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

”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

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

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.

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 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)

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

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

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

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

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

Exercise Exercise: In what order would you tune the controllers? Give a practical example of a process that fits into this block diagram

Too few inputs Must decide which output (CV) has the highest priority Selectors

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”)

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

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?

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

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”

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

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

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

Symposium Chemical Process Control 6, Tucson, Arizona, 7-12 Jan 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).

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

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

Optimization: 2% energy reduction In service for 15 years

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

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

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

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

Reserach issue (

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? RTO CVs MPC PID MVs Process