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A SYSTEMATIC APPROACH TO PLANTWIDE CONTROL (1985-2010-2025)

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Title: A SYSTEMATIC APPROACH TO PLANTWIDE CONTROL (1985-2010-2025)


1
A SYSTEMATIC APPROACH TO PLANTWIDE CONTROL
(1985-2010-2025)
  • Sigurd Skogestad
  • Department of Chemical Engineering
  • Norwegian University of Science and Tecnology
    (NTNU)
  • Trondheim, Norway
  • DTU
  • March 2011



2
Title A systematic approach to plantwide control.
  • Abstract A chemical plant may have thousands of
    measurements and control loops. By the term
    plantwide control it is not meant the tuning and
    behavior of each of these loops, but rather the
    control philosophy of the overall plant with
    emphasis on the structural decisions. In
    practice, the control system is usually divided
    into several layers, separated by time scale
    scheduling (weeks) , site-wide optimization
    (day), local optimization (hour), supervisory
    (predictive, advanced) control (minutes) and
    regulatory control (seconds). Such a hiearchical
    (cascade) decomposition with layers operating on
    different time scale is used in the control of
    all real (complex) systems including  biological
    systems and airplanes, so the issues in this
    section are not limited to process control. In
    the talk the most important issues are discussed,
    especially related to the choice of variables
    that provide the link the control layers.

3
A SYSTEMATIC APPROACH TO PLANTWIDE
CONTROL (1985-2010-2025)
  • Sigurd Skogestad
  • Department of Chemical Engineering
  • Norwegian University of Science and Tecnology
    (NTNU)
  • Trondheim, Norway
  • PROST
  • Feb. 2011



4
Dealing with complexity
Plantwide control
The controlled variables (CVs) interconnect the
layers
OBJECTIVE
Min J (economics)
RTO
cs y1s
Follow path ( look after other variables)
MPC
y2s
Stabilize avoid drift
PID
u (valves)


5
How we design a control system for a complete
chemical plant?
  • Where do we start?
  • What should we control? and why?
  • etc.
  • etc.

6
  • 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.
  • Previous work on plantwide control
  • Page Buckley (1964) - Chapter on Overall process
    control (still industrial practice)
  • Greg Shinskey (1967) process control systems
  • Alan Foss (1973) - control system structure
  • Bill Luyben et al. (1975- ) case studies
    snowball effect
  • George Stephanopoulos and Manfred Morari (1980)
    synthesis of control structures for chemical
    processes
  • Ruel Shinnar (1981- ) - dominant variables
  • Jim Downs (1991) - Tennessee Eastman challenge
    problem
  • Larsson and Skogestad (2000) Review of plantwide
    control

7
Control structure design procedure
  • I Top Down
  • Step 1 Define operational objectives (optimal
    operation)
  • Cost function J (to be minimized)
  • Operational constraints
  • Step 2 Identify degrees of freedom (MVs) and
    optimize for
  • expected disturbances
  • Step 3 Select primary controlled variables cy1
    (CVs)
  • Step 4 Where set the production rate? (Inventory
    control)
  • II Bottom Up
  • Step 5 Regulatory / stabilizing control (PID
    layer)
  • What more to control (y2 local CVs)?
  • Pairing of inputs and outputs
  • Step 6 Supervisory control (MPC layer)
  • Step 7 Real-time optimization (Do we need it?)

y1
y2
MVs
Process
8
Step 1. Define optimal operation (economics)
  • What are we going to use our degrees of freedom u
    (MVs) for?
  • Define scalar cost function J(u,x,d)
  • u degrees of freedom (usually steady-state)
  • d disturbances
  • x states (internal variables)
  • Typical cost function
  • Optimize operation with respect to u for given d
    (usually steady-state)
  • minu J(u,x,d)
  • subject to
  • Model equations f(u,x,d) 0
  • Operational constraints g(u,x,d) lt 0

J cost feed cost energy value products
9
Step 2 Identify degrees of freedom and optimize
for expected disturbances
  • Optimization Identify regions of active
    constraints
  • Time consuming!

31
Control 2 active constraints (xA, xB) 2
selfoptimizing
Example (Magnus G. Jacobsen) Two distillation
columns in series. 4 degrees of freedom
5
31
40
13
10
Step 3 Implementation of optimal operation
  • Optimal operation for given d
  • minu J(u,x,d)
  • subject to
  • Model equations f(u,x,d) 0
  • Operational constraints g(u,x,d) lt 0

? uopt(d)
Problem Usally cannot keep uopt constant because
disturbances d change
How should we adjust the degrees of freedom (u)?
11
Implementation (in practice) Local feedback
control!
y
Self-optimizing control Constant setpoints for
c gives acceptable loss
d
Feedforward
Optimizing control
Local feedback Control c (CV)
12
Question What should we control (c)? (primary
controlled variables y1c)
  • Introductory example Runner

13
Optimal operation of runner
Optimal operation - Runner
  • Cost to be minimized, JT
  • One degree of freedom (upower)
  • What should we control?

14
Sprinter (100m)
Optimal operation - Runner
  • 1. Optimal operation of Sprinter, JT
  • Active constraint control
  • Maximum speed (no thinking required)

15
Marathon (40 km)
Optimal operation - Runner
  • 2. Optimal operation of Marathon runner, JT
  • Unconstrained optimum!
  • Any self-optimizing variable c (to control at
    constant setpoint)?
  • c1 distance to leader of race
  • c2 speed
  • c3 heart rate
  • c4 level of lactate in muscles

16
Conclusion Marathon runner
Optimal operation - Runner
select one measurement
c heart rate
  • Simple and robust implementation
  • Disturbances are indirectly handled by keeping a
    constant heart rate
  • May have infrequent adjustment of setpoint
    (heart rate)

17
Step 3. What should we control (c)? (primary
controlled variables y1c)
  • Selection of controlled variables c
  • Control active constraints!
  • Unconstrained variables Control self-optimizing
    variables!

18
Example active constraint Optimal operation
max. throughput (active constraint)
Want tight bottleneck control to reduce backoff!
Rule for control of hard output constraints
Squeeze and shift! Reduce variance
(Squeeze) and shift setpoint cs to reduce
backoff
19
Control self-optimizing variables
Unconstrained degrees of freedom
  • 1. Old idea (Morari et al., 1980)
  • We want to find a function c of the process
    variables which when held constant, leads
    automatically to the optimal adjustments of the
    manipulated variables, and with it, the optimal
    operating conditions.
  • 2. The ideal self-optimizing variable c is the
    gradient (c ? J/? u Ju)
  • Keep gradient at zero for all disturbances (c
    Ju0)
  • Problem no measurement of gradient

Ju
Ju0
20
H
Ideal c Ju In practise c H y
21
Optimal operation
Unconstrained optimum
Cost J
Jopt
copt
Controlled variable c
22
Optimal operation
Unconstrained optimum
Cost J
d
Jopt
n
copt
Controlled variable c
  • Two problems
  • 1. Optimum moves because of disturbances d
    copt(d)
  • 2. Implementation error, c copt n

23
H
24
Guidelines for selecting measurements as CVs
  • Rule 1 Optimal value for CV (cHy) is
    insensitive to disturbances d (minimizes effect
    of moving optimum)
  • dcopt/dd is small
  • Rule 2 c should be easy to measure and control
    (small implementation error n)
  • Rule 3 Maximum gain rule c should be
    sensitive to input changes (large gain G from u
    to c) or equivalently the optimum Jopt should be
    flat with respect to c (minimizes effect of
    implementation error n)
  • G dc/du is large

Reference S. Skogestad, Plantwide control The
search for the self-optimizing control
structure, Journal of Process Control, 10,
487-507 (2000).
25
Optimal measurement combination
  • Candidate measurements (y) Include also inputs u


H
26
Nullspace method
No measurement noise
27
With measurement noise
Optimal measurement combination, c Hy
0 in nullspace method (no noise)
Minimize in Maximum gain rule ( maximize S1 G
Juu-1/2 , GHGy )
Scaling S1
28
Example CO2 refrigeration cycle
pH
J Ws (work supplied) DOF u (valve opening,
z) Main disturbances d1 TH d2 TCs
(setpoint) d3 UAloss What should we
control?
29
CO2 refrigeration cycle
  • Step 1. One (remaining) degree of freedom (uz)
  • Step 2. Objective function. J Ws (compressor
    work)
  • Step 3. Optimize operation for disturbances
    (d1TC, d2TH, d3UA)
  • Optimum always unconstrained
  • Step 4. Implementation of optimal operation
  • No good single measurements (all give large
    losses)
  • ph, Th, z,
  • Nullspace method Need to combine nund134
    measurements to have zero disturbance loss
  • Simpler Try combining two measurements. Exact
    local method
  • c h1 ph h2 Th ph k Th k -8.53 bar/K
  • Nonlinear evaluation of loss OK!

30
CO2 cycle Maximum gain rule
31
Refrigeration cycle Proposed control structure
Control c temperature-corrected high pressure
32
Step 4. Where set production rate?
  • Where locale the TPM (throughput manipulator)?
  • Very important!
  • Determines structure of remaining inventory
    (level) control system
  • Set production rate at (dynamic) bottleneck
  • Link between Top-down and Bottom-up parts

33
Production rate set at inlet Inventory control
in direction of flow
TPM
Required to get local-consistent inventory
control
34
Production rate set at outlet Inventory control
opposite flow
TPM
35
Production rate set inside process
TPM
36
LOCATE TPM?
  • Default choice place the TPM at the feed
  • Consider moving if there is an important active
    constraint that could otherwise not be well
    controlled.

37
Degrees of freedom for optimization (usually
steady-state DOFs), MVopt CV1s Degrees of
freedom for supervisory control, MV1CV2s
unused valves Physical degrees of freedom for
stabilizing control, MV2 valves (dynamic
process inputs)
38
Step 5 Regulatory control layer
  • Step 5. Choose structure of regulatory
    (stabilizing) layer
  • (a) Identify stabilizing CV2s (levels,
    pressures, reactor temperature,one temperature in
    each column, etc.).
  • In addition, active constraints (CV1) that
    require tight control (small backoff) may be
    assigned to the regulatory layer.
  • (Comment usually not necessary with tight
    control of unconstrained CVs because optimum is
    usually relatively flat)
  • (b) Identify pairings (MVs to be used to control
    CV2), taking into account
  • Want local consistency for the inventory
    control
  • Want tight control of important active
    constraints
  • Avoid MVs that may saturate in the regulatory
    layer, because this would require either
  • reassigning the regulatory loop (complication
    penalty), or
  • requiring back-off for the MV variable (economic
    penalty)
  • Preferably, the same regulatory layer should be
    used for all operating regions without the need
    for reassigning inputs or outputs.

39
Example Distillation
  • Primary controlled variable y1 c xD, xB
    (compositions top, bottom)
  • BUT Delay in measurement of x unreliable
  • Regulatory control For stabilization need
    control of (y2)
  • Liquid level condenser (MD)
  • Liquid level reboiler (MB)
  • Pressure (p)
  • Holdup of light component in column
  • (temperature profile)

Unstable (Integrating) No steady-state effect
Variations in p disturb other loops
Almost unstable (integrating)
Ts
TC
T-loop in bottom
40
Why simplified configurations? Why control
layers? Why not one big multivariable
controller?
  • Fundamental Save on modelling effort
  • Other
  • easy to understand
  • easy to tune and retune
  • insensitive to model uncertainty
  • possible to design for failure tolerance
  • fewer links
  • reduced computation load

41
Advanced control STEP 6. 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

42
Summary. Systematic procedure for plantwide
control
  • Start top-down with economics
  • Step 1 Define operational objectives and
    identify degrees of freeedom
  • Step 2 Optimize steady-state operation.
  • Step 3A Identify active constraints primary
    CVs c. Should controlled to maximize profit)
  • Step 3B For remaining unconstrained degrees of
    freedom Select CVs c based on self-optimizing
    control.
  • Step 4 Where to set the throughput (usually
    feed)
  • Regulatory control I Decide on how to move mass
    through the plant
  • Step 5A Propose local-consistent inventory
    (level) control structure.
  • Regulatory control II Bottom-up stabilization
    of the plant
  • Step 5B Control variables to stop drift
    (sensitive temperatures, pressures, ....)
  • Pair variables to avoid interaction and
    saturation
  • Finally make link between top-down and bottom
    up.
  • Step 6 Advanced control system (MPC)
  • CVs Active constraints and self-optimizing
    economic variables
  • look after variables in layer below (e.g.,
    avoid saturation)
  • MVs Setpoints to regulatory control layer.
  • Coordinates within units and possibly between
    units

cs
43
Summary and references
  • The following paper summarizes the procedure
  • S. Skogestad, Control structure design for
    complete chemical plants'', Computers and
    Chemical Engineering, 28 (1-2), 219-234 (2004).
  • There are many approaches to plantwide control as
    discussed in the following review paper
  • T. Larsson and S. Skogestad, Plantwide control
    A review and a new design procedure'' Modeling,
    Identification and Control, 21, 209-240 (2000).

44
  • S. Skogestad Plantwide control the search for
    the self-optimizing control structure'', J. Proc.
    Control, 10, 487-507 (2000).
  • S. Skogestad, Self-optimizing control the
    missing link between steady-state optimization
    and control'', Comp.Chem.Engng., 24, 569-575
    (2000).
  • I.J. Halvorsen, M. Serra and S. Skogestad,
    Evaluation of self-optimising control
    structures for an integrated Petlyuk distillation
    column'', Hung. J. of Ind.Chem., 28, 11-15
    (2000).
  • T. Larsson, K. Hestetun, E. Hovland, and S.
    Skogestad, Self-Optimizing Control of a
    Large-Scale Plant The Tennessee Eastman
    Process'', Ind. Eng. Chem. Res., 40 (22),
    4889-4901 (2001).
  • K.L. Wu, C.C. Yu, W.L. Luyben and S. Skogestad,
    Reactor/separator processes with recycles-2.
    Design for composition control'', Comp. Chem.
    Engng., 27 (3), 401-421 (2003).
  • T. Larsson, M.S. Govatsmark, S. Skogestad, and
    C.C. Yu, Control structure selection for
    reactor, separator and recycle processes'', Ind.
    Eng. Chem. Res., 42 (6), 1225-1234 (2003).
  • A. Faanes and S. Skogestad, Buffer Tank Design
    for Acceptable Control Performance'', Ind. Eng.
    Chem. Res., 42 (10), 2198-2208 (2003).
  • I.J. Halvorsen, S. Skogestad, J.C. Morud and V.
    Alstad, Optimal selection of controlled
    variables'', Ind. Eng. Chem. Res., 42 (14),
    3273-3284 (2003).
  • A. Faanes and S. Skogestad, pH-neutralization
    integrated process and control design'',
    Computers and Chemical Engineering, 28 (8),
    1475-1487 (2004).
  • S. Skogestad, Near-optimal operation by
    self-optimizing control From process control to
    marathon running and business systems'',
    Computers and Chemical Engineering, 29 (1),
    127-137 (2004).
  • E.S. Hori, S. Skogestad and V. Alstad, Perfect
    steady-state indirect control'',
    Ind.Eng.Chem.Res, 44 (4), 863-867 (2005).
  • M.S. Govatsmark and S. Skogestad, Selection of
    controlled variables and robust setpoints'',
    Ind.Eng.Chem.Res, 44 (7), 2207-2217 (2005).
  • V. Alstad and S. Skogestad, Null Space Method
    for Selecting Optimal Measurement Combinations as
    Controlled Variables'', Ind.Eng.Chem.Res, 46 (3),
    846-853 (2007).
  • S. Skogestad, The dos and don'ts of
    distillation columns control'', Chemical
    Engineering Research and Design (Trans IChemE,
    Part A), 85 (A1), 13-23 (2007).
  • E.S. Hori and S. Skogestad, Selection of
    control structure and temperature location for
    two-product distillation columns'', Chemical
    Engineering Research and Design (Trans IChemE,
    Part A), 85 (A3), 293-306 (2007).
  • A.C.B. Araujo, M. Govatsmark and S. Skogestad,
    Application of plantwide control to the HDA
    process. I Steady-state and self-optimizing
    control'', Control Engineering Practice, 15,
    1222-1237 (2007).
  • A.C.B. Araujo, E.S. Hori and S. Skogestad,
    Application of plantwide control to the HDA
    process. Part II Regulatory control'',
    Ind.Eng.Chem.Res, 46 (15), 5159-5174 (2007).
  • V. Kariwala, S. Skogestad and J.F. Forbes,
    Reply to Further Theoretical results on
    Relative Gain Array for Norn-Bounded Uncertain
    systems'''' Ind.Eng.Chem.Res, 46 (24), 8290
    (2007).
  • V. Lersbamrungsuk, T. Srinophakun, S. Narasimhan
    and S. Skogestad, Control structure design for
    optimal operation of heat exchanger networks'',
    AIChE J., 54 (1), 150-162 (2008). DOI
    10.1002/aic.11366
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