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The Un-Tunable PID Control LoopBest-Practices and Innovations for Tuning Oscillatory, Noisy and Long Dead-Time Processes

Robert RiceVice President, EngineeringMarch 2015

PUBLIC CHICAGO PROCESS SOLUTIONS SUMMIT

Agenda

Closing Thoughts

Real-World Successes

Tuning Demystified

Real-World Challenges

Economic Drivers


Economic DriversProcess Automation: A State-of-the-State Assessment

The Amazing Problem-Free PlantMichael Brown Control Engineering

20% of control systems are not properly configured to meet their objectives30% of PID control loops are operated in manual mode

65% of controllers are poorly tuned to mask control-related problems

85% of controllers perform inefficiently when operated in automatic mode


Economic DriversTop Line and Bottom Line Benefits

ProductionThroughput

Production Yield

EnergyConsumption

ProductionDefects

2 – 5%

5 – 15% 25 – 50%

5 – 10%

Invest in Control – Payback in ProfitsCarbon Trust


Economic DriversMissed Opportunities for Financial Gain

Annual Production & Efficiency LossesControl Station, Inc.

BasicMaterials

Chemicals Power& Utilities

Oil & Gas

$7.6 Million $5.0 Million $1.8 Million $8.0 Million


Agenda

Closing Thoughts


Tuning Demystified


Economic Drivers


Real-World ChallengesThe ‘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


Real-World ChallengesThe Devil is in the Data

Noise Oscillations Dead-Time

Wait for it… Wait for it…


Real-World ChallengesWhere 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


Agenda

Closing Thoughts


Tuning Demystified


Economic Drivers


PID Controller TuningDemystifying the Process

11

Identify the Controller and

Specify the DLO and Control Objective

Find

Perform a “Bump Test” and Collect Dynamic

Process Data

Step

Fit a Model to the

Process Data

Model

Use Tuning Correlations to

Calculate Tunings Based

on Model

Tune

Implement and Test results

Test

Document the Tuning

Process

Document


Tuning DemystifiedTuning 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:

Identify Mechanical, Process and Controller Tuning Issues Facilitate Root-Cause Detection Recommend Appropriate Corrective Action Track and Report Findings


Tuning DemystifiedStep 1: Find Controller, Specify Objective

Good Control is “SIMPLE”


Tuning DemystifiedStep 1: Find Controller, Specify Objective

What is/are the primary Control Objective(s)? Maintain Liquid Level In the Reflux Drum Maintain Column Stability Prevent Environmental Release by Avoiding Drum Hi Limit

Reflux Drum – Level Control Example


Tuning DemystifiedStep 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

Here the PV moves approximately four (4) times the noise band – a good value



Good bump tests Open loop tests require the

Controller Output to be stepped Closed loop tests require a sharp

Controller Output change



Bad bump tests

AVOID Disturbance-Driven Data & Slow Ramping CO Changes



Types of process behavior Self-Regulating

If all inputs are held constant, the process will seek a steady-state

Example: Heat Exchanger

Non Self-Regulating Process will only reach a steady-

state at its ‘balancing’ point Example: Surge Tank



Simple First Order Models Self-Regulating

KP ⇨ Process Gain [ ] ƬP ⇨ Time Constant [time] θP ⇨ Dead-Time [time]

Non Self-Regulating

KP ⇨ Integrator Gain [ ] θP ⇨ Dead-Time [time]

· ∗ ·

“All models are wrong, some are useful” George Box

PVCO

PVtime·CO

*


Tuning DemystifiedStep 3: Fit a Process Model

First Order Plus Dead-Time (Self-Regulating Model)

∆

∆

63%∆

∆∆

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

Process Dead-Time How Much DelayHow much delay is there from when the CO is changed until the PV first moves



First Order Plus Dead-Time (Non Self-Regulating Model)

Integrating Process Gain How Far and

How FastHow 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



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


Tuning DemystifiedStep 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: ƬC is the larger of 0.1Ƭp or 0.8θp

Moderate: ƬC is the larger of 1Ƭp or 8θp

Conservative: ƬC is the larger of 10Ƭp or 80θp

PI tuning correlations use this and the FOPDT model values:and


Tuning DemystifiedStep 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:

1∗

22



Closed Loop Time Constant rules of thumb:

Flow Loops 3 to 5 times the Open Loop Time Constant,

Pressure Loops 2 to 4 times the Open Loop Time Constant,

Temperature Loops 1 to 3 times the Open Loop Time Constant,



Expected PI Controller Response:

Set Point tracking (servo) response as changes

Copyright © 2007 by Control Station, Inc. All Rights Reserved.

Conservative Moderate Aggressive



Challenges of PI Control: Self-Regulating Processes

Base Case Performance

2

Copyright © 2007 by Control Station, Inc. All Rights Reserved.

Kc*2

Kc/2

Kc

Ti/2 Ti Ti*2



Challenges of PI Control: Non Self-Regulating Processes

Kc*2

Kc/2

Kc

Ti/2 Ti Ti*2



PI vs. PID Set Point tracking response

PID shows decreased oscillations compared to PI performance

PID has somewhat: Shorter Rise Time Faster Settling Time Smaller Overshoot


Tuning DemystifiedStep 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 DisturbanceDid 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.


Tuning DemystifiedStep 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?


Tuning DemystifiedIndustrial-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:

Identify Mechanical, Process and Controller Tuning Issues Facilitate Root-Cause Detection Recommend Appropriate Corrective Action Track and Report Findings


Agenda

Closing Thoughts


Tuning Demystified


Economic Drivers


Case Study: PraxairContinuous 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


Case Study: Known UnderperformersContinuous Improvement & Process Optimization

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

Impact Stable control at lower value Savings: ~1% higher process

efficiency

0102030405060708090

100

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

0102030405060708090

100

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

BEFORE

AFTER


Case Study: Known UnderperformersContinuous Improvement & Process Optimization

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

Change PID loop from Manual to Auto; Stabilize control at higher SP Savings: >2% product recovery

increase

0102030405060708090

100

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

0102030405060708090

100

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

SP PV OT


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


Level Control of Medium Pressure Steam Separator

TuneVue Used Existing Set-Point Changes to Identify A Suitable Tuning Parameter Range

Case StudyModels and Tuning Range Automatically Determined


Agenda

Closing Thoughts


Tuning Demystified


Economic Drivers


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 Improve plant-wide awareness Identify problems, isolate root-causes

PUBLIC

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www.rockwellautomation.com www.us.endress.com

Questions

Robert Rice, PhDVice President, EngineeringNovember 2014

the un-tunable pid control loop - smart manufacturing … · the un-tunable pid control loop...

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