The Un-Tunable PID Control Loop
Best-Practices and Innovations for Tuning Oscillatory, Noisy and Long Dead-Time Processes
Robert Rice
Vice President, Engineering
March 2015
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www.rockwellautomation.com www.us.endress.com
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Agenda
Economic Drivers
Real-World Challenges
Tuning Demystified
Real-World Successes
Closing Thoughts
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CHICAGO PROCESS SOLUTIONS SUMMIT
Economic Drivers
Process Automation: A State-of-the-State Assessment
The Amazing Problem-Free Plant
Michael Brown Control Engineering
85% of controllers perform inefficiently when operated in automatic mode
65% of controllers are poorly tuned to mask control-related problems
30% of PID control loops are operated in manual mode
20% of control systems are not properly configured to meet their objectives
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Economic Drivers
Top Line and Bottom Line Benefits
Invest in Control – Payback in Profits
Carbon Trust
2 – 5%
Production
Throughput
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5 – 10%
Production
Yield
5 – 15%
25 – 50%
Energy
Consumption
Production
Defects
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Economic Drivers
Missed Opportunities for Financial Gain
Annual Production & Efficiency Losses
Control Station, Inc.
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$7.6 Million
$5.0 Million
$1.8 Million
$8.0 Million
Basic
Materials
Chemicals
Power
& Utilities
Oil & Gas
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Agenda
Economic Drivers
Real-World Challenges
Tuning Demystified
Real-World Successes
Closing Thoughts
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Real-World Challenges
The ‘Black Art’ of PID Controller Tuning
 Limited Education
 Chemical Engineering curriculum


Single semester totaling 16 hours
Not covered by most trade schools

Focus on PLC programming
 Limited Experience
 Few staff tasked with PID tuning
 Methods handed down

No formalized approach or methodology
Out-of-the-box parameters applied
 Limited Emphasis
 Other projects deemed more important

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Real-World Challenges
The Devil is in the Data
Noise
Wait for it…
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Oscillations
Wait for it…
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Dead-Time
Real-World Challenges
Where to Turn?
 Economic drivers
 Clear opportunities for improvement
 Strong financials: Payback, ROI
 Training & experience
 Limited skilled resources
 Pool of candidates drying up
 Traditional ‘state-of-the-art’ software
 Struggles under ‘real-world’ conditions
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Agenda
Economic Drivers
Real-World Challenges
Tuning Demystified
Real-World Successes
Closing Thoughts
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PID Controller Tuning
Demystifying the Process
Find
Step
Model
Tune
Test
Identify the
Controller and
Specify the DLO
and Control
Objective
Perform a
“Bump Test”
and Collect
Dynamic
Process Data
Fit a Model
to the
Process
Data
Use Tuning
Correlations to
Calculate
Tunings Based
on Model
Implement and
Test results
11
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Document
Document the
Tuning
Process
Tuning Demystified
Tuning Recipe: A Simplified, Repeatable Process
 How do you identify PID control loops that need to be tuned?

Reactive: Respond to the Operator’s Needs

Proactive: Analyze Process Data to Identify PIDs that Contribute to Increased
Process Variability
 Proactive monitoring should:
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
Identify Mechanical, Process and Controller Tuning Issues

Facilitate Root-Cause Detection

Recommend Appropriate Corrective Action

Track and Report Findings
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Tuning Demystified
Step 1: Find Controller, Specify Objective
 Good Control is “SIMPLE”
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Tuning Demystified
Step 1: Find Controller, Specify Objective
Reflux Drum – Level Control Example
 What is/are the primary Control Objective(s)?
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
Maintain Liquid Level In the Reflux Drum

Maintain Column Stability

Prevent Environmental Release by Avoiding Drum Hi Limit
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Tuning Demystified
Step 2: Step or Bump the Process
 Data should show “Cause and Effect”
 A bump test must generate a
response that clearly dominates
the random (noisy) PV behavior
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
Here the PV moves approximately
four (4) times the noise band – a
good value
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Tuning Demystified
Step 2: Step or Bump the Process
 Good bump tests
 Open loop tests require the
Controller Output to be stepped
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
Closed loop tests require a sharp
Controller Output change
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Tuning Demystified
Step 2: Step or Bump the Process
 Bad bump tests
AVOID Disturbance-Driven Data & Slow Ramping CO Changes
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Tuning Demystified
Step 2: Step or Bump the Process
 Types of process behavior
 Self-Regulating
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

If all inputs are held constant, the
process will seek a steady-state

Example: Heat Exchanger
Non Self-Regulating
Process will only reach a steadystate at its ‘balancing’ point
 Example: Surge Tank

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Tuning Demystified
Step 2: Step or Bump the Process
 Simple First Order Models
 Self-Regulating

Non Self-Regulating
∗
·
KP ⇨ Process Gain [ PV
]
CO
 ƬP ⇨ Time Constant [time]


·
PV
KP* ⇨ Integrator Gain [ time·CO
]
 θP ⇨ Dead-Time [time]

θP ⇨ Dead-Time [time]
“All models are wrong, some are useful”
George Box
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Tuning Demystified
Step 3: Fit a Process Model
 First Order Plus Dead-Time (Self-Regulating Model)
Process Gain
 How Far
How Far does the
PV Move for
Change in the
Output
Process Time
Constant
 How Fast
How Fast does it
take the PV to
reach 63% of its
total change
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63%∆
∆
∆
Process DeadTime
 How Much Delay
How much delay is
there from when
the CO is changed
until the PV first
moves
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∆
∆
Tuning Demystified
Step 3: Fit a Process Model
 First Order Plus Dead-Time (Non Self-Regulating Model)
Integrating
Process Gain
 How Far and
How Fast
How Far and How
Fast does the PV
Move when the CO
is moved from its
balancing point Process Dead-Time
 How Much
Delay
How much delay is
there from when the
CO is changed until
the PV first moves
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Tuning Demystified
Step 3: Fit a Process Model
 Tunings are only as good as the model

Manual or Auto-Tune Approaches


Sufficient for Simplest of Controllers
Software Modeling Much More Robust
Open Loop and Closed Loop
 Noisy and Non-Steady State (NSS) Conditions

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Tuning Demystified
Step 4: Tune the PID Control Loop
1
 First compute, ƬC, the Closed Loop Time Constant

A small ƬC provides an aggressive or quick response
 Choose your performance using these rules:



Aggressive:
Moderate:
Conservative:
ƬC is the larger of 0.1Ƭp or 0.8θp
ƬC is the larger of 1Ƭp or 8θp
ƬC is the larger of 10Ƭp or 80θp
 PI tuning correlations use this
and the FOPDT model values:
and
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Tuning Demystified
Step 4: Tune the Level PID Control Loop
 IMC tuning correlation: Depending PID, Non Self-Regulating Process
1
 The Closed Loop Time Constant, , should be as large as possible but
still fast enough to arrest or recover from a major disturbance.
 PI tuning correlations use this and the FOPDT Integrating model values:
2
1
∗
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2
Tuning Demystified
Step 4: Tune the PID Control Loop
 Closed Loop Time Constant rules of thumb:

Flow Loops


Pressure Loops


 2 to 4 times the Open Loop Time Constant,
Temperature Loops

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 3 to 5 times the Open Loop Time Constant,
 1 to 3 times the Open Loop Time Constant,
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Tuning Demystified
Step 4: Tune the PID Control Loop
 Expected PI Controller Response:
Conservative

Moderate
Set Point tracking (servo) response as
Aggressive
changes
Copyright © 2007 by Control Station, Inc. All Rights Reserved.
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Tuning Demystified
Step 4: Tune the PID Control Loop
 Challenges of PI Control: Self-Regulating Processes
Kc*2
Base Case Performance
Kc
Kc/2
2
Copyright © 2007 by Control Station, Inc. All Rights Reserved.
Ti/2
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Ti
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Ti*2
Tuning Demystified
Step 4: Tune the PID Control Loop
 Challenges of PI Control: Non Self-Regulating Processes
Kc*2
Kc
Kc/2
Ti/2
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Ti
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Ti*2
Tuning Demystified
Step 4: Tune the PID Control Loop
 PI vs. PID Set Point tracking response
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
PID shows decreased oscillations
compared to PI performance

PID has somewhat:

Shorter Rise Time

Faster Settling Time

Smaller Overshoot
CHICAGO PROCESS SOLUTIONS SUMMIT
Tuning Demystified
Step 5: Implement and Test Results
 Modified tuning parameters must be tested
 Testing PID Controllers Typically
Involve:

Adjust Set-Point to ensure adequate
tracking
 Did the Process Variable overshoot?
 Did the Controller Output move too
much?

Introduce a Load Change or Disturbance
Did the Process Variable recover quick
enough?
 NOTE: PID controllers work off of controller error (SP-PV). If there is no error, there is nothing for
the PID controller to do. You MUST introduce controller error and force the controller to respond
before it can be determined if the tuning changes actually improved the system.
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Tuning Demystified
Step 6: Document, Document, Document
 Who:
 Who is accountable for the change(s)?
 What:
 Which loop was tuned? What were the
‘As Found’ and ‘Recommended’ tuning
values?
 When:
 When was the loop adjusted?
 Why:
 Why was this particular loop tuned?
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Tuning Demystified
Industrial-Grade Software for Real-World Applications
 How do you identify PID control loops that need to be tuned?

Reactive: Respond to the Operator’s Needs

Proactive: Analyze Process Data to Identify PIDs that Contribute to Increased
Process Variability
 Proactive monitoring should:
PUBLIC

Identify Mechanical, Process and Controller Tuning Issues

Facilitate Root-Cause Detection

Recommend Appropriate Corrective Action

Track and Report Findings
CHICAGO PROCESS SOLUTIONS SUMMIT
Agenda
Economic Drivers
Real-World Challenges
Tuning Demystified
Real-World Successes
Closing Thoughts
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CHICAGO PROCESS SOLUTIONS SUMMIT
Case Study: Praxair
Continuous Improvement & Process Optimization
 Praxair, Inc.
 The largest industrial gases company in
North and South America and one of the
largest worldwide.
 Over 400 Cryogenic Plants Worldwide
 On-stream reliability of 99%
 Standardized on Rockwell Automation
Process Controllers
 Standardized on LOOP-PRO TUNER PID
tuning software across all regions
 The following 2 PID controllers alone
contributed between $75K-$100K USD /
year of savings
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Case Study: Known Underperformers
Continuous Improvement & Process Optimization
 Impact
Stable control at lower value
 Savings: ~1% higher process
efficiency

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BEFORE
0:01
1:37
3:13
4:49
6:25
8:01
9:37
11:13
12:49
14:25
16:01
17:37
19:13
20:49
22:25
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
AFTER
0:01
1:31
3:01
4:31
6:01
7:31
9:01
10:31
12:01
13:31
15:01
16:31
18:01
19:31
21:01
22:31
 Example #1: LIQUID LEVEL CONTROL
 Instability occurred at lower levels making
PID tuning difficult
 Control the level at a reasonable value
(i.e. lower is better)
 Before: Highly noisy PV
 Process safety and efficiency impact
Case Study: Known Underperformers
Continuous Improvement & Process Optimization
Change PID loop from Manual to
Auto; Stabilize control at higher SP
 Savings: >2% product recovery
increase

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0:01
0:24
0:47
1:10
1:33
1:56
2:19
2:42
3:05
3:28
3:51
4:14
4:37
5:00
5:23
5:46
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
SP
PV
OT
0:01
0:24
0:47
1:10
1:33
1:56
2:19
2:42
3:05
3:28
3:51
4:14
4:37
5:00
5:23
5:46
 Example #2: MIXING VALVE CONTROL
 Mix two flows with different specifications
(higher is better)
 Before: Poor tuning. Once in Auto, nearly
tripped the plant. As a result, most of time in
Manual, with low PV.
 Process safety and low product recovery impact
 Impact
PlantESP – TuneVue™
 Continuously Watches for Suitable Data For Analysis and Recommends
Tunings Parameters
 Including SP Changes, Manual Bump Tests

No configuration required for setting noise limits, minimum step size or window length
 Model Fits are Generated using full Non Steady State (NSS) Modeling
Innovation
 Tuning Parameters Generated for each loop based on the criteria specified
by the user (Fast/Slow, Slider Bar)
 Reports/Alerts Generated based on Deviation from Recommended Tunings
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Case Study
Models and Tuning Range Automatically Determined
Level Control of Medium Pressure
Steam Separator
 TuneVue Used Existing Set-Point
Changes to Identify A Suitable
Tuning Parameter Range
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Agenda
Economic Drivers
Real-World Challenges
Tuning Demystified
Real-World Successes
Closing Thoughts
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Closing Thoughts
 Demystify PID controller tuning

Apply a proven, repeatable recipe

Integrate the procedure with existing processes
 Apply ‘industrial-grade’ technologies

Eliminate the steady state requirement

Leverage advanced heuristics
 Proactively address performance issues
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
Improve plant-wide awareness

Identify problems, isolate root-causes
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Questions
Robert Rice, PhD
Vice President, Engineering
November 2014
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www.rockwellautomation.com www.us.endress.com
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