Automating Continuous Process Improvement · Automating Continuous Process Improvement George...

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Continuous Process Improvement

© 2011 ExperTune, Inc.

Real-Time Performance Supervision by ExperTune.

Phone: (262) 369

Automating Continuous Process Improvement

© 2011 ExperTune, Inc.

George Buckbee, P.E.ExperTune, Inc.

Time Performance Supervision by ExperTune.

Phone: (262) 369-7711

Continuous Process Improvement

George Buckbee, P.E. ExperTune, Inc.


www.planttriage.com Phone: (262) 369-7711

Automating Continuous Process Improvement George Buckbee, P.E., ExperTune Inc. © 2011 ExperTune Inc

Summary Continuous Improvement separates winners from losers. Your competition is making improvements every day, and you need to keep ahead of them to succeed. Unfortunately, if you are like most process manufacturers, you just don’t have enough staffing. If only there were some way to automatically capture improvement opportunities. New, automated methods are now driving continuous improvements at process manufacturing plants. These methods use process data, with automated analysis and automated diagnostics, that pinpoint improvements to process, equipment, and controls. These plants see significant results for operating cost, energy reduction, and quality improvement.

Overview

Data History – An Under-Utilized Asset Continuous improvement requires a lot of data. A modern control system contains mountains

of data describing the actual performance of a process operation. In fact, most control systems are automatically logging thousands of pieces of information, every second, and storing it in history. But it is not being used! The bottleneck to continuous improvement has become the monitoring and analysis of these mountains of data. Most companies simply do not have enough highly skilled engineers to comb through the data, identify the best opportunities for improvement, diagnose, analyze, and prioritize the actions.



New Methods to Identify and Accelerate Improvements New methods automate these bottleneck steps, making it possible to increase the pace of improvement, even with existing staffing levels. This paper describes methods to:

• Automatically identify continuous improvement opportunities.

• Avoid expensive Design of Experiments (DOE)

• Automatically diagnose specific issues

• Automatically prioritize actions that will achieve the biggest improvements. This paper describes these new methods, giving specific examples to show how most process plants can speed up the rate of improvement with comparatively little investment of time and money.

Automated Assessment & Diagnostics The key to these new methods is to automatically identify opportunities that make process improvements. To automatically find opportunities, software is applied to:

• Continuously monitor process data from the control system or historian

• Periodically assess performance

• Apply known engineering rules to pinpoint specific opportunities

• Prioritize the information and report

Continuous Monitoring and History Over the past 10 years, continuous monitoring and historization of process data has become common. Nearly every plant has some form of process data historian, which collects a 24 hour a day history of the process operation. The data is stored away, and is available for engineers and technicians to track, trend, analyze, and evaluate. Even further, most of his data is available, either in historical form, or as a stream of real-time data using the OPC open standard for communications. So the first step, the infrastructure for continuous monitoring, is effectively in place for most process plants.

Periodic Assessment The next step, automated periodic assessment, is a key step forward. In the past, this step was accomplished manually by engineers. Process engineers would come in every day, review the process operation, assess how the plant operated in the past 24 hours, and then, as time allowed, move on to problem-solving. In today's environment, software is used to perform this performance assessment automatically. A few simple metrics for performance of a control system include:

• Service Factor - what % of time are the controls actually controlling?

• Statistical Measures - Averages, Variability, Standard Deviation, etc.

• Quality - % of time making off-spec product, process capability, etc.

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In fact, many plants do monitor these things on a periodic, monthly basis. And here is great value in doing so. But monthly is not often enough. To effectively manage these items, faster is better. With continuous monitoring and periodic evaluation, you can immediately take a corrective action. This periodic assessment performs two functi

1. Tracking of specific economic and technical performance criteria.2. Serves as a basis for diagnostics.

But there are many “KPI Machines” claiming to perform this sort of analysis. What distinguishes these new methods is that they track not only the hialso lower-level technical factors…things like instrument noise band, number of operator changes, period of oscillation, and many more. This inprovides the information that is needed in the next step Diagnostics.

Automated Analysis and DiagnosticsThe key to these new methods is automated analysis and diagnostics. With limited engineering staff, most companies could not possibly analyze all the data that they are collecting. Automated diagnosengineering analysis unassisted. These automated methods range from quite simple to extremely sophisticated. This section details some of the techniques that have yielded the best results.

Starting Simple

The first, and simplest analysis can propportunity available. The simplest analysis will look at indicators of overall performance. The following automated analysis will identify some opportunities:

Figure 1. A Repor

% of Controllers in Manual

Surprisingly, in a typical process plant, as many as 30% of controllers will be found in Manual mode. This means that these controls are world-class facility will have less than 10% of controllers in manual operation.


Phone: (262) 369

In fact, many plants do monitor these things on a periodic, monthly basis. And here reat value in doing so. But monthly is not often enough. To effectively manage

these items, faster is better. With continuous monitoring and periodic evaluation, you can immediately take a corrective action.

This periodic assessment performs two functions: Tracking of specific economic and technical performance criteria.Serves as a basis for diagnostics.

But there are many “KPI Machines” claiming to perform this sort of analysis. What distinguishes these new methods is that they track not only the high-level KPIs, but

level technical factors…things like instrument noise band, number of operator changes, period of oscillation, and many more. This in-depth assessment provides the information that is needed in the next step – Automated Analy

Automated Analysis and Diagnostics The key to these new methods is automated analysis and diagnostics. With limited engineering staff, most companies could not possibly analyze all the data that they are collecting. Automated diagnostics require computer software to perform the engineering analysis unassisted.

These automated methods range from quite simple to extremely sophisticated. This section details some of the techniques that have yielded the best results.

The first, and simplest analysis can provide an estimate of the amount of opportunity available. The simplest analysis will look at indicators of overall performance. The following automated analysis will identify some opportunities:

. A Report Summarizing Key Performance Factors

Surprisingly, in a typical process plant, as many as 30% of controllers will be found in Manual mode. This means that these controls are not controlling the process

will have less than 10% of controllers in manual operation.


Phone: (262) 369-7711

In fact, many plants do monitor these things on a periodic, monthly basis. And here reat value in doing so. But monthly is not often enough. To effectively manage

these items, faster is better. With continuous monitoring and periodic evaluation,

Tracking of specific economic and technical performance criteria.

But there are many “KPI Machines” claiming to perform this sort of analysis. What level KPIs, but

level technical factors…things like instrument noise band, number of depth assessment

Automated Analysis and

The key to these new methods is automated analysis and diagnostics. With limited engineering staff, most companies could not possibly analyze all the data that they

tics require computer software to perform the

These automated methods range from quite simple to extremely sophisticated. This section details some of the techniques that have yielded the best results.

ovide an estimate of the amount of opportunity available. The simplest analysis will look at indicators of overall performance. The following automated analysis will identify some opportunities:

t Summarizing Key Performance Factors

Surprisingly, in a typical process plant, as many as 30% of controllers will be found not controlling the process. A

will have less than 10% of controllers in manual operation.

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The loss of control is significant. The wasted potential to optimize is even more. The safety risk is evident. But these are not the primary problem. Manual is a symptom of a

Controllers are put in manual by operators. When the operator does not like the way that something behaves, he or she takes manual control. The operator may or may not report the issue. When controllers are left in manof time, it means that the underlying issue has not been resolved. To solve the problem requires a deeper analysis. More on that lateremember that we should track the % of Controllers in Manual as a highindicator of system performance.

Process Constraints

Another very simple analysis is identification of process constraints.the recorded information from process controls, we can see which controllers are fully saturated. That is, which control valves are 100% open, 100% of the time

This analysis is admittedly simplistic. But there is much information to be gained here. Keep in mind that a valvcontroller in manual. It is a constraint to the process, restricting flow more than a line without a valve. And this list is an excellent place to start looking at constraints.

More Simple Stuff

There are many other simple measures of process performance that can identify outliers. This includes on-line, real-time trending of indicators such as:

• Statistics: Variance, Variability, Average, Standard Deviation

• Control Metrics: Average Error, Integral Absolute ErAbsolute Error

• Specifications: Time Off Spec, Process Capability All of this information can be readily available with a little bit of manipulation of existing stores of real-time data.

Adding Specific Diagnostics

At the next level, we can apply automated techniques to identify and diagnose common patterns and common failures. These tools are particularly useful for diagnosing specific equipment failures. Extensive experience has shown us that,


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The loss of control is significant. The wasted potential to optimize is even more. The safety risk is evident. But these are not the primary problem. A controller in

Manual is a symptom of an underlying, unresolved problem.

Controllers are put in manual by operators. When the operator does not like the way that something behaves, he or she takes manual control. The operator may or may not report the issue. When controllers are left in manual for extended periods of time, it means that the underlying issue has not been resolved.

To solve the problem requires a deeper analysis. More on that later. For now, let us remember that we should track the % of Controllers in Manual as a highindicator of system performance.

Another very simple analysis is identification of process constraints. By looking at on from process controls, we can see which controllers are

which control valves are 100% open, 100% of the time

This analysis is admittedly simplistic. But there is much information to be gained here. Keep in mind that a valve that is 100% open is no more useful than a controller in manual. It is a constraint to the process, restricting flow more than a line without a valve. And this list is an excellent place to start looking at constraints.

y other simple measures of process performance that can identify outliers.

time trending of

Statistics: Variance, Variability, Average, Standard Deviation

Control Metrics: Average Error, Integral Absolute Error, Average

Specifications: Time Off Spec, Process Capability

All of this information can be readily available with a little bit of manipulation of time data.



Phone: (262) 369-7711

The loss of control is significant. The wasted potential to optimize is even more. A controller in

Controllers are put in manual by operators. When the operator does not like the way that something behaves, he or she takes manual control. The operator may or

ual for extended periods

For now, let us remember that we should track the % of Controllers in Manual as a high-level

By looking at on from process controls, we can see which controllers are

which control valves are 100% open, 100% of the time.

This analysis is admittedly simplistic. But there is much information to be gained e that is 100% open is no more useful than a

controller in manual. It is a constraint to the process, restricting flow more than a line without a valve. And this list is an excellent place to start looking at constraints.

All of this information can be readily available with a little bit of manipulation of




without a modern approach, many of these problems lie unnoticed, yet have

significant impact on the overall process.

Instrumentation, Valves, and Other Boring Things

You might think that instrument and valve diagnostics are rather mundane tasks, and that operators would already know if they are broken. Not so! Operators are often one step removed from the hardware, trusting the information displayed to them on the computer screen of their control system. How would an operator know if, for example, and instrument had completely flat-lined, and displayed the same value for weeks on end? As it turns out, instruments and valves have established, predictable failure modes. Many of these failure modes can be seen in patterns in ordinary process data history. For example, some flow meters will show “spiking” behavior, where the flow suddenly drops to zero. Aside from the operational issues, such as spurious alarms or nuisance trips, spiking also indicates an approaching instrument failure.

Automated Analysis of Root Cause Automated Root Cause Analysis is one of the most exciting new technologies to arrive on the scene. Given the huge amounts of process data available, it is now possible to identify process correlations and likely root causes without performing experiments.

Massive Cross-Correlation Studies One example of the new approach is the use of a massive cross-correlation study. Large amounts of existing historical process data are run through cross-correlation analysis. When comparing any two signals (process variables such as temperature, pressure, etc.), the analysis provides a correlation vector. The vector shows the strength of correlation between the signals at various time-shifts, which we can refer to as “leads” or “lags”.

Figure 2. A Cross-Correlation Plot



The lead-lag analysis is critically important, because most process plants are relatively linear in nature: Raw Materials are physically transported through the plant, as they are transformed into finished products. This transportation delay results in data “lag”. In other words, an instantaneous change to the raw material might result in changes to finished product many hours later. If we focus only on the strongest correlation in time, then the entire plot can be summarized in two values:

1. The peak correlation coefficient 2. The lead/lag time at that peak

The peak information can be displayed in an “Interaction Hot Spots” display, as shown in Figure 3. Strong correlations are shown in red, while weak correlations are green. Hovering over a particular grid location will display the strength and the lead/lag of the correlation.

Figure 3. Interaction Hot Spots identifies correlation strength and Lead/Lag

Can You Avoid a DOE?

The information contained in the Interaction Hot Spots is very similar to the results of a Designed Experiment (DOE):

• The magnitude of the interaction is made clear.

• The groupings between variables is made clear.

• The lead/lag time factor is made clear.



So, we have achieved many of the results of a DOE, using normally-occurring process data, without performing any experiments. These results can, in fact, be improved by intentionally exciting the process, through the use of setpoint changes or other intentional process changes.

Automated Root-Cause Analysis Process plants are complex places, with many direct and indirect interactions between areas of the plant. The Interaction Hot Spots, shown above, is a good tool for casting a wide net across the plant, and determining which items are related. Suppose, however, that you need to resolve a specific problem. Perhaps a quality attribute is out-of-control, and product is being rejected or recycled. Or energy costs per ton are varying from day to day. Automated Root Cause Analysis can pinpoint the source of many of these problems. Automated Root Cause Analysis uses correlation data in the form of a “Process Interaction Map”, as shown in Figure 4.

Figure 4. A Process Interaction Map

A Process Interaction Map shows both the strength and lead/lag of all factors that

are influencing the key variable. Lead/lag is shown on the horizontal display, and strength of correlation is shown with color. Both positive (red) and negative (blue) correlations are significant.

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The Automated Root Cause Acorrelations with the longest time lead. of variation, furthest upstream in the plant facility.

Automated Root Cause is a Proven Technique

The automated root cause technology has been proven in many processworld-wide:

• A Plastics Plant in Alabama saved $1MM+ after identifying a Cooling Tower cycle that drove process temperature and pressure swings.

• A Chemical plant in Texas found the root cause of distillation column upsets, and immediately captured e

• A Paper Plant in Wisconsin identified the root cause of paper machine weight quality problems in an

Conclusions These new automated methods for continuous improvement can help to time and effort. In these days of limited staffing and limited expertise, great competitive advantage can be gained from:

1. Automatically diagnosinghardware.

2. Identifying key opportunities, such as process co3. Automatically pinpointing the root cause of process variation.4. Avoiding costly and time

Experiments (DOE). 5. Automatically tracking high

reports.

Learn More To learn more about these techniques, request a http://www.expertune.com/r2.asp?f=WPContinImprov&l=WebinarPTPrivate.asp


Phone: (262) 369

Root Cause Analysis finds the root cause by looking for strocorrelations with the longest time lead. This is most likely to be the original source of variation, furthest upstream in the plant facility.

Automated Root Cause is a Proven Technique

The automated root cause technology has been proven in many process

A Plastics Plant in Alabama saved $1MM+ after identifying a Cooling Tower cycle that drove process temperature and pressure swings.

A Chemical plant in Texas found the root cause of distillation column upsets, and immediately captured energy savings of 7,000 pounds per hour.

A Paper Plant in Wisconsin identified the root cause of paper machine quality problems in an unlikely upstream location.

These new automated methods for continuous improvement can help to time and effort. In these days of limited staffing and limited expertise, great competitive advantage can be gained from:

diagnosing common problems with instrumentation

Identifying key opportunities, such as process constraints and poor control.Automatically pinpointing the root cause of process variation. Avoiding costly and time-consuming activities, such as Design of

Automatically tracking high-level results and providing exception

To learn more about these techniques, request a private web-based demonstrationhttp://www.expertune.com/r2.asp?f=WPContinImprov&l=WebinarPTPrivate.asp


Phone: (262) 369-7711

nalysis finds the root cause by looking for strong This is most likely to be the original source

plants

A Plastics Plant in Alabama saved $1MM+ after identifying a Cooling Tower

A Chemical plant in Texas found the root cause of distillation column upsets, nergy savings of 7,000 pounds per hour.

A Paper Plant in Wisconsin identified the root cause of paper machine basis

These new automated methods for continuous improvement can help to reduce time and effort. In these days of limited staffing and limited expertise, great

common problems with instrumentation and

nstraints and poor control.

level results and providing exception-based

based demonstration: http://www.expertune.com/r2.asp?f=WPContinImprov&l=WebinarPTPrivate.asp



PlantTriage is a registered Trademark of ExperTune, Inc. ExperTune is a registered Trademark of ExperTune, Inc. ©2011 ExperTune, Inc.

About the Author George Buckbee is V.P. of Marketing and Product Development at ExperTune. He has been elected a Fellow of the International Society of Automation (ISA). George has 25 years of practical experience improving process performance in a wide array of process industries, George holds a B.S. in Chemical Engineering from Washington University, and an M.S. in Chemical Engineering from the University of California. He is the author of several books and dozens of articles in the process control field.

About PlantTriage ® PlantTriage is a Control Performance Monitoring System that optimizes your entire process control system, including instrumentation, controllers, and control valves. Using advanced techniques, such as Active Model Capture Technology, PlantTriage can identify, diagnose, and prioritize improvements to your process.

Glossary Term Definition

DCS Distributed Control System. A centralized process control system that typically provides data collection, operator interface, and control functions.

I/O Input & Output. KPI Key Performance Indicator. A metric that can be used to monitor overall

performance. OPC OLE for Process Control. An industry standard communications protocol,

allowing OPCHDA OPC Historical Data Access. An enhancement to the OPC protocol that

allows data to be pulled directly from standard data historians. ROI Return on Investment. Measured as the amount of time needed to fully

recoup an investment.

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