Plantwide control: Towards a systematic procedure

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Plantwide control Towards a systematic procedure

• Sigurd Skogestad
• Department of Chemical Engineering
• Norwegian University of Science and Tecnology
  (NTNU)
• Trondheim, Norway
• PROST årsmøte 11. Juni 2002
• Based on Plenary Presentation at ESCAPE12,
• den Haag, May 2002

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Personal anecdote from Jack Ponton, University of
Edinburgh (1993)

• Some years ago, when a fairly junior academic,
  he took an industrial sabattical.
• Having told that he was a teacher of process
  control, he was presented with a process
  flowsheet and asked to put control loops on it.
• Despite having taught process control including
  differential eqautions, Laplace tranforms, Bode
  diagrams and so on he was at loss even as to
  start the task. And so most have been generations
  before of chemical engineering graduates. And
  this is the control task which process engineers
  in industry are most frequently called upon to
  perform.

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Idealized view of control(Ph.D. control)
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Practice I Tennessee Eastman challenge problem
(Downs, 1991)
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Practice II Typical PID diagram(PID control)
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Practice III Hierarchical structure
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• Alan Foss (Critique of chemical process control
  theory, AIChE Journal,1973)
• The central issue to be resolved ... is the
  determination of control system structure. Which
  variables should be measured, which inputs should
  be manipulated and which links should be made
  between the two sets?

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Plantwide control

• Not the tuning and behavior of each control loop,
  
• But rather the control philosophy of the overall
  plant with emphasis on the structural decisions
• Selection of manipulated variables (inputs)
• Selection of controlled variables (outputs)
• Selection of (extra) measurements (extra
  outputs)
• Selection of control configuration (structure of
  overall controller that interconnects the
  controlled, manipulated and measured variables)
• Selection of controller type (PID, decoupler, MPC
  etc.).
• That is All the decisions made before we get to
  Ph.D control

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Outline

• Plantwide control procedure
• Top-down definition of objectives
• What to control I Primary controlled variables
• Inventory control - where set production rate
• Bottom-up assignment of control loops
• What to control II Secondary controlled
  variables
• Decentralized versus multivariable control in
  supervisory layer

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Related work

• Page Buckley (1964) - Chapter on Overall process
  control (still industrial practice)
• Alan Foss (1973) - control system structure
• George Stephanopoulos and Manfred Morari (1980)
• Bill Luyben and coworkers (1975- ) many
  snowball effect
• Ruel Shinnar (1981- ) - dominant variables
• Jim Douglas and Alex Zheng (Umass) (1985- )
• Jim Downs (1991) - Tennessee Eastman process
• Larsson and Skogestad (2000) Review of plantwide
  control

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Stepwise procedure plantwide control
I. TOP-DOWN Step 1. DEFINE OVERALL CONTROL
OBJECTIVE Step 2. DEGREE OF FREEDOM
ANALYSIS Step 3. WHAT TO CONTROL? (primary
variables) Step 4. PRODUCTION
RATE Steady-state considerations No control
knowledge required!
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II. BOTTOM-UP (structure control system) Step
5. REGULATORY CONTROL LAYER
5.1 Stabilization (including level control)
5.2 Local disturbance rejection (inner
cascades) What more to control? (secondary
variables) Step 6. SUPERVISORY CONTROL LAYER
Decentralized or multivariable control
(MPC)? Pairing? Step 7. OPTIMIZATION LAYER
(RTO)
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Step 1. Overall control objective

• What are the operational objectives?
• Quantify Minimize scalar cost J
• Usually J economic cost /h
• Constraints on flows, equipment constraints,
  product specifications, etc.

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Step 2. Degree of freedom (DOF) analysis

• Nm no. of dynamic (control) DOFs (valves)
• Nss Nm- N0 steady-state DOFs
• N0 liquid levels with no steady-state effect
  (N0y) purely dynamic control DOFs (N0m)

Cost J depends normally only on steady-state DOFs
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Distillation column with given feed

Nm 5, N0y 2, Nss 5 - 2 3 (2 with
given pressure)
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Heat exchanger with bypasses
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Alternatives structures for optimizing control
Step 3 What should we control? (Control theory
has little to offer)
Control theory has a lot to offer
Hierarchical Centralized
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Step 3. What should we control? (primary
controlled variables)

• Intuition Dominant variables (Shinnar)
• Systematic Define cost J and minimize w.r.t.
  DOFs
• Control active constraints (constant setpoint is
  optimal)
• Remaining DOFs Control variables c for which
  constant setpoints give small (economic) loss
• Loss J - Jopt(d)
• when disturbances d occurs

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Loss with constant setpoints
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Application Recycle processJ V (minimize
energy)
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4
1
Given feedrate F0 and column pressure
2
3
Nm 5 N0y 2 Nss 5 - 2 3
Constraints Mr lt Mrmax, xB gt xBmin 0.98
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Recycle process Selection of controlled
variables

• Step 3.1 JV (minimize energy with given feed)
• Step 3.1 DOFs for optimization Nss 3
• Step 3.3 Most important disturbance Feedrate F0
• Step 3.4 Optimization Constraints on max. Mr and
  xB always active
• Step 3.5 1 DOF left, candidate controlled
  variables F, D, L, xD, ...
• Step 3.6 Loss with constant setpoints. Good xD,
  L/F. Poor F, D, L

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Recycle process Loss with constant setpoint, cs
Large loss with c F (Luyben rule)
Negligible loss with c L/F
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Recycle process Proposed control structurefor
case with J V (minimize energy)
Active constraint Mr Mrmax
Active constraint xB xBmin
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Self-optimizing control(Skogestad, 2000)
Loss L J - Jopt (d)
Self-optimizing control is achieved when a
constant setpoint policy results in an
acceptable loss L (without the need to reoptimize
when disturbances occur)
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Effect of implementation error on cost
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Recycle systems
Do not recommend Luybens rule of fixing a flow
in each recycle loop (even to avoid
snowballing)
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Good candidate controlled variables c (for
self-optimizing control)

• Requirements
• The optimal value of c should be insensitive to
  disturbances
• c should be easy to measure and control
• The value of c should be sensitive to changes in
  the steady-state degrees of freedom
• (Equivalently, J as a function of c should be
  flat)
• For cases with more than one unconstrained
  degrees of freedom, the selected controlled
  variables should be independent.

Singular value rule (Skogestad and Postlethwaite,
1996) Look for variables that maximize the
minimum singular value of the appropriately
scaled steady-state gain matrix G from u to c
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Step 4. Where set production rate?

• Very important!
• Determines structure of remaining inventory
  (level) control system
• Set production rate at (dynamic) bottleneck
• Link between Top-down and Bottom-up parts

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Production rate set at inlet Inventory control
in direction of flow
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Production rate set at outletInventory control
opposite flow
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Production rate set inside process
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Reactor-recycle processGiven feedrate with
production rate set at inlet
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Reactor-recycle processReconfiguration required
when reach bottleneck (max. vapor rate in column)
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Reactor-recycle processGiven feedrate with
production rate set at bottleneck (column)
F0s
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II. Bottom-up assignment of loops in control layer

• Identify secondary (extra) controlled variable
• Determine structure (configuration) of control
  system (pairing)
• A good control configuration is insensitive to
  parameter changes!
• Industry most common approach is to copy old
  designs

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Step 5. Regulatory control layer

• Purpose Stabilize the plant using local SISO
  PID controllers to enable manual operation (by
  operators)
• Main structural issues
• What more should we control? (secondary cvs, y2)
• Pairing with manipulated variables (mvs)

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Selection of secondary controlled variables (y2)

• The variable is easy to measure and control
• For stabilization Unstable mode is quickly
  detected in the measurement (Tool pole vector
  analysis)
• For local disturbance rejection The variable is
  located close to an important disturbance
  (Tool partial control analysis).

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Partial control
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Step 6. Supervisory control layer

• Purpose Keep primary controlled outputs cy1 at
  optimal setpoints cs
• Degrees of freedom Setpoints y2s in reg.control
  layer
• Main structural issue Decentralized or
  multivariable?

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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

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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

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Step 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 self-optimizing)

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Conclusion

• Procedure plantwide control
• I. Top-down analysis to identify degrees of
  freedom and primary controlled variables (look
  for self-optimizing variables)
• II. Bottom-up analysis to determine secondary
  controlled variables and structure of control
  system (pairing).

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More details....

• Skogestad, S. (2000), Plantwide control -towards
  a systematic procedure, Proc. ESCAPE12
  Symposium, Haag, Netherlands, May 2002.
• Larsson, T., 2000. Studies on plantwide control,
  Ph.D. Thesis, Norwegian University of Science and
  Technology, Trondheim.
• Larsson, T. and S. Skogestad, 2000, Plantwide
  control A review and a new design procedure,
  Modeling, Identification and Control, 21,
  209-240.
• Larsson, T., K. Hestetun, E. Hovland and S.
  Skogestad, 2001, Self-optimizing control of a
  large-scale plant The Tennessee Eastman
  process, Ind.Eng.Chem.Res., 40, 4889-4901.
• Larsson, T., M.S. Govatsmark, S. Skogestad and
  C.C. Yu, 2002, Control of reactor, separator and
  recycle process, Submitted to Ind.Eng.Chem.Res.
• Skogestad, S. (2000). Plantwide control The
  search for the self-optimizing control
  structure. J. Proc. Control 10, 487-507.

See also the home page of Sigurd
Skogestad http//www.chembio.ntnu.no/users/skoge/