Practical plantwide process control Sigurd Skogestad NTNU Thailand

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

Part 3 (1 h). Advanced control layer • 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? 2

Control layers CV 1 s “Advanced “control CV 2 s PID 3 u (valves)

Outline • Skogestad procedure for control structure design I Top Down • Step S 1: Define operational objective (cost) and constraints • Step S 2: Identify degrees of freedom and optimize operation for disturbances • Step S 3: Implementation of optimal operation – What to control ? (primary CV’s) (self-optimizing control) • Step S 4: Where set the production rate? (Inventory control) II Bottom Up • Step S 5: Regulatory control: What more to control (secondary CV’s) ? – Distillation example • Step S 6: Supervisory control • Step S 7: Real-time optimization 4

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 5

Most important control structures 1. 2. 3. 6 Feedback control Ratio control (special case of feedforward) Cascade control

Ratio control (most common case of feedforward) 7

Usually: Combine ratio (feedforward) with feedback • Adjust (q 1/q 2)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 q. H/q. F (and use feedback from T to fine tune ratio) 8

Example 9

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 flow out 10 flow out

What are the benefits of adding a flow controller (inner cascade)? Extra measurement y’ = q 11

Example (again): Evaporator with heating evaporation 12

Split Range Temperature Control 13

Split Range Temperature Control 14

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. 15

Use of extra measurements: Cascade control (conventional) The reference r 2 (= setpoint ys 2) is an output from another controller General case (“parallel cascade”) Special common case (“series cascade”) 16

Series cascade 1. 2. 3. Disturbances arising within the secondary loop (before y 2) are corrected by the secondary controller before they can influence the primary variable y 1 Phase lag existing in the secondary part of the process (G 2) is reduced by the secondary loop. This improves the speed of response of the primary loop. Gain variations in G 2 are overcome within its own loop. Thus, use cascade control (with an extra secondary measurement y 2) when: • The disturbance d 2 is significant and G 1 has an effective delay • The plant G 2 is uncertain (varies) or nonlinear 17 Design / tuning (see also in tuning-part): • First design K 2 (“fast loop”) to deal with d 2 • Then design K 1 to deal with d 1 Example: Flow cascade for level control u = z, y 2=F, y 1=M, K 1= LC, K 2= FC

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 Also known as: Midranging control, valve position control 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) 18

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

QUIZ: Heat exchanger with bypass 20

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IRV = ideal resting value 23

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>20 C – 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 24

Control of primary variables • Purpose: Keep primary controlled outputs c=y 1 at optimal setpoints cs • Degrees of freedom: Setpoints y 2 s in reg. control layer • Main structural issue: Decentralized or multivariable? 25

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 26

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 - 27 Requires multivariable dynamic model Tuning may be difficult Less transparent “Everything goes down at the same time”

”Summary Advanced control” STEP S 6. SUPERVISORY LAYER Objectives of supervisory layer: 1. Switch control structures (CV 1) depending on operating region – Active constraints – self-optimizing variables 2. Perform “advanced” economic/coordination control tasks. – Control primary variables CV 1 at setpoint using as degrees of freedom (MV): • Setpoints to the regulatory layer (CV 2 s) • ”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 28 Implementation: • Alternative 1: Advanced control based on ”simple elements” (decentralized control) • Alternative 2: MPC

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 1. Use of extra measurements for improved control; : Cascade control – Cascade control is when MV (for master) =setpoint to slave controller – MV 1 = CV 2 s 2. Switch between active constraints: Selectors 3. Make use of extra inputs – Dynamic (improve performance): Input resetting = valve position control = midranging control – Steady state (extend operating range): Split range control 4. Reduce interactions when usingle-loop control: Decouplers (including phsically based) 29

Model predictive control (MPC) = “online optimal control” Discretize in time: 30

Implementation MPC project (Stig Strand, Statoil) 31 31

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CV 1=TOP COMPOSITION CV 2=BOTTOM COMPOSITION 35 35

CV 2=BOTTOM COMPOSITION 36 36

Quality estimator 37

Top: Binary separation in this case Quality estimator vs. gas chromatograph (use logarithmic composition to reduce nonlinearity, CV = - ln ximpurity) 38

The final test: MPC in closed-loop 39

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

Outline • Skogestad procedure for control structure design I Top Down • Step S 1: Define operational objective (cost) and constraints • Step S 2: Identify degrees of freedom and optimize operation for disturbances • Step S 3: Implementation of optimal operation – What to control ? (primary CV’s) (self-optimizing control) • Step S 4: Where set the production rate? (Inventory control) II Bottom Up • Step S 5: Regulatory control: What more to control (secondary CV’s) ? • Step S 6: Supervisory control • Step S 7: Real-time optimization 41

Optimization layer (RTO) • Purpose: Identify active constraints and compute optimal setpoints (to be implemented by control layer) MVs 42 Process

An RTO sucess story: Statoil Mongstad Crude oil preheat train 43

Symposium Chemical Process Control 6, Tucson, Arizona, 7 -12 Jan. 2001, Preprints pp. 476 -480. Published in AICh. E Symposium Series, 98 (326), pp. 403407. ISBN 0 -8169 -0869 -9 (2002). 44

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

Data reconcilation 46

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

Improvements 48

An RTO failure: Complete Statoil Kårstø gas processing plant 49

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

Step S 7. 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 selfoptimizing) • RTO not needed when – Can “easily” identify change in active constraints (operating region) – For each operating region there exists self-optimizing variables 51

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? MVs 52 Process