞ ⎜ ⎟ ⎝ ⎠ Model increment : M MP H H H(C - (n)) (1 - ) (n) + u(n) GM(1 - ) - (n)y y yr λ α α= Control equation : P(n+H) M(n+H)Δ Δ= Increment = Free(n+h) + Forced(n+h)- ymodel(n) The only mathematical problem for trainees …!
  8. 8. BAYER reactor
  9. 9. 0 20 40 60 80 100 120 0 20 40 60 80 100 120 TEMPERATURE DE MASSE d° min 92° Tmax=108.4° Tinit=42° Figure 4
  10. 10. ON LINE ESTIMATOR of DISTURBANCES Pert R1 R2 Cons1 MV1 P SP SP* M≡P MV2 Cons2=SP + + B + + Pert* - +
  11. 11. 0 10 20 30 40 50 60 70 80 0 20 40 60 80 100 120 ESTIMATION DE L‘EXOTHERMICITE Tmasse/ Tmasse estimée TS- TE TS EXOTHERMIC ITE min Figure 3
  12. 12. 31 Reactant Flow T1 Reactor Cooler Steam T2 T4 T3 Heat Exchanger BASFBASFBASF
  13. 13. B A S F
  14. 14. 15 R TM Fi, Ti Te R Reagent ¥ r M C P MV M dT M dt = UA T e - T M + DHx reCPe Ve dTe dt = reFi Ti- Te + UA TM- Te q(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
  15. 15. Batch Reactor : MV=Qf / CV= TM DEGUSSA EVONIK Qf Tif Tof TM
  16. 16.  Control PID Degussa / EVONIK Datum | Name der Präsentation Seite | 17 time temperature[°C] +/- 2 °C 39 different batches with PID
  17. 17. Control PFC Degussa / EVONIK Datum | Name der Präsentation Seite | 18 time temperature[°C] 32 different batches with PPC +/- 0,2 °C
  18. 18. CONTINUOUS CASTING
  19. 19. poche de 335 t d’acier liquide à 1550 °C distributeur de 60 t lingotière à largeur variable 850-2200 mm, refroidie à l’eau capteur radio-actif consigne=70% PFCPFC mesure de niveau cages d’extraction consigne de poids tquenouille PFC PI vérin mesure de position busette rotative busette à 2 ouïes A A mesure de poids amplificateur de puissance
  20. 20.   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    
  21. 21. -10 0 10 20 30 40 50 60 70 BULGING COMPLEX CONTROL without with STOPPER Amp sinus Bmp cosinus Frequency reference -10 0 10 20 30 40 50 60 70 -10 0 10 20 30 40 50 60 70 BULGING COMPLEX CONTROL without BULGING COMPLEX CONTROL without with STOPPER Amp sinus Bmp cosinus Frequency reference COMPLEX ALGEBRA PREDICTIVE CONTROL ?!
  22. 22.      -­‐ Bench test of pump gaskets   -­‐      Flow : 35000 m3/h ….! ? - Pressure : 17.50 MPa - Temperature : 330 d°  
  23. 23. PFC controller PUMP CONTROL ENVIRONMENT Pressuriser REACTOR Steam generatorr
  24. 24. PFC control joints Engine Pump : " Size : 10 m " Mass : 100 tons " Flow: ~35000 m3/h Support
  25. 25. PFC CHAUD Convexité Echangeur Chaud TEQ = L x T31 + ( 1 – L ) x T0 Prise en tendance de la température d’entrée F(T0) PFC FROID Convexité Echangeur Froid TEQ = L x T30 + ( 1 – L ) x T51 Prise en tendance de la température d’entrée F(T51) Logique de choix de l’action (Chaud ou Froid) T5 T0 T31 T5 T51 T30 Consigne de température Régulation débit chaud Régulation débit froid Débit source chaude Débit source froide T0 T51 T5 T31 T30
  26. 26. PID Essai A2si du 17 avril 2012 (673) 15,0 25,0 35,0 45,0 55,0 65,0 18:14:24 18:21:36 18:28:48 18:36:00 18:43:12 18:50:24 18:57:36 19:04:48 Temps (heures) Température(°C) positionvanne(%)
  27. 27. PFC Essai A2si du 18 avril 2012 (676) 0,0 20,0 40,0 60,0 80,0 100,0 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 Temps (heures) Température(°C) positionvanne(%) T Inj Consigne retard +3 mn
  28. 28. F1, T1 F2, T2 F, T F.T =F1.T1+F2.T2 (enthalpic balance) T=λ.T1 +(1- λ).T2 with 0 ≤ λ ≤ 1 Hyperbolic function ! λ = F1/(F1+F2) Convexity Theorem
  29. 29. tsf qf, tef qp, teptsp )] 11 (.exp[1 )] 11 (.exp[1 )( FfFp AU Ff Fp FfFp AU Qf −−− −−− =Γ Fp, Ff : Thermal flows Fp = (ρ.Cp)p.Qp Ff = (ρ.Cp)f.Qf.
  30. 30.   SANOFI       VERTOLAY VITRY sur SEINE ARAMON MONTPELLIER KÖLN Training of staff: Transfer of Know How
  31. 31. DIFFUSION  and  PROMOTION   •  Training  in  many  countries   •  Universi@es,  technical  schools  (IRA  in  France)   •  Training  of  professors   •  Con@nuous  educa@on  of:   –   Teachers  of  technical  schools   –   Industrial    operators   •  Documenta@on:  “Techniques  de  l’ingénieur”  
  32. 32. Prac@cal  implementa@on  with   Scilab   •   The  Scilab  PFC  book:   Text  +  Diagram  +  Program  Code   •  45  programs  in  Scilab  implemen@ng  PFC   control:   – Elementary   – Ordinary   – Advanced  
  33. 33. Elementary  example:  control  of  an  integrator  process  with  delay   // E_com_intg12 // process H(s) :integrator with delay : H(s)=exp(-R.s)/s clear; xdel(winsid());clc tf=1200;w=1:1:tf; u=zeros(1,tf); MV=u;CV=u; SP=u;sm=u;DV=u; tech=1;//sampling period r=50; // dealy R=r*tech G=0.01; // proportional gain tau=1/G; // closed loop time constant am=exp(-tech/tau); bm=1-am; //model parameters trbf=250; lh=1-exp(-tech*3/trbf); SP=100; // set point for ii =2+r:1:tf, // perturbation if ii>600 then DV(ii)=-0.2; end // MV proportional loop ec(ii)=MV(ii-1-r)-CV(ii-1); // error MVp(ii)=G*ec(ii); CV(ii)=CV(ii-1)+MVp(ii)+DV(ii); // pfc sm(ii)=sm(ii-1)*am+bm*MV(ii-1); // model spred=CV(ii)+DV(ii)*1+sm(ii)-sm(ii-r); d=(SP-spred)*lh+sm(ii)*bm; MV(ii)=d/bm; // MV pfc end scf(0) plot(w,SP*ones(w),'k',w,CV,'r',w,DV*100, 'k',w,MV,'b') a=gca(); a.grid=[1,1]; a.tight_limits="on"; a.data_bounds=[1,-40;tf,140]; xlabel('sec'); xtitle('Control of a process : integrator + delay CV r / MV b / DV*100 k')
  34. 34. Ordinary  example:  control  of  a  loop  with  constraint  transfer   // O_pfc_transparent // back calculation, transparent control of level clear,xdel(winsid()),clc tf=1000; //duration of test w=1:1:tf; //time; tech=1;//sampling period u=zeros(1,tf); CV=u;//process output MVp=u; MV=u;//manipulated variable eps=u; //error sm=u; //output of model k=0.1; // gain of integrative process level Kp=0.07; // gain of P(ID) tau=143; // =1/(k*Kp) am=exp(-tech/tau); bm=1-am; //time constant of internal closed loop system by P(ID) trbf=200; //desired closed loop system by PFC: only tuning factor h=1; lh=1-exp(-tech*3*h/trbf); fl=input('fl (1 with back calculation / 0 without back calculation) = '); MVmax=5; //maxvalue of mv SP(1:tf)=100; for ii=2:1:tf, eps(ii)=MV(ii-1)-CV(ii-1); // error of P(ID) MVp(ii)=Kp*eps(ii); // manipulated variable of P(ID) if MVp(ii)>MVmax then MVp(ii)=MVmax;end // constraint on the mvp mvbc=(MVp(ii)/Kp)+CV(ii-1); // back calculation CV(ii)=CV(ii-1)+k*MVp(ii-1);// output of process sm(ii)=sm(ii-1)*am+bm*(fl*mvbc+(1-fl)*MV(ii-1));// internal model of PFC with/without transfer of constraint d =(SP(ii)-CV(ii))*lh+sm(ii)*bm; // computation of mv MV(ii)=d/bm; // computation of mv end scf(0) plot(w,SP','k',w,MVp*10,'m',w,CV,'r',w,MV,'b'); a=gca(); a.grid=[1,1]; a.tight_limits="on"; a.data_bounds=[2,0;tf,230]; xlabel('sec') if fl==0 then title('without back calculation SP k / MVp*10 m / CV r / MV b'),end if fl==1 then title('with back calculation SP k / MVp*10 m / CV r / MV b'), end
  35. 35. Conclusion   •  Dissemination? : where is the problem? •  The technical and economic efficiency of PFC is clearly demonstrated on many different processes World Wide •  Implementing PFC: the Scilab PFC book

×