Practical plantwide process
control
Sigurd Skogestad, NTNU
Thailand, April 2014
Course description
• This practically oriented course shows how to
control your plant for improved stability and
economics.
• The approach is systematic and based on the
latest methods, but uses a limited amount of
mathematics.
• You will learn what to control, how to structure
the loops and how to tune your PID controllers.
Course Summary
1.
2.
Find active constraints + self-optimizing variables (CV1). (Economic
optimal operation)
Locate throughput manipulator (TPM)
•
3.
“Gas pedal”
Select stabilizing CV2 + tune regulatory loops
•
4.
SIMC PID rules
Design supervisory layer (control CV1)
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•
Multi-loop (PID) ++
MPC
Difficulties:
1.
2.
Optimization! May need to guess active constraints (CV1)
Handling of moving active constraints
•
Want to avoid reconfiguration of loops
Part 1 (4h): Plantwide control
Introduction to plantwide control (what should we really control?)
Part 1.1 Introduction.
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Objective: Put controllers on flow sheet (make P&ID)
Two main objectives for control: Longer-term economics (CV1) and shorter-term stability (CV2)
Regulatory (basic) and supervisory (advanced) control layer
Part 1.2 Optimal operation (economics)
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Active constraints
Selection of economic controlled variables (CV1). Self-optimizing variables.
Part 1.3 -Inventory (level) control structure
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Location of throughput manipulator
Consistency and radiating rule
Part 1.4 Structure of regulatory control layer (PID)
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Selection of controlled variables (CV2) and pairing with manipulated variables (MV2)
Main rule: Control drifting variables and "pair close"
Summary: Sigurd’s rules for plantwide control
Part 2 (4h): PID tuning
Part 2 (4h). PID controller tuning: It pays off to be systematic!
•
Derivation SIMC PID tuning rules
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Obtaining first-order plus delay models
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Open-loop step response
From detailed model (half rule)
From closed-loop setpoint response
Special topics
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Controller gain, Integral time, derivative time
Integrating processes (level control)
Other special processes and examples
When do we need derivative action?
Near-optimality of SIMC PID tuning rules
Non PID-control: Is there an advantage in using Smith Predictor? (No)
Examples
Part 3 (1h) + Part 4 (3h): case studies
Part 3 (1h). Advanced control layer
• Design based on simple elements:
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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?
Part 4 (3h). Case studies
• Example: Distillation column control
• Example: Plantwide control of complete plant Recycle processes: How to
avoid snowballing