1

Outline

• Skogestad procedure for control structure designI 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

2

”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”• Alternative 2: MPC

3

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

• Selectors

• Split-range control

4

• Cascade control arises when the output from one controller is the input to another. This is broader than the conventional definition of cascade control which is that the output from one controller is the reference command (setpoint) to another. In addition, in cascade control, it is usually assumed that the inner loop K2 is much faster than the outer loop K1.

• Feedforward elements link measured disturbances to manipulated inputs.

• Decoupling elements link one set of manipulated inputs (“measurements”) with another set of manipulated inputs. They are used to improve the performance of decentralized control systems, and are often viewed as feedforward elements (although this is not correct when we view the control system as a whole) where the “measured disturbance” is the manipulated input computed by another decentralized controller.

5

Use of extra inputs

Two different cases

1. Have extra dynamic inputs (degrees of freedom)Cascade implementation: “Input resetting to ideal resting value”

Example: Heat exchanger with extra bypass

2. 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)

6

Extra inputs, dynamically

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

7

QUIZ: Heat exchanger with bypass

CW

Nvalves = 3, N0valves = 2 (of 3), Nss = 3 – 2 = 1 •Want tight control of Thot

•Primary input: CW•Secondary input: qB

•Proposed control structure?

qB

Thot

closed

8

CW

Nvalves = 3, N0valves = 2 (of 3), Nss = 3 – 2 = 1

qB

ThotTC

Use primary input CW: TOO SLOW

Alternative 1

closed

9

CW

Nvalves = 3, N0valves = 2 (of 3), Nss = 3 – 2 = 1

qB

Thot

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

Alternative 2

closed

10

CW

Nvalves = 3, N0valves = 2 (of 3), Nss = 3 – 2 = 1

qB

Thot

TC

Alternative 3: Use both inputs (with input resetting of dynamic input)

closed

FCqBs

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

11

Exercise

• Exercise: (a) In what order would you tune the controllers?

(b) Give a practical example of a process that fits into this block diagram

12

Too few inputs

• Must decide which output (CV) has the highest priority– Selectors

13

Use of extra measurements: Cascade control (conventional)

The reference r2 (= setpoint ys2) is an output from another controller

General case (“parallel cascade”)

Special common case (“series cascade”)

Not always helpful…y2 must be closely related to y1

14

Series cascade

1. Disturbances arising within the secondary loop (before y2) are corrected by the secondary controller before they can influence the primary variable y1

2. 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.

3. 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 controlu = z, y2=F, y1=M, K1= LC, K2= FC

15

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?

16

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

17

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”

18

Outline

• Skogestad procedure for control structure designI 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

19

Optimization layer (RTO)

• Purpose: Identify active constraints and compute optimal setpoints (to be implemented by control layer)

CVs

Process

MVs

RTO

MPC

PID

Sigurd Skogestad

20

An RTO sucess story: Statoil Mongstad Crude oil preheat train

20 heat exchangers, 5 DOFs (splits), 85 flow andf temperature measurments

Max T

21

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).

22

European Symposium on Computer Aided Process Engineering 11,

Kolding, Denmark, 27-30 May 2001, Elsevier, pp. 1041-1046.

23

Data reconcilation

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

24

Optimization: 2% energy reduction

In service for 15 years

25

20 heat exchangers, 5 DOFs (splits), 85 flow andf temperature measurments

Max T

Improvements

26

An RTO failure: Complete Statoil Kårstø gas processing plant

Plan: 20 + distillation columns, 4 parallel trains, steam system,...

27

Alternative to Real-Time Opimization: Indirect optimization using control layer

Use off-line optimization to identify controlled variables (CV):

- Active constraints- Self-optimizing variables

CVs

Process

MVs

RTO

MPC

PID

28

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

29

• Reserach issue (

30

Question

• From: Ruben Marti, Un. Valledolid, Spain

• 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?

CVs

Process

MVs

RTO

MPC

PID