Plantwide%20control:%20Towards%20a%20systematic%20procedure

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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
• March 2002

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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? There is more than a
  suspicion that the work of a genius is needed
  here, for without it the control configuration
  problem will likely remain in a primitive, hazily
  stated and wholly unmanageable form. The gap is
  present indeed, but contrary to the views of
  many, it is the theoretician who must close it.
• Carl Nett (1989)
• Minimize control system complexity subject to the
  achievement of accuracy specifications in the
  face of uncertainty.

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Outline

• Introduction
• Plantwide control procedure
• Top-down
• Bottom-up
• What to control I Primary controlled variables
• Inventory control - where set production rate
• What to control II Secondary controlled
  variables
• Decentralized versus multivariable control

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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 (1975- ) - 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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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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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 controlled variables (outputs)
• Selection of manipulated variables (inputs)
• Selection of (extra) measurements
• 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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Stepwise procedure plantwide control
I. TOP-DOWN Step 1. MANIPULATED VARIABLES
Step 2. DEGREE OF FREEDOM ANALYSIS Step 3.
WHAT TO CONTROL? (primary variables) Step 4.
PRODUCTION RATE
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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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I. Top-down

• Define operational objectives
• Identify degrees of freedom
• Identify primary controlled variables (look for
  self-optimizing variables)
• Determine where to set the production rate

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Step 1. Manipulated variables

• Usually given by design
• Check that there are enough manipulated variables
  (DOFs) - both dynamically and at steady-state
  (step 2)
• Otherwise Need to add equipment
• extra heat exchanger
• bypass
• surge tank
• .

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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-integrated distillation process
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Heat exchanger with bypasses
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Alternatives structures for optimizing control
Step 3 What should we control?
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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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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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Tennessee Eastman plant
Conclusion Do not use purge rate as controlled
variable
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Example sharp optimum. High-purity distillation
c Temperature top of column
Water (L) - acetic acid (H) Max 100 ppm acetic
acid 100 water 100 C 99.99 water
100.01C
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Procedure for selecting (primary) controlled
variables (Skogestad, 2000)

• Step 3.1 Determine DOFs for optimization
• Step 3.2 Definition of optimal operation J (cost
  and constraints)
• Step 3.3 Identification of important disturbances
• Step 3.4 Optimization (nominally and with
  disturbances)
• Step 3.5 Identification of candidate controlled
  variables
• Step 3.6 Evaluation of loss with constant
  setpoints for alternative controlled variables
• Step 3.7 Evaluation and selection (including
  controllability analysis)
• Case studies Tenneessee-Eastman,
  Propane-propylene splitter, recycle process,
  heat-integrated distillation

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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
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Recycle process Selection of controlled
variables

• Step 2.1 DOFs for optimization Nss 3
• Step 2.2 JV (minimize energy with given feed)
• Step 2.3 Most important disturbance Feedrate F0
• Step 2.4 Optimization Constraints on Mr and xB
  always active
• (so Luybens structure is not optimal)
• Step 2.5 1 DOF left, candidate controlled
  variables F, D, L, xD, ...
• Step 2.6 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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Snowball effect Forget Luybens rule about
fixing a flow in each recycle loop
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Recycle process Proposed control structureJ V
(minimize energy)
Active constraint Mr Mrmax
Active constraint xB xBmin
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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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Definition of bottleneck
A unit (or more precisely, an extensive variable
E within this unit) is a bottleneck (with
respect to the flow F) if - With the flow F as
a degree of freedom, the variable E is optimally
at its maximum constraint (i.e., E Emax at the
optimum) - The flow F is increased by
increasing this constraint (i.e., dF/dEmax gt 0
at the optimum). A variable E is a dynamic(
control) bottleneck if in addition - The
optimal value of E is unconstrained when F is
fixed at a sufficiently low value Otherwise E
is a steady-state (design) bottleneck.
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Heat integrated distillation processGiven
feedrate with production rate set at inlet
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Heat integrated distillation processReconfigurat
ion required when reach bottleneck (max. cooling
in column 2)
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Heat integrated distillation processGiven
feedrate with production rate adjusted at
bottleneck (column 2)
SET
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Recycle process Given feedrate
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Bottleneck in column
MAX
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II. Bottom-up

• Determine secondary controlled variables and
  structure (configuration) of control system
  (pairing)
• A good control configuration is insensitive to
  parameter changes

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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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References

• Skogestad, S. (2000), Plantwide control -towards
  a systematic procedure, Proc. ESCAPE12
  Symposium, Haag, 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 S. Skogestad http//www
.chembio.ntnu.no/users/skoge/