1. A practical approach for process control optimization during start-up

2. Presenter Presenter Bio
Marc Tardif, Technologist, has more than 20 years of experience working as a consultant. He has worked in many industrial facilities as an expert in process control and optimization.
He has vast experience in start-up, process optimization, development of control standards and training in power and steam plants, mining, paper mills, manufacturing facilities, pharmaceutical and consumer products.

3. Presentation Outline Introduction and context
Five (5) steps to process optimization
Expected results
Business case

4. What is Optimization?
Lost opportunities will never come back!

5. Is this Good Control Performance?
Stock chest consistency

6. Controller Performance Criteria
Closed loop system must be stable
Closed loop response to disturbances and SP changes must meet performance criteria (slow, fast, etc.)
3. Excessive control action is avoided
4. The system is robust: insensitive to changes in process conditions caused by non-linearities

7. No! SP Response is Too Slow
Stock Chest Consistency
5 min

8. Improved Response to SP Change
Stock Chest Consistency

9. Also Decreased Steady State Variability
Stock Chest Consistency
Before
After

10. Tuning is a compromise Sluggish Aggressive Compromise Setpoint Change
1. Introduction
February 19
Tuning is a compromise
Sluggish
Aggressive
Compromise
Setpoint Change
Load Change
© TOPControl Inc., Unauthorized Reproduction Prohibited

11. Prior to Controller Optimization
Careful observation of steady state data can reveal several issues such as:
Process non-linearity
Valve stiction
Valve hysteresis
Oversized valves

12. Steady State Observations
If tuning is stable at one setpoint, but oscillates at another, the process may be non-linear
TIC
Stable tuning at
low temp cycles at high temp

13. Prior to Controller Operation
With operations personnel, determine
Performance criteria, limitations and constraints
Desired settling time
Determine the effect on other loops and from other loops
Inspect the equipment
Process, Transmitter, Valve
Collect PV, SP, CO time trend data
Verify scaling and set “sampling time” properly

14. Other Loops?
The feedwater A loop is in manual mode. Why is the flow oscillating?

15. 6. Optimization Practices
2/21/2019
Other Loops?
The two feedwater loops will interact FT FT
Steam
Steam LC LT LC LT FY  FY  FC FC FT FV FT FV
Feedwater
© TOPControl Inc., Unauthorized Reproduction Prohibited

16. Process Control Worldwide
20% | control loops have the wrong design
30% | valves have problems
15% | equipment was incorrectly installed
30% | controllers have nonsensical tuning parameters
85% | tuning parameters are inappropriate
30% | controllers are in the wrong mode (not highest mode)
“Only 25% of control loops improve control performance”

17. Steps to Optimizing Controllers
1. Initial tests
Collect data in automatic mode with a constant setpoint
Make one or more setpoint changes
Collect data in manual mode with a constant output
2. Test for process problems and modelling
Test for hysteresis, stiction, linearity, asymmetry if you suspect this is a problem
3. Compute new controller settings
4. Download new settings to the controller
5. Validate new settings with setpoint tests

18. Testing for Process Problems
Process Problem Tests
Hysteresis
Stiction
Linearity
Asymmetry
CO down, down, up, up, up, up, ... CO
CO, or SP down, up

19. Process Tuning Tests Process load must be stable
PV during test must be:
Stable
Change
Manual mode
Step CO up or down
Pulse or double pulse test
Need software
Auto mode
SP change PV PV CO CO PV PV CO CO PV

20. Compute New Tuning Select tests to tune from Tune for Robustness
Tune for the worst case!
Tune for Robustness
Do not forget a PV filter
Simulate the response for old and new settings if you have software for this

21. Evolution – Example: Tuning
Automated tests and identification
While process is running
MPC, multi PID
Optimizing tools
Tuning and analysis
software
Tuning tools
software
Bump test
models
formulas
1970, bump tests
1981, first trainign on loop tuning soft + laptop 40#
1990, loop tuning soft mimic bump tests
2000, automated tests, in auto and cascade
2010, PRBS and other automated tuings, multi loops, matrix of models
Our univ should present those techniques, using a sheet of paper for identification is from another era
Tuning by
trial and error
1970s
1980s
1990s
2000s
2010s

22. Data→ Knowledge → Diagnostics
Result$
Diagnostics
Analysis
Performance
Process systems
Data

23. Try new tuning SP Post-tuning analysis Report After Tuning
Change in variability, no oscillations, etc.
Report

24. Actions and Expected Results
Reduction in:
Variability by 2
Cycling remove
Valve wear by
Increase in:
Robustness/Stability by times
Performance by 2 times
Efficiency by %

25. Business Case – Paper Mill
Four (4) days optimization during a startup
Initial tunings were based on experience with similar process
Initial control strategies were based on industry best practice
Valve/Instrument problem even with new equipment!

26. Business Case – Specific Results
Compared to initial performance with startup values
Variability reduced by 70% on consistency loops
Loops in auto 90% of the time - From startup!
Oscillations eliminated on four (4) level loops
Three (3) advanced control strategies implemented
SP changes eliminated by operator
Action taken every 2-10 minutes by operator

27. Conclusions
According to good practices, one must ensure that every component in the loop is functioning correctly
Changing the controller settings is not always the answer, but if required, follow the five steps

28. Questions?
Marc Tardif