Practical plantwide process control

1. Practical plantwide process control Sigurd Skogestad, NTNU Thailand, April 2014

2. Part 3 (1h). Advanced controllayer • Design basedon simple elements: • Ratio control • Cascadecontrol • Selectors • Input resetting (valvepositioncontrol) • Split range control • Decouplers(includingphsicallybased) • Whenshouldthese elements be used? • Whenuse MPC instead?

3. Control layers CV1s “Advanced “control CV2s PID u (valves)

4. Outline • Skogestad procedure for control structure design I Top Down • Step S1: Define operational objective (cost) and constraints • Step S2: Identify degrees of freedom and optimize operation for disturbances • Step S3: Implementation of optimal operation • What to control ? (primary CV’s) (self-optimizing control) • Step S4: Where set the production rate? (Inventory control) II Bottom Up • Step S5: Regulatory control: What more to control (secondary CV’s) ? • Distillation example • Step S6: Supervisory control • Step S7: Real-time optimization

5. Control configuration elements • Control configuration. The restrictions imposed on the overall controller by decomposing it into a set of local controllers (subcontrollers, units, elements, blocks) with predetermined links and with a possibly predetermined design sequence where subcontrollers are designed locally. Some control configuration elements: • Cascade controllers • Decentralized controllers • Feedforward elements • Decoupling elements • Input resetting/Valve position control/Midranging control • Split-range control • Selectors

6. Most important control structures • Feedback control • Ratio control (special case of feedforward) • Cascade control

7. Ratio control (most common case of feedforward) General: Use for extensive variables (usually flows) with constant optimal ratio Example: Process with two feeds q1(d) and q2 (u), where ratio should be constant. (q2/q1)s (desired flow ratio) Use multiplication block (x): q1 (measured flow disturbance) q2 (MV: manipulated variable) x “Measure disturbance (d=q1) and adjust input (u=q2) such that ratio is at given value (q2/q1)s”

8. Usually: Combine ratio (feedforward) with feedback • Adjust (q1/q2)s based on feedback from process, for example, composition controller. • This is a special case of cascade control • Example cake baking: Use recipe (ratio control = feedforward), but adjust ratio if result is not as desired (feedback) • Example evaporator: Fix ratio qH/qF (and use feedback from T to fine tune ratio)

9. Example

10. Cascade control • Controller (“master”) gives setpoint to another controller (“slave”) • Without cascade: “Master” controller directly adjusts u (input, MV) to control y • With cascade: Local “slave” controller uses u to control “extra”/fast measurement (y’). “Master” controller adjusts setpoint y’s. • Example: Flow controller on valve (very common!) • y = level H in tank (or could be temperature etc.) • u = valve position (z) • y’ = flowrate q through valve flow in Hs flow in Hs H H LC LC MV=z valve position MV=qs q FC measured flow z flow out flow out WITHOUT CASCADE WITH CASCADE

11. What are the benefits of adding a flow controller (inner cascade)? qs Extra measurement y’ = q q z f(z) • Counteracts nonlinearity in valve, f(z) • With fast flow control we can assume q = qs • Eliminates effect of disturbances in p1 and p2 1 linear valve 0 0 z (valve opening) 1

12. ∞ Example (again): Evaporator with heating qF [m3/s] TF [K] cF [mol/m3] evaporation From reactor level measurement H temperature measurement T q [m3/s] T [K] c [mol/m3] Heating fluid qH [m3/s] TH [K] concentrate NEW Control objective • Keep level H at desired value • Keep composition c (rather than temperature T) at desired value BUT: Composition measurement has large delay + unreliable Suggest control structure based on cascade control

13. Split Range Temperature Control

14. Split Range Temperature Control E0 Note: adjust the location er E0 to make process gains equal

15. Sigurd’s pairing rule for decentralized control: “Pair MV that may (optimally) saturate with CV that may be given up” • Reason: Minimizes need for reassigning loops • Important: Always feasible (and optimal) to give up a CV when optimal MV saturation occurs. • Proof (DOF analysis): When one MV disappears (saturates), then we have one less optimal CV.

16. Use of extra measurements: Cascade control (conventional) The reference r2 (= setpoint ys2) is an output from another controller General case (“parallel cascade”) Not always helpful… y2 must be closely related to y1 Special common case (“series cascade”)

17. Series cascade • Disturbances arising within the secondary loop (before y2) are corrected by the secondary controller before they can influence the primary variable y1 • Phase lag existing in the secondary part of the process (G2) is reduced by the secondary loop. This improves the speed of response of the primary loop. • Gain variations in G2 are overcome within its own loop. Thus, use cascade control (with an extra secondary measurement y2) when: • The disturbance d2 is significant and G1 has an effective delay • The plant G2 is uncertain (varies) or nonlinear Design / tuning (see also in tuning-part): • First design K2 (“fast loop”) to deal with d2 • Then design K1 to deal with d1 Example: Flow cascade for level control u = z, y2=F, y1=M, K1= LC, K2= FC

18. Use of extra inputs Two different cases • Have extra dynamic inputs (degrees of freedom) Cascade implementation: “Input resetting to ideal resting value” Example: Heat exchanger with extra bypass Also known as: Midranging control, valve position control • Need several inputs to cover whole range (because primary input may saturate) (steady-state) Split-range control Example 1: Control of room temperature using AC (summer), heater (winter), fireplace (winter cold) Example 2: Pressure control using purge and inert feed (distillation)

19. Extra inputs, dynamically • Exercise: Explain how “valve position control” fits into this framework. As en example consider a heat exchanger with bypass

20. QUIZ: Heat exchanger with bypass closed qB Thot • Want tight control of Thot • Primary input: CW • Secondary input: qB • Proposed control structure?

21. qB Thot Alternative 1 closed TC Use primary input CW: TOO SLOW

22. qB Thot Alternative 2 closed TC Use “dynamic” input qB Advantage: Very fast response (no delay) Problem: qB is too small to cover whole range + has small steady-state effect

23. qB Thot Alternative 3: Use both inputs (with input resetting of dynamic input) closed qBs FC TC TC: Gives fast control of Thot using the “dynamic” input qB FC: Resets qB to its setpoint (IRV) (e.g. 5%) using the “primary” input CW IRV = ideal resting value Also called: “valve position control” (Shinskey) and “midranging control” (Sweden)

24. Too few inputs • Must decide which output (CV) has the highest priority • Selectors • Implementation: Several controllers have the same MV • Selects max or min MV value • Often used to handle changes in active constraints • Example: one heater for two rooms. Both rooms: T>20C • Max-selector • One room will be warmer than setpoint. • Example: Petlyuk distillation column • Heat input (V) is used to control three compositions using max-selector • Two will be better than setpoint (“overpurified”) at any given time

25. Control of primary variables • Purpose: Keep primary controlled outputs c=y1 at optimal setpoints cs • Degrees of freedom: Setpoints y2s in reg.control layer • Main structural issue:Decentralized or multivariable?

26. Decentralized control(single-loop controllers) Use for: Noninteracting process and no change in active constraints + Tuning may be done on-line + No or minimal model requirements + Easy to fix and change - Need to determine pairing - Performance loss compared to multivariable control - Complicated logic required for reconfiguration when active constraints move

27. Multivariable control(with explicit constraint handling = MPC) Use for: Interacting process and changes in active constraints + Easy handling of feedforward control + Easy handling of changing constraints • no need for logic • smooth transition - Requires multivariable dynamic model - Tuning may be difficult - Less transparent - “Everything goes down at the same time”

28. ”Summary Advanced control” STEP S6. SUPERVISORY LAYER Objectives of supervisory layer: 1. Switch control structures (CV1) depending on operating region • Active constraints • self-optimizing variables 2. Perform “advanced” economic/coordination control tasks. • Control primary variables CV1 at setpoint using as degrees of freedom (MV): • Setpoints to the regulatory layer (CV2s) • ”unused” degrees of freedom (valves) • Keep an eye on stabilizing layer • Avoid saturation in stabilizing layer • Feedforward from disturbances • If helpful • Make use of extra inputs • Make use of extra measurements Implementation: • Alternative 1: Advanced control based on ”simple elements” (decentralized control) • Alternative 2: MPC

29. Summary of some simple elements Feeforwardcontrolwith Multiple feedsetc. (extensive variables).: Ratio control • Ratio setpointusuallyset by feedback in a cascademanner Feedback • Useofextrameasurements for improvedcontrol;: Cascadecontrol • Cascadecontrol is when MV (for master) =setpoint to slave controller • MV1 = CV2s • Switch betweenactiveconstraints: Selectors • Make useofextra inputs • Dynamic (improveperformance): Input resetting = valvepositioncontrol = midrangingcontrol • Steady state (extend operating range): Split range control • Reduceinteractionswhenusing single-loop control: Decouplers (includingphsicallybased)

30. Model predictive control (MPC) = “online optimal control” ydev=y-ys udev=u-us Discretize in time: Note: Implement only current input Δu1

31. Implementation MPC project(Stig Strand, Statoil) • Initial MV/CV/DV selection • DCS preparation (controller tuning, instrumentation, MV handles, communication logics etc) • Control room operator pre-training and motivation • Product quality control  Data collection (process/lab)  Inferential model • MV/DV step testing  dynamic models • Model judgement/singularity analysis  remove models? change models? • MPC pre-tuning by simulation  MPC activation – step by step and with care – challenging different constraint combinations – adjust models? • Control room operator training • MPC in normal operation, with at least 99% service factor DCS = “distributed control system” = Basic PID control layer

32. PDC 1021 24-HA-103 A/B 24-VA-102 24-PA-102A/B 39 21 6 17 33 34 18 1 5 48 40 35 20 LC TI 1001 1005 24LC1001.VYA 24 24 24 24 24 24 24 24 24 24 25 24 24 24 24 24 24 24 24 24 TI TI LC LC PC TI TI TI FI TI TI TI PI HC PC LC FC 1021 1018 1026 1020 1010 1015 1012 1020 1014 1013 1038 1011 1003 1009 1017 1009 1010 Depropaniser Train 100 – 24-VE-107 Flare 24 B = C2 C = C3 D = iC4 AR 1008 Kjølevann 24 FC 1008 Propane Bottoms from deetaniser 24 PD 1009 Normally 0 flow, used for start-ups to remove inerts Controlled variables (CV) = Product qualities, column deltaP ++ 24 TC 1022 Manipulated variables (MV) =Set points to PID controllers 24 Disturbance variables (DV) = Feedforward C = C3 E = nC4 F = C5+ AR 1005 24-VE-107 LP steam Debutaniser 24-VE-108 LP condensate

33. Depropaniser Train100 step testing • 3 days – normal operation during night DV =Feedrate MV1 = L MV2 = Ts CV1=TOP COMPOSITION CV2=BOTTOM COMPOSITION CV3=¢p

34. Estimator: inferential models • Analyser responses are delayed – temperature measurements respond 20 min earlier • Combine temperature measurements  predicts product qualities well CV1=TOP COMPOSITION Calculated by 24TI1011 (tray 39) CV2=BOTTOM COMPOSITION Calculated by 24TC1022 (t5), 24TI1018 (bottom), 24TI1012 (t17) and 24TI1011 (t39)

35. Depropaniser Train100 step testing – Final model • Step response models: • MV1=reflux set point increase of 1 kg/h • MV2=temperature set point increase of 1 degree C • DV=output increase of 1%. MV1 = L MV2 = Ts DV =Feedrate CV1=TOP COMPOSITION 3 t 20 min CV2=BOTTOM COMPOSITION CV3=¢p

36. Depropaniser Train100 MPC – controller activation • Starts with 1 MV and 1 CV – CV set point changes, controller tuning, model verification and corrections • Shifts to another MV/CV pair, same procedure • Interactions verified – controls 2x2 system (2 MV + 2 CV) • Expects 3 – 5 days tuning with set point changes to achieve satisfactory performance MV1 = L CV1=TOP COMPOSITION MV2 = Ts CV2=BOTTOM COMPOSITION DV =Feedrate CV3=¢p

37. Another column: Deethanizer Heat ex Reflux drum Reflux pumps 34 23 28 10 1 20 21 16 LC TC LC FC PC FC LC FC PC FC 0 – 65% 65-100% CV Flare Fuel gas to boilers Propane Feed from stabilizators DV Product pumps MV MV Quality estimator CV CV LP Steam Quality estimator LP Condensate To Depropaniser

38. Top: Binary separation in this caseQuality estimator vs. gas chromatograph(use logarithmic composition to reduce nonlinearity, CV = - lnximpurity) 7 temperatures 2 temperatures =little difference if the right temperatures are chosen

39. The final test: MPC in closed-loop CV1 MV1 CV2 MV2 CV3 DV

40. Conclusion MPC • Generally simpler than previous advanced control • Well accepted by operators • Statoil: Use of in-house technology and expertise successful

41. Outline • Skogestad procedure for control structure design I Top Down • Step S1: Define operational objective (cost) and constraints • Step S2: Identify degrees of freedom and optimize operation for disturbances • Step S3: Implementation of optimal operation • What to control ? (primary CV’s) (self-optimizing control) • Step S4: Where set the production rate? (Inventory control) II Bottom Up • Step S5: Regulatory control: What more to control (secondary CV’s) ? • Step S6: Supervisory control • Step S7: Real-time optimization

42. Sigurd Skogestad Optimization layer (RTO) • Purpose: Identify active constraints and compute optimal setpoints (to be implemented by control layer) RTO CVs MPC PID MVs Process

43. An RTO sucess story: Statoil Mongstad Crude oil preheat train Max T 20 heat exchangers, 5 DOFs (splits), 85 flow andf temperature measurments

44. Symposium Chemical Process Control 6, Tucson, Arizona, 7-12 Jan. 2001, Preprints pp. 476-480. Published in AIChE Symposium Series, 98 (326), pp. 403-407. ISBN 0-8169-0869-9 (2002).

45. European Symposium on Computer Aided Process Engineering 11, Kolding, Denmark, 27-30 May 2001, Elsevier, pp. 1041-1046.

46. Data reconcilation ”All” variables are reconciled: Flows, feed temperatures, UA-values....

47. Optimization: 2% energy reduction In service for 20 years

48. Improvements Max T 20 heat exchangers, 5 DOFs (splits), 85 flow and temperature measurements

49. An RTO failure: Complete Statoil Kårstø gas processing plant Plan: 20 + distillation columns, 4 parallel trains, steam system,...

50. Alternative to Real-Time Opimization: Indirect optimization using control layer Use off-line optimization to identify controlled variables (CV): - Active constraints - Self-optimizing variables RTO CVs MPC PID MVs Process