Control Structure Design: New Developments and Future Directions

1
Control Structure Design New Developments and
Future Directions

• Vinay Kariwala and Sigurd Skogestad
• Department of Chemical Engineering
• NTNU, Trondheim, Norway

skoge_at_chemeng.ntnu.no
2

• Control Structure Design
• Challenges
• Tools (partial solutions)
• Case Studies
• Future Directions

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Control Systems
Truly Optimal
Optimizing Controller
d
u
ym
Process
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Control Structure Design Structural Decisions
Truly Optimal
Ph.D. Control
PID Control
Optimizing Controller
Economic Optimizer
Local Optimizer
d
zset
d
Process
Process
Regulatory Controller
zset
Controller
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Previous Work

• Buckley (1964)
• Umeda et al. (1978)
• Fisher et al. (1988)
• Price, Georgakis et al. (1993)
• McAvoy and Ye (1994)
• Luyben et al. (1997)
• Ng and Stephanopolous (1998)

We want a generic approach that is mathematically
well-formulated and extends beyond process
control
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• Control Structure Design
• Challenges
• Tools (partial solutions)
• Case Studies
• Future Directions

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Challenges

• Q1. What should be controlled?
• Choice depends on operational objectives -
• usually steady-state economics
• Often the most important decision

Local Optimizer
zset
Supervisory Controller
y2,set
Regulatory Controller
u
ym
Process
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Challenges
Q1. What should be controlled?
Local Optimizer
zset

• Q2. What variables should be used for
• regulatory control?
• Hundreds of measurements
• Lack of precise mathematical formulation

Supervisory Controller
y2,set
Regulatory Controller
u
ym
Process
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Challenges
Q1. What should be controlled?
Local Optimizer
zset
Q2. What variables should be used for regulatory
control?
Supervisory Controller
y2,set

• Q3. To decentralize or not? If yes, how?
• Pairing selection or process decomposition
• Usually an issue for supervisory layer

Regulatory Controller
u
ym
Process
10

• Control Structure Design
• Challenges
• Tools (partial solutions)
• Case Studies
• Future Directions

11
Q1. What should be controlled?
Local Optimizer

• Self-optimizing control
• Control active constraints
• Unconstrained Control variables that give
• acceptable loss when held constant

zset
Supervisory Controller
y2,set
Regulatory Controller
u
ym
Process
12
Q1. What should be controlled?
Local Optimizer

• Self-optimizing control
• Control active constraints
• Unconstrained Control variables that give
• acceptable loss when held constant

zset
Supervisory Controller
y2,set
Regulatory Controller
u
ym
Process
13
Q1. What should be controlled?
Local Optimizer

• Self-optimizing control
• Control active constraints
• Unconstrained Control variables that give
• acceptable loss when held constant

zset
Supervisory Controller
y2,set
Regulatory Controller
u
ym
Process
14
Q1. What should be controlled?
Local Optimizer

• Self-optimizing control
• Control active constraints
• Unconstrained Control variables that give
• acceptable loss when held constant

zset
Supervisory Controller
y2,set

• Brute force evaluation
• Locally optimal methods
• Maximum gain rule max
• Combination of measurements

Regulatory Controller
u
ym
Process
Skogestad. J. Proc. Control, 2000 Halvorsen,
Morud, Skogestad and Alstad. Ind. Eng. Chem. Res.
2003 Ph.D. Thesis of M. Govatsmark and V. Alstad,
NTNU, Norway
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Regulatory layer
Local Optimizer
zset
Supervisory Controller
y2,set
Prevent the runner from falling (Separation of
tasks)
Regulatory Controller
u
ym

• Objectives regulatory control
• Stabilization
• Disturbance rejection

Process
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Q2a. Variables for stabilization?
Choose variables that minimize input usage

• Reduced likelihood of input saturation
• Least disturbing effect on stabilized system

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Q2b. Variables for Disturbance Rejection
Local

r
Disturbance
-
Rejection

n
u
-

2
y2
Stabilized
d
System
z
u
1
With y2 controlled
Choose variables to reduce disturbance sensitivity
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Sequential Approach
Primary CVs Economically self-optimizing
z
System
1.
u
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• Control Structure Design
• Challenges
• Tools (partial solutions)
• Case Studies
• Future Directions

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Applications

• Traditional
• Chemical plants
• Aerospace and mechanical systems
• Emerging
• Fuel cells
• Bioreactors
• Systems biology

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Example Binary Distillation Column
1. Primary CVs (self-optimizing) z Top and
Bottom compositions
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Example Binary Distillation Column
2b. Disturbance rejection y2 Temperature
on Tray 15 u2 Vapor Boilup
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Example Solid Oxide Fuel Cell
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Extra Manipulated Variables
Disturbance sensitivity Fresh fuel, Air Bypass
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Performance Evalution
Inputs are also within bounds
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• Control Structure Design
• Challenges
• Tools (partial solutions)
• Case Studies
• Future Directions

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Controller Complexity

• Minimize controller complexity subject to the
  achievement of accuracy specifications in the
  face of uncertainty. (Nett, 1990)
• How to define controller complexity?
• Number of non-zero elements of controller
• Number of tuning parameters
• How to consider it during structure selection?

Nobakhti, Proceedings of ISIC MED, 2005
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Computational Aspects
Alternatives
Problem size
Millions of Alternatives
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Conclusions Remaining Challenges

• Controller complexity
• Definition, inclusion in selection procedure
• Computational aspects
• Integer variables, Non-convexity, Multi-objective
• NP-hardness (Integrity problem)
• Non-linear systems
• Most of theory Linear systems
• Time-scale separation
• Speeds of layers

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Control Structure Design New Developments and
Future Directions

• Vinay Kariwala and Sigurd Skogestad
• Department of Chemical Engineering
• NTNU, Trondheim, Norway

skoge_at_chemeng.ntnu.no