1. Part 5: Advanced control + case studies
Thu AM
Part 5: Advanced control + case studies
Advanced control layer (1h)
Design based on simple elements:
Ratio control
Cascade control
Selectors
Input resetting (valve position control)
Split range control
Decouplers (including phsically based)
When should these elements be used?
When use MPC instead?
Case studies (3h)
Example: Distillation column control
Example: Plantwide control of complete plant Recycle processes: How to avoid snowballing

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

3. 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

4. ”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

5. Summary of some simple elements
Feeforward control with Multiple feeds etc. (extensive variables).: Ratio control
Ratio setpoint usually set by feedback in a cascade manner
Feedback
Use of extra measurements for improved control;: Cascade control
Cascade control is when MV (for master) =setpoint to slave controller
MV1 = CV2s
Switch between active constraints: Selectors
Make use of extra inputs
Dynamic (improve performance): Input resetting = valve position control = midranging control
Steady state (extend operating range): Split range control
Reduce interactions when using single-loop control: Decouplers (including phsically based)

6. 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

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

8. 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.
Use multiplication block (x): x
(q2/q1)s
(desired flow ratio) q1
(measured
flow
disturbance) q2
(MV: manipulated variable)
“Measure disturbance (d=q1) and adjust input (u=q2) such that
ratio is at given value (q2/q1)s”

9. 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)

10. Cascade control Example Hs Hs H H LC LC MV=z MV=qs q FC z
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 LC H 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 z
(valve opening) 1

12. Example (again): Evaporator with heating
qF [m3/s]
TF [K]
cF [mol/m3]
From
reactor
evaporation
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
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. Pressure control distillation
Need to stabilze p using Qc
But want Qc to be max
Use cascade with backoff on Qc (
Another similar example: reactor temperature control (stabilization) closed to Qmax.

19. 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)

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

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

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

23. Advantage: Very fast response (no delay)
Alternative 2 qB
Thot
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

24. Alternative 3: Use both inputs (with input resetting of dynamic input)qB
Thot
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)

25. 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

26. Divided wall column example

27. 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?

28. 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

29. 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”

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

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. CV DV MV MV CV CV Another column: Deethanizer Quality estimator
0 – 65% PC
65-100% CV
Flare
Propane
Fuel gas
to boilers
Heat ex 34 FC 28 LC
Reflux drum 23 FC 21 DV
Feed from stabilizators 20 FC FC 16
Product pumps 10 MV
Reflux pumps MV TC 1
Quality estimator
One example of using MPC at the column level.
What do we want to control? Product quality + avoid flaring
Bias update from analyzators PC CV LC CV
LP Steam LC
Quality estimator
LP Condensate
To Depropaniser

38. Top: Binary separation in this case Quality estimator vs
Top: Binary separation in this case Quality estimator vs. gas chromatograph (use logarithmic composition to reduce nonlinearity, CV = - ln ximpurity)
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. Optimization layer (RTO)
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
Symposium Chemical Process Control 6, Tucson, Arizona, 7-12 Jan. 2001, Preprints pp Published in AIChE Symposium Series, 98 (326), pp ISBN (2002).

45. European Symposium on Computer Aided Process Engineering 11, Kolding, Denmark, May 2001, Elsevier, pp

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

51. Step S7. Optimization layer (RTO)
Purpose: Identify active constraints and compute optimal setpoints (to be implemented by supervisory control layer)
Main structural issue: Do we need RTO? (or is process self-optimizing)
RTO not needed when
Can “easily” identify change in active constraints (operating region)
For each operating region there exists self-optimizing variables

52. Question
Why not combine RTO and control in a single layer with economic cost function (N-MPC = D-RTO)?
Why is this not used?
What alternatives are there?
RTO
CVs
MPC
PID
MVs
Process