A practical approach for process control optimization during start-up

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.

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

What is Optimization? Lost opportunities will never come back!

Is this Good Control Performance? Stock chest consistency

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

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

Improved Response to SP Change Stock Chest Consistency

Also Decreased Steady State Variability Stock Chest Consistency Before After

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

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

Steady State Observations If tuning is stable at one setpoint, but oscillates at another, the process may be non-linear TIC550.006 Stable tuning at low temp cycles at high temp

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

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

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

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”

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

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

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

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

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

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

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

Actions and Expected Results Reduction in: Variability by 2 Cycling remove Valve wear by 4 - 10 Increase in: Robustness/Stability by 2 - 3 times Performance by 2 times Efficiency by 1 - 10%

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!

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

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

Questions? Marc Tardif marc.tardif@bba.ca