Control Station: PID Control Loops and Process Optimization: Capitalize on What Your Plant is Telling You

Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved. Rockwell Automation TechED 2015 @ROKTechED #ROKTechED

PUBLIC INFORMATION

PR23 Control Station PID Control Loops and Process Optimization


AGENDA

PR23 - Control Station: PID Control Loops and Process Optimization:

Capitalize on What Your Plant is Telling You

Identify controller tuning, mechanical and process architecture issues

using your plant’s existing process data.

Use advanced forensics to isolate root-causes.

Industrial case studies highlight enhancements to production

throughput and efficiency.


Economic Drivers

Process Automation: A State-of-the-State Assessment

The Amazing Problem-Free Plant

Michael Brown Control Engineering

20% of control systems are not properly configured to meet their objectives

30% 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 Drivers

Top Line and Bottom Line Benefits

Production

Throughput

Production

Yield

Energy

Consumption

Production

Defects

2 – 5%

5 – 15% 25 – 50%

5 – 10%

Invest in Control – Payback in Profits

Carbon Trust


Does Tuning Really Save Money?

Previously Unmonitored Temperature Loop

No Complaints from Production!

Controls Cooling Liquid to Part Quench Bath

Just the numbers

Before tuning,

Average temperature was 54°C,

Average controller output was 8.64%

After tuning,

Average temperature is 55°C (at set-point)

Average Controller output is now 7.53%

1% Less Cooling, with more opportunity through Set-Point Adjustments

~1% Less Cooling for Every 1 Degree Increase in Set-Point


What Your Plant is Telling You

Overall Loop Health

Stiction

Average Absolute Error

PV Noise

Tuning Recommendations

Oscillation Likelihood

Distribution

Controller Mode

Metrics Focus on

General Loop Performance

Controller Tuning Issues

Valve/Mechanical Issues

Interaction Analysis


Simple Performance Metrics

Overall Health

The overall health

provides a single

performance factor to identify

controller performance

issues.

Noise Band

This is an estimation of the amount of high frequency noise present in the process

variable. Increases in noise could

indicate potential

sensor failure.

PV & SP Distribution

This metric can be used to

understand how well

sensors or processes are

sized.

PV & SP Travel

The process variable travel can indicate if your process

is operating at steady-state or is consistently moving around

PV & SP Average

The Average Variable is a measure of the typical

value of the process

variable and set-point signal.

PV & SP Variance

The Variable Variance is a measure of

the spread of the Variable

value. A tighter

variance is desirable as it shows a well

controlled process.


Controller Performance Metrics

Tuning Deviation

Track changes in process data and

calculate PID Tuning Values. How far are the

tuning parameters from optimal

Oscillation

This is the likelihood that the process variable is oscillating. It is the amount of the

power of the dominant

frequency divided by the total power from the spectral

analysis.

AAE

The AAE measures the

difference between the Set

Point and the measured

Process Variable. This metric is

used to track how well the controller is able to maintain

its set-point.

Mode Changes

This metric tracks the number of

changes (i.e. auto vs manual) in

order to highlight potential

problems.

Percent Time In Normal

This metric tracks the percentage of time the controller

spends in each available

controller mode (i.e. auto vs

manual) in order to highlight potential

problems.


Everyday Output Changes

Output Changes a Source of Suitable Data For Analysis

Including SP Changes, Manual Bump Tests

No configuration required for setting noise limits, minimum step size or window length

Model Fits are Generated using Non Steady State (NSS) Modeling Innovation

Tuning Parameters Generated for each loop based on the criteria specified by the user

Slider Bar (Fast/Slow) or Specific Closed Loop Response Time

Metrics based on Deviation from Recommended Tunings



Users can quickly review all recent

model fits and alter the automatic

model fit starring

Review Model Fits



When a single loop is selected, the model parameters for all of the fits are

displayed in as a histogram.

The recommended tunings are based on the configuration specified for

each loop



Quickly Identify When Tunings

Need to be Adjusted

Existing Tunings are compared to

recommended tunings

Distribution of tunings based on

available model fits

Alerts based on number of

standard deviations outside of

recommended range.



Analyze and Tune Rockwell PID

Controllers

Import Data from Historian or

Connect to FactoryTalk Gateway

or RSLinx for High Speed Data

Bro

wse

Col

lect

M

odel

Tun

e R

epor

t


Mechanical Performance Metrics

Output Distribution

The output distribution identifies

areas where the Final Control

Element is spending its

time.

Output Travel

Output Travel gives

us insight into how

much effort by the Final

Control Elements

required to maintain control.

Stiction

This metric returns the likelihood

and amount of stiction present in

control valves.

Output Reversals

A high number of

output reversals suggests

that excessive wear and

tear on the Final Control

Element.

Output Variance

Output Variance is a measure of the spread of Controller

Output values.

Output Average

Average Controller Output is a measure of the typical

value of the Controller

Output signal.


Stiction Analysis

The mechanical elements

associated with moving a valve or

damper can wear over time. As

these linkages or other parts start

to wear down, there ability to

consistently move the valve stem

or damper degrades.


Controller Interaction Analysis

Power Spectrum

Power Spectrum is a method of evaluating interactions

among and between control loops A Power Spectrum plot

graphically reveals loop interaction by showcasing

common or shared frequency peaks.

Cross Correlation

Correlation analysis measures the similarity of two signals as

a function of a time-shift applied to one of the signals.

This tool can find relationships between signals and identify

the time delay, or lag, between the two.


Power Spectrum Analysis

Measures frequency and

magnitude of change in a signal

Especially good for identifying

oscillating behavior

Different signals may have

something in common if spectrum

peaks coincide

Think of it as a Disturbance

Fingerprint

5 10 15 20 25 30 35

period of oscillation (min)17

no

rma

lize

d p

ow

er

0

0.2

0.4

0.6

0.8

1.0D

PV

Power Spectrum (Spectral Density) Clearly Connects

Disturbance (D) to PV Behavior

disturbance (D) oscillates at a

frequency of 17 minutes.

Process Under PI Control


Power Spectrum Analysis

Group and Sort by any Frequency

Shaded regions represent strong power

Overlay several loops or view loop details

Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved. Rockwell Automation TechED 2015 @ROKTechED #ROKTechED 19

Cross Correlation Analysis


Cross Correlation Analysis

loops are arranged in a matrix

blue is used to denote lagging loops whereas red used to denote leading loops

If users want more information on the lead-lag relationship, they can click on the box to open an expanded view. A bar will be displayed showing where the specific interaction is occurring.


Different Metrics Used by Different Users

Corporate Engineers

Similar Loop Comparison

APC/Constraint Report

Plant Manager Reports

Loop Condition Daily Count

7-day Average Unit Health Trend

Summarized Unit Level Metrics

Control Engineer

Oscillations

Tuning Deviation

Controller Utilization (Normal)

Reliability Engineer/Manager

Valve Metrics

Valve Distribution (Sizing)

PV Noise

Process Operator

Overall Loop Health

Controller Mode


Typical Use Cases

• Focus on comparing process performance across several locations

Corporate

• Basic Overview of how their assets are being utilized

• Which areas of their facility are operating efficiently and track improvements

Plant Manager

• Provide Troubleshooting and Root Cause Analysis

• Metrics Focusing on Controller Tuning, Controller Utilization, and Process Variability

Process/Control Engineer

• Provide Information on Valve Health (Stiction/Saturation)

• Provide information related to instrumentation noise.

Reliability Engineer/Manager

• Simple Indicators and Alerts to highlight controller performance issues

Process Operators


Corporate

Generates reports compare metrics across similar loops

Corporate engineers use these reports to compare performance at different plants

Case Study:

Dryer Performance Across Different Lines and Locations

Similar loop comparison report used to compare

temperature controllers across identical dryers to

identify performance outliers.


Plant Manager

Reports provide high level plant health overview to Plant Manager:

Loop Condition Daily Count

7 Day Average Unit Health Trend

Unit Status

Plant Managers use reports to:

Gauge current plant performance and health

Compare performance to previous performance


Process Control Engineer

Reports provide control loop

performance analysis, for example:

Oscillation Report, Tuning Deviation

Control loop issues are also

identified via: Troublemakers, Alerts

Once an issue is identified, metrics

and process data can be used to

perform root cause analysis

Example Use Cases:

Interactions Analysis


Process Control Engineer

Spectral analysis tool used to identify an oscillation in the steam quality

Correlation showed that steam quality lagging behind the oxygen trim controller

Power Spectrum and Cross Correlation identified the O2 trim controller as the cause of the steam quality oscillations.

Further Investigation identified Stiction on the O2 Control Valve


Reliability Engineer / Maintenance

Metrics which can point to maintenance issues:

Valve metrics

Valve sizing (Average CO valve near constraints)

PV noise

Saturation reports

Case Studies:

Stiction Report

Output Distribution

Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved. Rockwell Automation TechED 2015 @ROKTechED #ROKTechED 28




Metrics identified decreasing controller output distribution (ie: Average CO drops)

Investigation showed excessive valve seat wear allowing more liquid to pass, liquid was even passing while valve was fully closed.


Process Operators

Focus on metrics that summarize

controller health and controller

mode

Operates use alerts to indicate

when a loop metric exceeds its

benchmark for a set period of

time


What Do You Need?

Data Tools Ownership


QUESTIONS?

PR23 - Control Station: PID Control Loops and Process Optimization: Capitalize on What Your Plant is Telling You

Identify controller tuning, mechanical and process architecture issues using your plant’s existing process data.

Use advanced forensics to isolate their root-causes.

Industrial case studies highlight enhancements to production throughput and efficiency.

Your Plant is Constantly Communicating…

Metrics and Reports Provide the Translation…

Listen to Your Plant!

Contact Info:

Bob Rice

[email protected]

Technology

Control Station: PID Control Loops and Process Optimization: Capitalize on What Your Plant is Telling You