The Un-Tunable PID Control Loop

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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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

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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 PUBLIC

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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 CHICAGO PROCESS SOLUTIONS SUMMIT

∆ ∆

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 PUBLIC

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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 PUBLIC

Ti CHICAGO PROCESS SOLUTIONS SUMMIT

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 PUBLIC

Ti CHICAGO PROCESS SOLUTIONS SUMMIT

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

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PID has somewhat: 

Shorter Rise Time

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Faster Settling Time

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Smaller Overshoot

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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. PUBLIC

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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:

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

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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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 PUBLIC

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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

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Leverage advanced heuristics

 Proactively address performance issues

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Improve plant-wide awareness

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Identify problems, isolate root-causes

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Questions Robert Rice, PhD Vice President, Engineering November 2014

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