an evolution of step testing and its impact on model predictive control project times ·...

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An Evolution of Step Testing and its Impact

on Model Predictive Control Project Times

Executive Summary

Bumping the process or step testing became a necessary part of implementing Advanced Process Control (APC)

applications with the introduction of Model Predictive Control (MPC) in the 1970’s. MPC technology revolutionized APC in

that it was able to use a model to make control moves based on the future predicted behavior of the process instead of

relying solely on feedback from the current process measurement. The basis of these models were linear ordinary

differential equations. Instead of having to derive these models mathematically, it was discovered that they could be

obtained empirically from process data. To generate the required process data, it was necessary to step the process

multiple times in an up and down fashion until the empirical models could be acquired by model identification algorithms.

In the early days, manual methods were used to step the process. These methods, although sufficient to get models,

typically required many weeks if not months of bumping the process. Deficiencies in these methods were discovered and

new methods were invented to compensate and reduce project implementation time. The on-going practice of inventing

new methods to improve on the previous one started in the 1990’s and continues today. The result has been a threefold

reduction in project times over the last three decades. Efforts to improve on these methods continue today.

Currently, intelligent stepping software is available that incorporates decades of experience. The software automatically

performs many of the tasks previously done manually such as stepping the process, collecting data, identifying models,

and validating models. The software even allows conducting these tasks while an MPC application is controlling the

process. This enables step testing with less impact on the process operation and stepping while the process is being

controlled. This modern stepping software is opening the door to implementing MPC on processes previously considered

inapplicable to MPC. It also affords itself to easing the burden of maintaining existing MPC applications.

An Evolution of Step Testing and its Impact on Model Predictive Control Project Times 2

Table of Contents

Introduction .............................................................................................................................................................................. 3

Brief History ............................................................................................................................................................................. 4

Manual Step Testing (1970s and 1980s) ................................................................................................................................ 4

Open Loop Sequential Automated Stepping (1990) ............................................................................................................. 10

Open Loop Simultaneous Automated Stepping (2000) ........................................................................................................ 15

Open Loop Automated Stepping and Auto ID (2005) ........................................................................................................... 19

Closed Loop Automated Stepping with MPC and Auto ID (2005) ........................................................................................ 24

Enhanced Closed Loop Automated Stepping with MPC and Auto ID (Since 2010) ............................................................. 27

Recent Enhancements (Since 2010) .................................................................................................................................... 32

Summary ............................................................................................................................................................................... 32

Table of Figures Figure 1. Table of various types of CV responses to an MV or DV unit change

Figure 2. Illustration of manual stepping, process movement and model prediction

Figure 3. Compensated MV moves correlated with stepping MV moves

Figure 4. Result of making correlated compensated MV moves

Figure 5. Effect of steps held for long periods

Figure 6. Effect of large steps on distillation column

Figure 7. Effect of large steps on a furnace

Figure 8. Effect of large steps on a reactor

Figure 9. Effect of large steps on a valve

Figure 10. Open loop sequential step patterns

Figure 11. Effect of compensated moves on model ID results using a PRBS pattern

Figure 12. Effect of UMD and drift on model ID results using a PRBS pattern

Figure 13. Comparison of sequential step patterns from appropriate and short settling times

Figure 14. Effect on model ID of a PRBS step pattern from short settling times

Figure 15. Open loop simultaneous PRBS step test patterns

Figure 16. Effect of UMD and drift on model ID results using simultaneous PRBS patterns

Figure 17. Comparison of simultaneous step patterns from appropriate and short settling times

Figure 18. Effect on model ID of simultaneous PRBS step patterns from short settling times

Figure 19. Automated stepping software

Figure 20. Automated stepping software allowable step patterns

Figure 21. Configuring the stepping software

Figure 22. Collect data, step the process and monitor ID results

Figure 23. Evaluate results and lock models

Figure 24. Changing the MVs being stepped and stopping when done

Figure 25. Connecting stepping software to an MPC application

Figure 26. The stepping application and MPC application are connected

Figure 27. Moderating interaction between steps and MPC moves

Figure 28. Configuring the seed MPC application

Figure 29. Specifying seed models for CV/MV pairs

Figure 30. Downloading new models from stepping software to the seed MPC application

Figure 31. Updated seed MPC model matrix


Introduction

Model Predictive Control (MPC) was introduced in the 1970s. At its simplest it aims to use a model to predict the movement of the

processes and inform control. Control moves can therefore anticipate future events, rather than simply reacting to current conditions.

As implied in the name, the technology requires a model of the process. The basis of these models is a derivation of a differential

equation, and more specifically a linear ordinary differential equation (ODE). Although the behavior of the process is not exactly linear,

linear models do a good job of approximating it. The feedback mechanism built into all MPC technologies compensates for subtle

nonlinear process behavior, and this is sufficient for controlling most processes.

Linear ODEs also have many properties that make it easier to implement control algorithms and do closed-loop stability analysis. One

is the principle of superposition: If an independent variable is made larger and then smaller by the same amount, the dependent

variable response going in one direction will be a mirror image going in the other direction. The other key property is that the initial

starting value of the independent variables and dependent variables is irrelevant in absolute value terms. Once starting values are

established, the only thing that matters is how much the independent variables change.

A simple form of linear ODE is expressed by the following equation:

dCV/dt = (material, energy, momentum balance)

To calculate the behavior of the controlled variable (CV) over time (t), it is assumed that a step input of an independent variable, also

known as a manipulated variable (MV) or disturbance variable (DV), is made to the process. Once the step is made, the model

describes the CV’s behavior over time until it steadies out to the steady state (SS).

In commercially available MPC technologies, derivation of the linear ODEs is not necessary. Approximations of the linear ODEs can be

obtained empirically by bumping the MVs or DVs and using a least-squares data fitting algorithm to describe the behavior of the CVs.

These empirically derived models come in many forms, such as step response models, finite impulse response models, Laplace

transform models, various forms of auto regressive moving average models, state space models, and others. Irrespective of the form

used by the commercial technology to describe the model characteristics, when used online, the models are all used in the same way

to predict the future behavior of the CVs given changes in the MVs and DVs.

Figure 1. Table of various types of CV responses to an MV or DV unit change


Figure 1 provides examples of typical responses. Various CV responses are shown along with their respective Laplace Transform. The

top row illustrates first and second order models with and without deadtime. The second row illustrates inverse response and overshoot

models. The third row illustrates zero gain (hump) models and ramp (integrating) models. The final row illustrates higher order (third

and fourth order) models.

Since all MPC technologies rely on process data to derive the models, it is necessary to go through the process of bumping the process

(or conducting step testing), gathering data, identifying the model(s), validating them and commissioning the controller. Although these

tasks are typically considered independent on a project, in reality, they are commingled. Step testing determines the basis for how long

the other tasks take. Successful step testing tends to minimizes the time spent on the successive tasks. Improper step testing can

create an iterative loop with successive tasks, wherein the engineer moves on to the next task only to find out that the models acquired

from step testing was insufficient and the engineer is forced to revisit the step testing procedure. This iterative loop can initiate during

model identification, model validation or commissioning. The net result of improper step testing is longer project times and a higher

percentage of the project time spent on these tasks. This paper focuses not on the identification algorithms, online control algorithm, or

form of model used. Instead it concentrates on methods used to step the process to develop models of sufficient quality to use in an

online controller, and the effect these methods have on the time it takes to implement an MPC application.

Brief History

Step testing methods have evolved over 40 years:

The 1970s and 1980s saw manual open loop step testing methods, conducted by process operators and engineers

In 1990 open loop sequential automated step testing methods were first deployed

2000 marked the development of open loop simultaneous automated step testing methods

In 2005 open loop automated step testing in conjunction with automated model identification methods were deployed

Also in 2005 closed loop automated step testing in conjunction with an online MPC and automated model identification methods were

deployed to maintain existing MPC applications

2010 saw enhanced closed loop automated step testing in conjunction with an online MPC and automated model identification

methods. Enhancements continue to be developed.

The main goal has always been to acquire quality models as quickly and with as little disruption to the process as possible. Progress

over the past decades has been significant, with higher quality models acquired amid less disruption to the process and in shorter time

frames. However, the evolution is on-going. It is useful to look at the methods in more detail.

Manual Step Testing (1970s and 1980s)

In these early years, the primary ambition was to simply get models that could be used online. Stepping the process was a new concept

and met resistance. Engineers had to work closely with operators to implement the steps while ensuring key process variables did not

violate operational constraints. Computing power was insufficient for sophisticated identification algorithms, so simple data sets were

required.

The general method deployed follows:

1. Set up the PIDs such that each MV corresponds to the SP of a PID or the OP of a PID (or a hand valve); in other

words, make the underlying control system open loop. This requires breaking certain cascade structures or putting a

PID in manual.

2. Allow the process to settle so it is not moving very much.

3. Negotiate the size of steps with the operator based on their tolerance for disruption to the process.

4. Make manual steps to a PID.SP or PID.OP, one MV at a time. The steps to the SP or OP are typically made by

requesting the operator make the change.

5. Once stepping commences, the operator is responsible for ensuring CVs do not violate key operational constraints.

The operator is instructed to make compensating changes to other MVs to prevent constraint violations.


6. Collect data off of an historian and take off-line to run through the identification (ID) algorithm.

7. Massage the data as necessary to remove bad data and rerun through the ID algorithm.

8. Repeat 6-7 until acceptable models are obtained.

9. Once acceptable models are obtained, commence stepping on another MV.

10. Repeat 2-9 until acceptable models are obtained for all the CVs and MVs.

11. Repeat 2-9 until acceptable models are obtained for all the CVs and DVs.

12. Validate all models using engineering knowledge and judgment. Models that do not make sense are either removed

or stepping is conducted again to obtain a more realistic model.

13. Build the MPC application and run in prediction mode for the final validation of model quality.

14. With MPC online, step the MVs one at a time to evaluate the behavior of the predicted CVs against their respective

process CV.

15. Massage data or re-step the process until the predictions follow the process closely enough for online control.

16. Begin commissioning by forcing key CVs to violate their constraints and evaluating how effectively the controller

brings them back. If the response is insufficient and tuning cannot rectify the issues, massage the data or step the

process once again.

17. Configure the optimizer and turn on. This typically causes the operation to slowly move from one operating state to

another.

18. Evaluate the performance of the optimizer and further massage models or re-step the process as necessary.

Figure 2. Illustration of manual stepping, process movement and model prediction

Figure 2 illustrates a typical MV manual step sequence (top), the process movement result from stepping the MV (middle), and the

expected prediction results overlaid on the process movement (bottom). These illustrations depict the expected results from the manual

stepping procedure described above. However, processes are rarely so cooperative.


The explanation of the manual method shows the inherent inefficiency. There are a myriad of reasons for this, but three principal factors

contribute to it: compensated MV moves are correlated with the MV being stepped; each step is held for a long time; and the steps

made are too large.

When steps are made manually, the intended step pattern is broken to address CV constraint violations. Unfortunately, breaking the

step pattern often results as an attempt to counteract unmeasured disturbances or drift causing CVs to violate their constraints. This

frequently results in erroneous data as the true movement of the process due to the steps is not captured.

Another method used to address CV constraint violations during this period was making compensating changes with other MVs to keep

key CVs from violating their constraints. Again, when people make the steps, it is natural for correlated compensated changes to be

made between the MV being stepped and MVs used to counteract constraint violations. This tends to prolong the duration of step

testing and results in repeat stepping of many of the MVs. The ID software can handle changes being made to multiple MVs but cannot

deal with correlated changes that essentially cancel out steps.

Figure 3 illustrates steps made to an MV (above), correlated compensated moves made to a different MV (middle), and the resulting

process movement from all steps (bottom). Instead of the process moving in an easily identifiable pattern, as illustrated in Figure 2, the

process is hardly moving at all and it is impossible to make sense of the contribution each MV is having on the process movement. To

make matters worse, the real process movement will also have unmeasured disturbances and drift in it that renders the data essentially

useless for obtaining models.

Figure 3. Compensated MV moves correlated with stepping MV moves

Figure 4 illustrates the results of making this type of move pattern. The correlated MV moves are shown (top), a comparison of the

process movement with and without unmeasured disturbances and drift (middle blue and middle green, respectively), and the predicted

results of the models obtained from both process movements (bottom).

In the bottom graph, the green is the process movement without UMD or drift; the blue represents the prediction from the model ID

performed on the data without UMD or drift; and the red represents the prediction from the model ID performed on the data with UMD

and drift. The blue prediction seems to follow the process reasonable well, but it is difficult to discern. The red prediction does not

represent the process movement at all, and the models that generated it are the likely results from real process data with these types of

compensated MV moves.


Figure 4. Result of making correlated compensated MV moves.

The result was step testing data that did not yield accurate enough models to use online and the need to redo the step testing for the

MV. This is something that obviously needs to be avoided for any step testing method to be successful.

Figure 5. Effect of steps held for long periods

The next issue encountered with the manual method is that steps are held until the process settles out. Long periods between steps

were best practice at the time, but this allowed UMD or drift to affect the quality of the data. Figure 5 illustrates the result. The top graph

illustrates the process movement without UMD or drift (green), the process movement with UMD and drift (blue) and the resultant

prediction from the model ID using the process movement with UMD and drift (red). The duration of the stepping was 1320 minutes.


The bottom graph illustrates the process movement without UMD or drift (green), the prediction from the model ID using process

movement without UMD and drift (blue), and the prediction from the model ID using process movement with UMD and drift (red). In the

top graph, the prediction looks like it fits the process without UMD and drift fairly well. However, in the bottom graph, when compared to

the prediction from the process without UMD and drift, the prediction is not quite accurate. The model that produced the red prediction

differs in gain to the model that produced the blue by 25%, and the red’s settling time is about 30% shorter. As illustrated, holding steps

for long periods makes the data sensitive to UMD and drift that naturally occur in processes. This unusable data must be massaged out

to get better models from the ID algorithm, or the step testing period prolonged. The result is very long step testing periods for each MV

to ensure sufficient data for quality models.

The final issue encountered with the manual method is that steps made to the process are many times too large and do not resemble

the MV moves the MPC would make. Larger steps are implemented to ensure a response to the CVs can be seen. However, large

steps tend to push the process well out of equilibrium, while smaller changes made by the MPC will not.

Figure 6 illustrates a potential problem encountered when making large steps to distillation columns. The tower on the left i llustrates two

examples of what happens to the temperature profile when a large reflux move is made suddenly. The tower on the right illustrates

what happens to the temperature profile when the MPC makes the same size reflux move but over a longer period. The upper curve on

the left tower shows the temperature profile sagging more when the move is made suddenly.

Figure 6. Effect of large steps on distillation column

The model produced from the ID reflects the process behavior during step testing, which is the behavior expected when the MPC

makes moves online. However, when the MPC is online, the actual process behavior differs from the model’s prediction. In this

example, the model gain is fairly close to reality, but the dynamic behavior differs significantly. The lower curve on the left tower shows

similar attributes to the upper curve, but the sag is more exaggerated. The model gain is very different from the reality. Both examples

lead to errant models, but the second example is much more serious and will likely result in having to correct the models for the MPC to

effectively control the process.

Figure 7 shows a similar potential problem encountered when making large steps to furnaces. The furnace on the left illustrates the

effect on the furnace outlet temperature when a large, sudden feed move is made; the furnace on the right, when the same size feed

move is made gradually.


Figure 7. Effect of large steps on a furnace

The curve on the left furnace shows the outlet temperature sagging more than the curve on the right. The result of erroneous

predictions of the process behavior when the MPC is online is similar here to that described above. Again it may lead to having to

correct the models for the MPC to effectively control the process.

Figure 8 illustrates a potential problem encountered when making large steps to reactors. The reactor on the left shows the severity

when a large temperature move is made at once; the reactor on the right when the MPC makes the move gradually. The curve on the

left shows the severity overshooting and moving back down, while using the MPC results in no overshoot. Again, the results of

inaccurate predictions when the MPC is online are similar to those already described, and could mean having to correct the models.

Figure 8. Effect of large steps on a reactor

Finally, Figure 9 shows a potential problem in making large steps to setpoints. The left shows the valve output when a large setpoint

move is made all at once; the right when it is made gradually. The left valve’s output overshoots and falls back, while the right suffers

no overshoot. Here again, models may need correcting for the MPC to effectively control the process.


Figure 9. Effect of large steps on a valve

In summary, larger steps make it easier to ID models, but the models do not represent the process well. The above illustrations show

that the large steps used in the past tended to excite the process in a manner that is inconsistent with the impact of smaller changes

made by the online MPC. Once this was discovered during commission, model massaging or re-stepping proved necessary to rectify

the problem. This prolonged commissioning time or required repeated step testing.

This manual method typically led to stepping for two to six months or more (depending on the size of the application) to get enough for

a base set of models. Model validation and correcting erroneous models added another few weeks or months to the project. The need

to further modify models during commissioning added another two to six months. Projects typically took six months from start to finish

for small applications (5-6MVs) and up to two years for larger applications (25-30MVs). The tasks of stepping the process, identifying

models, validating models and commissioning took up to 75% of the overall project time.

Open Loop Sequential Automated Stepping (1990)

By 1990 MPC applications were more accepted by the process industry and interest in implementing them was growing. The need for

step testing became more widely accepted. Attention turned to reducing the time it took to implement MPC applications.

Faster computers enabled implementation of more sophisticated identification algorithms to handle larger data sets and data that had

steps of an MV commingled with scattered steps of other MVs and DVs. The importance of steps that spanned the power spectrum to

obtain better quality models was also recognized – in other words, a mix of steps consisting of varying switching times (steps held for

short, medium and long periods of time). The final development was the move to restrict human involvement in the step testing

procedure as far as possible to reduce the effect of correlated moves. Instead, software could automatically step the process.


1. The size of steps is negotiated with the operator based on their tolerance for disruption to the process.

2. This is input, together with an estimate of the process settling time, to an algorithm that designs the desired step

pattern to span the entire power spectrum. Two types of step patterns were available – a pseudo random binary

sequence (PRBS) and a Schroeder-Phased sinusoid.

3. Transfer the designed step pattern to an online software module to conduct the steps.


4. Set up the PIDs such that each MV corresponds to the SP of a PID or the OP of a PID (or a hand valve) – in other

words, make the underlying control system open loop. This required breaking certain cascade structures or putting a

PID in manual.

5. Allow the process to settle out so it is not moving much.

6. Make steps to a PID.SP or PID.OP one MV at a time using the designed step pattern.

7. Once stepping commences, the operator is still responsible for ensuring CVs do not violate key operational

constraints. The operator is instructed to make compensating changes to other MVs to prevent constraint violations.

8. Collect data off an historian and take off-line to run through the ID algorithm.

9. Massage the data as necessary to remove bad data and rerun through the ID algorithm.

10. Repeat 8-9 until acceptable models are obtained.

11. Once acceptable models are obtained, commence stepping on another MV.





15. Build the MPC application and run in prediction mode to do the final validation of model quality.


process CV.



brings them back. If the response is insufficient and tuning cannot rectify the issues, data massaging or stepping the

process is again necessary.

19. Configure the optimizer and turn on. This typically caused the operation to slowly move from one operating state to

another.

20. Evaluate the performance of the optimizer and further massage data or step the process again as necessary.

Figure 10 illustrates a typical pseudo random binary sequence (PRBS), a Schroeder Phased sinusoid, and a filtered PRBS. All three

step patterns are designed with switching intervals that cover the entire power spectrum for the settling time provided. To design the

step pattern of choice, the engineer entered the desired step size and an approximate settling time for the CVs associated with each

MV into a software program. The program then generated the step pattern that was deployed via an online running application. The

PRBS was typically used unless large steps to the process were deemed necessary. The user would then choose either the Schroeder

Phased sinusoid or a filtered PRBS step pattern to avoid the effect of implementing large steps described in the section on manual step

testing, above.

Despite the computer implementing most of the steps, an operator is still needed to make compensated moves to keep CVs from

violating constraints. The structure of the automated step pattern helps reduce the incidence of humans making correlated moves, but

they still need to be removed from data before the ID algorithm is run. The benefit is there are many more steps made and at different

switch intervals so the proportion of correlated compensated moves is significantly reduced, and many times the ID algorithm could still

get quality models with the correlated moves left in the data.


Figure 10. Open loop sequential step patterns

Figure 11 illustrates a PRBS pattern and the same compensated MV moves illustrated in the section on manual step testing (above); a

comparison of the process movement with unmeasured disturbances and drift (middle blue) and without (middle green); and the

predicted results of the models obtained from both process movements (bottom).

Figure 11. Effect of compensated moves on model ID results using a PRBS pattern

In the bottom graph, the green is the process movement without UMD or drift; the blue represents the prediction from the model ID

performed on the data without UMD or drift; and the red represents the prediction from the model ID performed on the data with UMD

and drift. Both the blue and red predictions follow the process well and there is little difference between them. The gains and time

constants are essentially the same. This example shows that compensated moves can be made with very little effect on the model ID

results.

PRBS

Schroeder Phased

Filtered PRBS


The next issue to address is the effect of UMD and drift on the model ID results. Figure 12 illustrates the result of UMD and drift on step

patterns that cover the power spectrum.

Figure 12. Effect of UMD and drift on model ID results using a PRBS pattern

The top graph illustrates the process movement without UMD or drift (green), the process movement with UMD and drift (blue), and the

resultant prediction from the model ID using the process movement with UMD and drift (red). The duration of the stepping was 655

minutes, compared to 1320 for the manual method illustrated in Figure 5. The bottom graph shows the process movement without UMD

or drift (green), the prediction from the model ID using process movement without UMD and drift (blue), and the prediction from the

model ID using process movement with UMD and drift (red). In the top graph, the prediction looks like it fits the process without UMD

and drift. This is verified in the bottom graph when it is compared to the prediction from the process without UMD and drift. The model

producing the red prediction differs in gain to the model that produced the blue by 10% and the settling times are essentially the same.

The graph shows issues from making long steps are essentially rectified by using a step pattern that covered the power spectrum. With

this step pattern, the model ID algorithms are able to get quality models without having to remove much of this naturally occurring

phenomenon over the duration of the step testing period. The mix of short, medium and long steps not only reduces the effect of

unmeasured disturbances and drift on the step test data, but the model is obtained with half the step testing time of the manual

stepping method.

Making steps that are too large is still an issue. Even though the step pattern is automated, there is still a desire to make big enough

steps to see the movement in the CVs. To rectify this, the engineer is provided with choices in the type of steps that can be designed.

He can use a standard PRBS signal to make conventional steps if the process is well behaved or does not require large steps to get the

CVs to move. If steps have to be large, the engineer implements the standard PRBS but filters the step change in over time, or designs

a Schroeder-Phased sinusoid as the step pattern. As illustrated in Figure 10, the Schroeder-Phased sinusoid is an oscillating pattern

that does not make large changes to the process all at once. The downside to filtering the PRBS or using the Schroeder is that the

engineer must trust that the ID algorithm will get a quality model from the data as it is difficult to detect sufficient CV movement in the

data with the naked eye.

Over time with this method engineers found that quality models could still be obtained and began to rely on these methods more often.

Despite the benefits automated step patterns brought, new issues arose. First, to get quality models, the engineer had to provide a

good approximation of the process settling time, but to reduce the step testing time, engineers tended to underestimate this. The result


was step patterns with switching intervals that were too close together. Figure 13 illustrates the difference between step patterns with

appropriate settling times and those with settling times that are too short.

Figure 13. Comparison of sequential step patterns from appropriate and short settling times

The difference between the sequential step pattern resulting from an appropriate settling time (top) and from a short settling time

(bottom) can be seen. Switching intervals in the top patterns are much further apart than those in the bottom.

Figure 14 shows the effect on the prediction of using a shorter step pattern than appropriate in the presence of UMD and drift (top), and

in the presence of compensated moves, UMD and drift (bottom).

The top graph illustrates the process movement without UMD or drift (green), the prediction from the model ID using an appropriate

settling time (blue), and the prediction from the model ID using a short settling time (red). The models differ in both gain and settling

time, with the model producing the red prediction having a 40% smaller gain and 400% longer settling time than the model producing

the blue prediction. The bottom graph illustrates the process movement without UMD or drift but in the presence of compensated MV

moves (green); the prediction from the model ID using an appropriate settling time (blue); and the prediction from the model ID using a

short settling time (red). These models also differ in both gain and settling time. The model that produced the red prediction is 35%

smaller in gain and has a 50% longer settling time than the model that produced the blue.

The model differences are the result of step patterns that did not cover the true power spectrum but only the higher frequency parts of

it. This leads to erroneous models in both gain and settling time. To make matters worse, with short switching intervals it is nearly

impossible for the engineer to evaluate whether the steps are generating data good enough for the ID algorithm to obtain quality

models. Under these circumstances, the engineer has no choice but to redo the step testing exercise.


Figure 14. Effect on the model ID of a PRBS step pattern from short settling times

Using automated step patterns did, however, reduce the duration of MPC projects compared to manual stepping, cutting the time taken

to step a process in half. Model validation and correcting erroneous models was also twice as quick. The need to further modify models

during commissioning was cut by about 25%. Projects now typically took up to four months from start to finish for small applications and

up to 18 months for larger applications. Time spent stepping the process, identifying models, validating models and commissioning was

reduced to 67% of the overall project time.

Open Loop Simultaneous Automated Stepping (2000)

The quest to reduce implementation time continued with simultaneous, rather than sequential, automated stepping. Stepping two or

more MVs at the same time could reduce the overall time to step the process, while computers continued to get faster and ID

algorithms continued to improve.


1. Negotiate the size of steps with the operator based on the disruption to the process they are comfortable with. This

method, however, allows larger steps to be made without causing severe constraint violations due to the inherent

filtering resulting from stepping multiple MVs in an uncorrelated fashion.

2. Input the size of steps, an estimated settling time, and the group of MVs to be stepped simultaneously to an

algorithm, which designs the desired step pattern. These patterns not only cover the complete power spectrum but

are also uncorrelated. Only the PRBS step pattern was available since it is impossible to create uncorrelated

Schroeder-Phased sinusoids.

3. Transfer the step patterns to an online software module to conduct the steps.

4. Set up the PIDs so that each MV corresponds to the SP of a PID or the OP of a PID (or hand valve). In other words,

make the underlying control system open loop. This required breaking certain cascade structures or putting a PID in

manual.

5. Allow the process to settle out.

6. Make steps to the group of MVs in accordance to their uncorrelated step patterns.

7. Once stepping commences, the operator remains responsible for ensuring CVs do not violate key operational

constraints. The operator is instructed to make compensating changes to other MVs to prevent constraint violations.

8. Collect data from an historian and take off-line to run through the ID algorithm.


9. Massage the data to remove bad data and rerun through the ID algorithm.

10. Repeat 8-9 until acceptable models are obtained for the MV group.

11. Once acceptable models are obtained, commence stepping on another group of MVs.





15. Build the MPC application and run in prediction mode to do the final validation of model quality.


process CV.




process should be done again.

19. Configure the optimizer and turn on. This typically caused the operation to slowly move from one operating state to

another.

20. Evaluate the performance of the optimizer and further massage models or step the process again as necessary.

Figure 15 illustrates a PRBS pattern for MV1 (green), a PRBS pattern for MV2 (brown), and the resultant process movement without

UMD or drift (blue). The simultaneous step patterns are designed to be uncorrelated and with switching intervals that cover the entire

power spectrum for the settling time provided. To design the step pattern of choice, the engineer entered the desired step size and an

approximate settling time for the CVs associated with each MV into a software program. The program then generated the step patterns

deployed via an online running application. The PRBS was typically used unless large steps to the process were deemed necessary.

The user could choose a filtered PRBS step pattern to avoid the effect of implementing large steps as described in the manual step

testing section. The Schroeder Phased sinusoid is no longer available due to the difficulty of creating uncorrelated patterns.

Figure 15. Open loop simultaneous PRBS step test patterns


Despite a computer implementing most of the steps, the operator must still make compensated moves to keep CVs from violating

constraints. The structure of the simultaneous step patterns essentially eliminates the risk of people making correlated moves.

Removing correlated steps from the data before the ID algorithm is run becomes less of an issue.

The effect of UMD and drift on the step test data is also much less of an issue. ID algorithms are able to obtain quality models without

having to remove this naturally occurring phenomenon over the duration of the step testing period.

Figure 16 illustrates the result of UMD and drift on simultaneous uncorrelated step patterns that cover the power spectrum.

Figure 16. Effect of UMD and drift on model ID results using simultaneous PRBS patterns

The top graph illustrates the process movement without UMD or drift (green), the process movement with UMD and drift (blue) and the

resultant prediction from the model ID using the process movement with UMD and drift (red). The duration of the stepping was 550

minutes, compared to 1320 for the manual method (Figure 5) and 655 for the automated sequential method (Figure 12). The bottom

graph illustrates the process movement without UMD or drift (green), the prediction from the model ID using process movement without

UMD and drift (blue), and the prediction from the model ID using process movement with UMD and drift (red).

In the top graph, the prediction looks like it fits the process without UMD and drift well. This is verified in the bottom graph, when it is

compared to the prediction from the process without UMD and drift. The MV1 and MV2 models that produced the red prediction have

the same gains as the models that produced the blue one. The red and blue model for MV2 have the same settling time, and the red

model for MV1 differs in settling time from the blue MV1 by 20%. The sensitivity to UMD and drift resulting from long steps is essentially

removed by using simultaneous uncorrelated step patterns that cover the power spectrum. With these simultaneous step patterns, the

model ID algorithms are able to get quality models without removing much of this naturally occurring phenomenon over the step testing

period. The models are also obtained with only 75% of the step testing time required for the sequential stepping method.

Making steps that are too large is still possible but is less of an issue. Stepping multiple MVs at once tends to create a built-in filtering of

the CV movement, dampening the ability of larger steps to throw the process out of equilibrium. Of course, the size of the steps still

needs to be selected prudently to avoid upsetting the process operation too much. The option to filter the steps is still available in the

event that steps need to be large. The downside of filtering the PRBS signals too much also still exists.

While an additional reduction in implementation time is realized with this method, the need to specify an accurate settling time becomes

crucial. Failing to specify an appropriate settling time leads to very low quality data that is essentially useless.


Figure 17. Comparison of simultaneous step patterns from appropriate and short settling times

Figure 17 illustrates the difference between step patterns with appropriate settling times (top) and short settling times (bottom). The

switching intervals for the former are much further apart than those for the latter.

Figure 18. Effect on the model ID of simultaneous PRBS step patterns from short settling times

In figure 18 the top graph illustrates the process movement without UMD or drift (green), the process movement with UMD and drift

(blue), and the prediction from the model ID using short settling times (red). The bottom graph illustrates the process movement without

UMD or drift (green), the prediction from the model ID using appropriate settling times (blue), and the prediction from the model ID

using short settling times (red).

It is clear from the top graph that the model ID results generated bogus models with short simultaneous step patterns. The bottom

graph, which compares the predictions using models with short simultaneous step patterns and predictions using appropriate patterns,

shows just how far off the models are. Short step patterns when stepping a single MV at a time produces low model quality results.


Using short step patterns when stepping multiple MVs simultaneously produces garbage. It is also impossible for the engineer to

massage this data into something useful. If quality models are not acquired, the engineer has little choice but to redo the step testing.

Overall properly implementing simultaneous automated step patterns further reduced the duration of MPC projects. The time to step a

process was cut by another 25%; the model validation and correcting of erroneous models was cut by a further 50%; the need to further

modify models during commissioning was cut by approximately another 25%. Projects now typically took up to 3 months from start to

completion for small applications and up to 14 months for larger applications. The tasks of stepping the process, identifying models,

validating models and commissioning were reduced to 60% of the overall project time.

Open Loop Automated Stepping and Auto ID (2005)

The next evolution to reduce implementation time was to fully automate the step testing procedure. The key drivers were the desire to

remove human intervention from the procedure and reduce the time and skill level required to implement step testing.

This was accomplished by creating stepping software that could step the process, collect data, identify models and validate them into a

single methodology. Computers were now fast enough to allow very sophisticated ID algorithms to be developed and run in real-time,

and to allow stepping the process, collecting data and running the identification algorithm online to all be automated. It was no longer

necessary to collect data and transport it to an off-line software package to run the ID algorithm. Automatic data massaging algorithms

are implemented to look for certain types of bad data and remove it. The ID algorithm automatically estimates the quality of models

being identified and reports this to the engineer. The stepping software supports open loop sequential and open loop multivariable

stepping procedures. The majority of the off-line model validation is incorporated as part of the procedure.


1. Negotiate the size of steps with the operator based on how much disruption to the process they are comfortable with.

2. Configure the stepping software to communicate with the underlying PID.SPs or PID.OPs for each MV and the

process variables associated with each CV and DV.

3. Build the stepping application to run in real-time at a specified interval.

4. Determine whether sequential stepping or simultaneous stepping is to be conducted.

5. For sequential stepping, configure the stepping application with the size of steps and an estimate of the process

settling time for each MV.

6. For simultaneous stepping, configure the stepping application with the step sizes, an estimate of the process settling

time and the first group of MVs to be stepped simultaneously. For this type of stepping, the step sizes can be larger,

as discussed in the previous section.

7. Specify how often the ID algorithm executes.

8. Specify all the sub models (CV-MV and CV-DV) to be identified that corresponded to the MV or group of MVs to be

stepped.

9. Set up the PIDs such that each MV corresponds to the SP of a PID or the OP of a PID (or hand valve) – in other

words make the underlying control system open loop. This requires breaking certain cascade structures or putting a

PID in manual.

10. Allow the process to settle.

11. Enable the stepping software to begin data collection.

12. Enable the stepping software to begin making steps either sequentially or to the first group of MVs. The stepping

software automatically designs the sequential step patterns for each MV or designs the uncorrelated step patterns for

the group of MVs to be stepped.

13. Once stepping commences, the operator is still responsible for ensuring CVs do not violate key operational

constraints. However, the stepping software enables the engineer to make compensating steps to any MV at any time

and in any direction to address CV constraint violations.

14. The stepping software automatically triggers the ID algorithm to execute at the specified interval.


15. After each ID execution, the engineer evaluates the quality of the models obtained by comparing model predictions to

the process movement and utilizing model quality information reported for each sub model by the ID algorithm.

16. Massage the data to remove remaining bad data and rerun through the ID algorithm or wait for the next automatic ID

execution. The automated data removal algorithm is designed to look for certain types of data and automatically

remove them, but other bad data may remain.

17. Observe the progress of the model ID until models of sufficient quality are obtained.

18. Move on to the next sequential MV or to the next group of MVs to be stepped.

19. Repeat 12-18 until acceptable sub models are obtained for all the CVs and MVs.

20. Repeat 12-18 until acceptable sub models are obtained for all the CVs and DVs.

21. Validate all models using engineering knowledge and judgment. The majority of this exercise is done during the

stepping procedure.

22. Build the MPC application and run in prediction mode for the final validation of model quality.


process CV.

24. Massage data or re-step the process until the predictions follow the process closely enough for online control. This

requirement is significantly reduced.



process is required again. This requirement is significantly reduced.


another.

27. Evaluate the performance of the optimizer and further massage models or step the process as necessary. This


Figure 19 shows the automated stepping software. The red square highlights where the engineer enters the size of steps and settling

time for each MV to be stepped. The red circles highlight where the engineer specifies how often the ID algorithm runs (right most), the

button to start the data collection (left most), and the button to start the stepping (middle).

Figure 19. Automated stepping software


Figure 20 illustrates the allowable step patterns as either sequential or simultaneous. There are no restrictions to how many MVs can

be specified. For sequential stepping, the engineer tells the software when an MV is done and it automatically moves onto the next one.

For simultaneous stepping, the engineer specifies all the MVs or a group of MVs to be stepped. When a group of MVs is done, the user

turns those off and configures the next group.

Figure 20. Automated stepping software allowable step patterns

In Figure 21, the configuration page for specifying the size of steps and settling time for each MV (top) is shown. This is where

sequential stepping of MVs (not shown) or simultaneous stepping is specified. All MVs can be stepped simultaneously or in specified

groups. Also illustrated is the configuration page for specifying which sub models are to be identified (bottom). Here the intended matrix

of models for the MPC is specified. The ID algorithm only attempts to obtain models for sub models not labeled as NULL.

Figure 21. Configuring the stepping software


Figure 22. Collect data, step the process and monitor ID results

Figure 22 illustrates the data collection and model ID interface (top) and the model ID progress (bottom). Once the data collector is

started, process data is automatically collected and inserted into the model ID software. The historical data is shown to the left of the

horizontal line in the top chart. The future step patterns are shown to the right of the horizontal line. The horizontal line shifts to the right

each time the stepping software executes. Every time the model ID algorithm executes, the results and model quality data are updated

in the Model Highlights page of the stepping software, illustrated in the bottom chart. The engineer is provided with a model rank (1-5),

the gain, the settling time and the deadtime of each sub model. The rank is color coded to display good quality models (green), medium

quality models (yellow), and poor quality models (red).

Model prediction for all CVs (top) and the lock model function (bottom) are shown in Figure 23. Each time the model ID algorithm

executes, the current models are stored in the model ID software. The engineer can then generate plots of how well the current models

predict the process movement for each CV, as illustrated in the top chart. The green curves represent the process movement and the

red the prediction based on the current models.

Figure 23. Evaluate results and lock models


As can be seen, the plots for most of the CVs have red curves superimposed on the green curves. This is an indication to the user that

the models for those CVs are high quality and there is no need to identify better models. In two of the plots, there is a distinguishable

difference between the red and green, indicating the need for further stepping to acquire better models. In the bottom chart a dialogue

box has popped up on the Model Highlight page. This alerts the engineer to all the sub models that have a high rank and have been

essentially the same for the past few ID executions. It recommends that these can be locked.

Using the plots in the top chart, the engineer can assess if the recommendation to lock the model is valid. As illustrated on the Model

Highlight page, the user can select the sub models to be locked. The model ID software will no longer attempt to get better models for

these and will use them for predictions on subsequent model ID executions in the event that not all sub models are locked for a CV. If

all models for a CV are locked, the ID algorithm will stop attempting to ID better models. The engineer can lock and unlock models at

anytime.

Figure 24 shows the display for altering the MV stepping configuration (top) and when stepping is complete (bottom). Once all the

models for an MV being stepped are locked, it is no longer necessary to step that MV. From the configuration page of the stepping

software, the engineer has multiple options for how to treat an MV being stepped. They can hold or resume the MV, or step the MV up

or down at will. Here the engineer can make compensating moves to any MV to address CV constraint violations, relieving the operator

from the task. The engineer can also put an MV on hold indefinitely or for a period. If all sub models for that MV are locked, there is no

reason to continue stepping it. If the engineer deems there is too much interference between MVs being stepped simultaneously, one or

more MVs can be put on hold to resume later. In the bottom chart, the engineer has assessed that all models are good and has locked

them. The testing procedure is then stopped and the engineer can move onto final model validation and commissioning.

Figure 24. Changing the MVs being stepped and stopping when done

Since this method utilizes both sequential and simultaneous step patterns, all the benefits described in the Open Loop Sequential and

Open Loop Simultaneous sections are realized. These include reduced sensitivity to UMD, drift, and correlated compensating moves,

and elimination of the need to make excessively large moves. An additional advantage with this method is that it essentially removes

the need for the operator to make compensating moves to keep CVs from violating constraints. The engineer can make compensating

changes as necessary directly from the stepping software as illustrated in the top display of Figure 24.

The creation of software fully automating the step testing tasks of configuring step patterns, data collection, stepping the process,

model identification, and model validation radically changed the way step testing was implemented:


The ability to observe ID results on the fly, along with the ease of changing step sizes, allowed the engineer to moderate the step size

early to ensure quality data was generated.

Users could filter and moderate the amount of filtering in the event that the steps had to be excessively large.

Certain types of bad data were detected and automatically removed, reducing the need for the engineer to track and remove them.

The development of more sophisticated ID algorithms reduced the need to massage data to get quality models. If bad data was seen

to be affecting the ID results, the engineer could easily remove it instead of going through an off-line iterative procedure of massaging

the data and running the ID again.

Since the engineer had the ability to pause MVs in a group to validate steps on other MVs, the concern of getting useless data from

stepping multiple MVs at the same time was reduced.

The improved model ID algorithms obtained much better results with multivariable data than previous algorithms.

The need to repeat the step testing procedure was essentially eliminated. The engineer knows when quality models are obtained for

all sub models, and this is the indication that step testing can cease.

The method cut implementation times and reduced the experience required. One major issue remained: Not specifying an appropriate

settling time of the process could still lead to very low quality data that is essentially useless. However, even this was partly addressed

since the engineer can observe the ID results on the fly and change the settling time online. The worst case now was that some step

data was useless and the stepping procedure prolonged.

Overall the time to step a process was cut by another 25%; model validation and correcting erroneous models, after stepping and

model ID were complete, was no longer necessary; and further modifying models during commissioning was cut by an additional 50%.

Projects now typically took 2 months from start to finish for small applications and up to 10 months for larger applications. The tasks of

stepping the process, identifying models, validating models and commissioning were reduced to as little as 40% of the overall project

time.

Closed Loop Automated Stepping with MPC and Auto ID (2005)

The new software to fully automate the open loop step testing procedure was also implemented to allow automated closed loop

stepping. A multitude of implemented MPC applications prompted efforts to make it easier to maintain the applications, especially when

it came to updating models that were no longer predicting the process well. Users wanted to be able to step test without having to turn

off the online running MPC application. Faster computers along with very sophisticated ID algorithms that could effectively deal with

closed loop data meant they could.

All the automated procedures discussed in the open loop section were still available. In addition, the stepping software coordinated step

changes to the MVs with changes required by the MPC application to keep CVs from violating limits. Off-line validation of models was

unnecessary as the engineer could compare the prediction capability of the new models against previous ones.

The general method is similar to that discussed in the previous section with a few differences:

1. Determine the starting size of steps and settling time of the process based on experience from the existing operation

of the MPC application.

2. Configure the stepping software to communicate with the MPC application. This automatically connects the stepping

software to all CVs, MVs and DVs in the MPC application.


4. Specify whether to step a single MV or simultaneous step a group of MVs.

5. Input the starting size of steps and an estimate of the process settling time for each MV to be stepped.


7. Specify the target sub models (CV-MV and CV-DV) that needed to be identified for the MV or group of MVs being

stepped. Unlike open loop stepping where all sub models need to be identified, for closed-loop stepping some sub

models may still be of sufficient quality, so only a portion may need to be identified (the target sub models).



9. Enable the stepping software to begin making steps. The stepping software automatically designs the step patterns

for a single MV or designs the uncorrelated step patterns for a group of MVs to be stepped.

10. Once stepping commences, the operator still has overall responsible for ensuring CVs do not violate key operational

constraints. However, since the MPC application is running online, it makes changes as necessary to address CV

violations. If deemed necessary, the engineer can still make compensating steps to any MV at any time and in any

direction to help the MPC application address CV constraint violations.



the process movement and utilizing model quality information reported back for each sub model by the ID algorithm.

13. Massage the data as necessary to remove any remaining bad data and rerun through the ID algorithm or wait for the

next automatic ID execution. The automated data removal algorithm looks for certain types of bad data and

automatically removes them, but other bad data may remain.


15. Move on to the next MV or the next group of MVs to be stepped.




stepping procedure.

19. Update the MPC application with new models.

20. Re-commission the MPC application with new models. This is straight forward as the engineer has previous models

to compare against.

The procedure to connect the stepping software to an MPC application is shown in Figure 25. The engineer first enters the name and

description of the stepping application to be built (upper most red circle); then browses for an existing running MPC application (middle

red circle); and selects the target MPC application (bottom red circle). The stepping application is now built and ready for configuration.

Figure 25. Connecting stepping software to an MPC application

When using the step testing software for open loop stepping, the engineer needs to directly connect each CV, MV and DV to its

respective process variable. With closed loop stepping, this is no longer necessary. Instead, the engineer connects the stepping


software directly to the MPC application that requires model maintenance, and uses the connections to the process already established

by the MPC application. From this point the engineer needs to configure the stepping software as illustrated in Figure 21, and follow the

rest of the procedure for open loop stepping.

Figure 26. The stepping application and MPC application are connected

Figure 26 shows the user interface for the MPC application (top), and the user interface for the stepping application (bottom). When

stepping an MPC application, a Stepping button appears on the MPC application user interface. This button allows the operator to

pause stepping if, for example, it is causing problems with controlling the process. Once the problems are addressed, the operator

pushes the button again to resume stepping.

Figure 27. Moderating interaction between steps and MPC moves


Figure 27 illustrates the interactions between the stepping application making steps, and the MPC application making compensating

moves. Under normal operating conditions, the MPC application is configured to push the process aggressively towards its optimal

operating state. In the figure, under the Optimizer Normal window, the steps to MV1 and MV2 look more like pulses than conventional

steps. This is due to the optimizer making compensating changes to MV 1 and MV2 to push CV1 and CV2 back to their optimal

operating states (165 low limit for CV1 and 9.5 high limit for CV2). The resulting data is low quality and little use for obtaining new

models as it resembles short settling time step test patterns discussed above.

To rectify this problem, it is necessary to moderate the aggressiveness of the optimizer to yield better quality step test data and more

accurate models. The Optimizer Slowed data shows the optimizer is less aggressive. CV1 and CV2 stay close to their optimal limits but

are allowed to drift away further than under Optimizer Normal operation. The steps for MV1 and MV2 are now elongated pulses, and

are better than before, but still might yield inaccurate models. The Optimizer Slower data shows the optimizer moderated even more.

The MV1 and MV2 moves are further elongated and approaching square. ID results from this data should yield accurate models.

Finally, with the optimizer off and no longer pushing the operation to its optimal operating state, the steps are square and the movement

of the CVs, even though the controller is running, closely resemble that of open loop. The data quality is as good as it gets. The

downside is the cost of operation is higher.

With closed loop stepping, the engineer must decide the proper balance between operating close to optimal conditions and obtaining

high enough quality step test data to generate accurate model ID results. The more aggressively the optimizer is allowed to make

compensating moves to push the operation back to optimal, the longer the step test period will have to be. Moderating the trade-off

simply requires adjusting a single tuning parameter. The engineer can adjust this parameter as necessary to achieve the desired level

of interaction.

The closed loop method built on the benefits of the automated open loop with ID method described above. The need for the operator to

address CV constraint violation is eliminated unless the MPC has to be turned off due to severe process upsets; the option to filter and

moderate the amount of filtering is still available; and so is the ability for automatic and manual engineering removal of bad data on the

fly. Validating how well the new models predicted process behavior compared to the previous models is easily done; and it is no longer

necessary to turn off the MPC controller to re-step the MVs. The interaction between the steps made to the MVs and the MPC

application’s compensation can be moderated to give precedence to obtaining better quality step data.

This method provided additional options for implementing step testing. Revising models that were no longer predicting the process well

could be done without having to conduct open loop steps from scratch. Allowing the MPC application to stay online meant key

economic operating variables could continue to operate close to optimal levels while MVs were being re-stepped. Updating the MPC

with the new models required minimal, if any, effort in terms of retuning the MPC.

The new closed loop method reduced the time to acquire and re-commission new models by up to 75% compared to turning the MPC

application off and re-conducting open loop stepping. It also opened the door for the next big evolution in step testing methodologies.

Enhanced Closed Loop Automated Stepping with MPC and Auto ID (Since 2010)

This next step was to reduce implementation time by extending the capability of the closed loop step testing methodology to open loop

stepping. This would eliminate the need for open loop stepping and model validation, and reduce commissioning time.

The major problem with open loop stepping is that it requires moving the process away from its most economic operating condition and

away from key operating constraints to allow the process to swing, within operating constraints, while steps are implemented. For

example, it involves reducing the charge rate to the process or reducing the load on operating equipment that typically operates close to

full capacity. There are also processes that do not behave well under open loop operation resulting in a reluctance to conduct any form

of open loop step testing on them. Finally, open loop stepping requires close monitoring of the process operation to ensure MV steps do

not lead to unacceptable constraint violations. This is particularly true if ramp CVs are included in the MPC application design, which

typically requires somebody to be present while stepping is taking place. This means either putting in place 24 hour coverage or

stopping the step testing when engineers leave and restarting when they return.


Extending the ability to step the MVs with an MPC application running online to open loop stepping requires a seed MPC application.

There must be at least a minimum set of models to build this seed application. To address any CV constraint violation, every CV must

have at least one seed model with respect to an MV. The seed MPC will initially be configured to just push CVs back within their limits

upon a limit violation. This reduces the need for highly accurate models at the start. It is possible to update gains on any sub model at

any time in the seed MPC as well as download new models from the stepping software into the seed MPC without turning the seed

MPC off. This enables the MPC model structure to be built on the fly as the closed loop step testing method previously described is

worked through.

The general method is similar to that discussed in the previous section with a few differences:

1. Design the basis of the seed MPC by defining the CVs, MVs and DVs from the process.

2. Create an expectation matrix that identifies all the required sub models.

3. For each CV, select one or more sub models to include in the seed MPC matrix.

4. Obtain model parameters for each seed model via historical data or engineering/operator judgment.

5. Build the seed MPC application using the seed models.

6. Implement the seed MPC application online and specify the high and low limits for the CVs and MVs, as well as other

relevant parameters.

7. Validate the seed models by ensuring the gain sign of each model is correct and trends with its respective CV. The

seed models do not have to be highly accurate, just trend in the direction of their respective CVs.

8. Determine the starting size of steps and settling time of the process for each MV.

9. Configure the stepping software to communicate with the seed MPC application. This automatically connects the

stepping software to all CVs, MVs and DVs in the MPC application.


11. Specify whether to step a single MV or a group of MVs simultaneously.

12. Input the starting size of steps and an estimate of the process settling time for each MV to be stepped.


14. Specify all the sub models (CV-MV and CV-DV) to be identified.


16. Enable the stepping software to begin making steps. The stepping software automatically designs the step patterns

for a single MV or designs the uncorrelated step patterns for a group of MVs.

17. Once stepping commences, the operator still has overall responsible for ensuring CVs do not violate key operational

constraints. However, since the seed MPC application is running online, it makes changes as necessary to address

CV violations. If deemed necessary, the engineer can make compensating steps to any MV at any time and in any

direction to help the seed MPC application address CV constraint violations.



the process movement and utilizing model quality information reported back for each sub model by the ID algorithm.

The engineer also evaluates the signal to noise ratio for each sub model to ensure step sizes are large enough and

settling times not too short.

20. Massage the data as necessary to remove remaining bad data and rerun through the ID algorithm or wait for the next

automatic ID execution. The automated data removal algorithm looks for certain types of bad data and automatically

removes them, but others may remain.


22. Download newly obtained models directly to the seed MPC application for better CV constraint management. (This

task is optional.)

23. Once models are downloaded for key CVs, configure the optimizer to run the process closer to optimal operating

conditions. Again, this is optional.


24. Move on to the next MV or group to be stepped.




stepping procedure.

28. Run the seed MPC application in prediction mode for the final validation of model quality.

29. With the seed MPC online, step the MVs one at a time to evaluate the behavior of the predicted CVs against their

respective process CV.

30. Massage data or re-step the process until the predictions follow the process closely enough for online control. This




process is again necessary. This requirement is significantly reduced.


another.

33. Evaluate the performance of the optimizer and further massage data or step the process as necessary. This


Figure 28 illustrates the CV and MV configuration for a seed MPC application. The application is configured with all the CVs and MVs,

along with high and low limits for all variables. The engineer may also need to enter values for tuning and other important parameters.

The operator interfaces with the application in the same way as other applications.

Figure 28. Configuring the seed MPC application

The beginning model matrix for the seed MPC application is shown in Figure 29. It is sparse, with the minimum for a seed MPC

application for most CVs – just one sub model for each CV. The engineer can specify more than one sub model if desired as is done for

CV3 (it has 2). These seed models are obtained from historical data or engineering or operator experience. The models do not have to

be very accurate; as long as the gain sign is correct, the seed MPC will make proper compensating moves to address CV constrain

violations. If the compensating moves fail to properly address a CV constraint violation, the engineer can increase the gain multiplier to


slow down the response or reduce the gain multiplier to accelerate the response. The idea is not to accurately control the CVs, merely

to keep them between the limits.

Figure 29. Specifying seed models for CV/MV pairs

Once the seed MPC application is configured and commissioned, the engineer can build the stepping application as illustrated in Figure

25. From this point the engineer needs to configure the stepping software as illustrated in Figure 21, and follow the rest of the

procedure for open loop stepping. As Figure 26 shows, the seed MPC application is connected to the stepping application and the

operator can pause and resume stepping from the seed MPC user interface as necessary. The optimizer of the seed MPC should be

turned off to begin with.

The stepping results should be similar to the Optimizer Off data from Figure 27. The engineer should utilize the calculated signal to

noise ratio for each sub model to ensure the MV step sizes are large enough. The engineer should also verify that the settling times

specified for the MVs are not too short.

Once the stepping procedure begins to produce quality models the engineer can verify the model prediction and then lock the models

as illustrated in Figure 23. The engineer can then begin to download models into the online running seed MPC application (Figure 30).

As shown, the engineer can select all models or a portion of the models to be downloaded (blue background). The engineer pauses the

stepper, downloads the models and then resumes stepping. The stepping software combines the selected models with the models

already in the seed MPC, builds the new model matrix, and notifies the seed MPC to read in the new set of models. No visit by the

engineer to the off-line software is necessary. The seed MPC reads in the new model matrix, performs bump-less initialization and

begins using the new matrix as shown in Figure 31


Figure 30. Downloading new models from stepping software to the seed MPC application

Figure 31. Updated seed MPC model matrix

The seed MPC matrix at the beginning of stepping (top) and after a new set of models is downloaded (bottom) can be seen. The bottom

matrix is much denser. At this point, the engineer has the option to configure the optimizer to maintain the process closer to ideal

economic operating conditions as the remainder of the stepping is conducted. The engineer must maintain an appropriate balance in

the interaction between stepping and the optimizer making compensating moves, as illustrated in Figure 27. The engineer continues to

monitor for lockable quality models and download them until all sub models in the seed MPC application are updated. At this point, the

stepping procedure is complete.


This method provides an alternative to open loop step testing. By building a seed MPC application up front, the engineer can realize all

the benefits of running the stepping software with an MPC application online to address any CV constraint violations. The ability to

download newly acquired models into the seed MPC application, without having to turn it off, enables model validation to be

incorporated into the step testing procedure. Once all the models for a CV are obtained and downloaded, it is possible to observe in

part how well the seed MPC application can handle CV constraint violations during the step testing procedure, reducing the need to do

this after commissioning commences. As key models in the seed MPC application are obtained, the engineer can configure the seed

MPC to operate closer to ideal economic conditions and key operating constraints while the remainder of the step testing is conducted.

The engineer can then moderate how aggressively the seed MPC pushes toward ideal economic conditions to ensure quality step test

data for the remaining models can still to be obtained. The engineer also has a measure of the signal to noise ratio for each sub model

to provide early confirmation on the sufficiency of the MV move sizes.

The new method has reduced the duration of MPC projects even further. Time taken to step a process is cut by another 25%. Model

validation and correcting erroneous models are incorporated into the stepping procedure, as is preliminary validation of how well the

models address CV constraints. This cuts commissioning time by an additional 25%. Projects now typically take under 2 months from

start to finish for small applications and up to 8 months for larger applications. Stepping the process, identifying models, validating

models and commissioning are reduced to as little as 30% of the overall project time.

Recent Enhancements (Since 2010)

Work to reduce implementation times and the skill required for step testing continues today, with a number of the improvements being

developed:

Technology to assist the engineer in obtaining seed models from historical data.

Combined manual and automated stepping (allows simultaneous stepping of fast responding or well behaved processes manually)

Techniques to improve automated bad data removal

Techniques to improved model quality assessment

Techniques to estimate an appropriate starting step size and settling time

Techniques to alert the engineer earlier that step sizes are too small for getting good quality data.

It is not yet clear how much more can be cut from the time required to implement MPC applications or what is the point of diminishing

returns. However, progress here and in efforts to reduce the skill level required to implement step testing continues to be made.

Summary

MPC technologies were introduced in the 1970s. Instead of deriving linear ODEs mathematically to predict the process behavior, it was

discovered that mathematical algorithms could approximate the models empirically. Empirical model generation, however, required

process data to correlate the movements in MVs and DVs to CVs. Methods for bumping or stepping the process were thus introduced.

This started with manually stepping MVs and DVs. A key problem was correlated moves between the MV being stepped and

compensating moves on other MVs. This was driven by the need to manually address limit violations of key operating variables. The

correlated moves were inadvertent, but common due to human nature and the desire to avoid upsetting the process operation too

much. Another key issue was the tendency to make a step change and hold it until the process settled out. This allowed unmeasured

disturbances and drift in the process to corrupt the step test data. The final problem was that step changes were much larger than the

changes the MPC application made to control the process. The MPC application typically made small, smooth changes over time, but

to generate enough movement for the ID algorithm to get models manual stepping made large changes. This tended to throw the

process out of equilibrium much more than the MPC application moves and rendered the models inadequate for prediction. The result

was prolonged step testing periods, a lot of time spent massaging data and re-running the ID algorithm, or repeated stepping. All these

combined to elongate project times.

The 1990s introduced sequential automated, rather than manual, step patterns for each MV. This addressed the issues of holding steps

until the process settled out by creating step patterns that had steps of varying length, reducing the impact of unmeasured disturbances


and process drift on data quality. These methods also reduced the incidence of correlated moves rendering data useless and issues

resulting from making steps too large, through filtering of conventional step patterns or deploying a sinusoidal based pattern. However,

automated step patterns made it more difficult to decipher whether quality data was produced so erring on the side of large steps was

usual. New issues also arose, with engineers tending to underestimate process settling times to speed up step testing. This led to

models with erroneous gains and settling times. Shorter switching intervals also made it difficult if not impossible to massage the data

during the off-line ID procedure.

The next methods, introduced around 2000, consisted of deploying simultaneous automated, rather than sequential, step patterns. This

essentially eliminated the impact of unmeasured disturbances and drift as well as correlated moves due to human intervention. Some

steps that were too large still occured, but it was much less of an issue. Moving multiple MVs simultaneously created a built in filtering

affect allowing larger steps without significantly throwing the process out of equilibrium. The result was richer data sets to run the ID

algorithm against and models that gave good predictions, even allowing for the smaller, smoother changes deployed by the MPC

application. Moreover, the ability to filter in steps was still available if very large steps had to be made. However, the issue of providing

a representative settling time became crucial to avoid low quality or useless data. Massaging data off-line during the model ID

procedure was also essentially impossible, so re-stepping was necessary if ID results were unfavorable.

From around 2005 stepping software could combine stepping the process, collecting data, identifying models and validating models into

a single procedure. Both sequential stepping and simultaneous stepping were supported. The need to collect data and transport it to an

off-line software package to run the ID algorithm was eliminated. Automatic data massaging algorithms were implemented to look for

certain types of bad data and remove them, and the engineer could also remove bad data on the fly. The ID algorithm automatically

estimated the quality of models being identified and reported it back to the engineer. Correlated moves were also all but eliminated as

the engineer could make compensating changes to MVs as necessary from the stepping software. Likewise, steps that were too large

were avoided since the engineer could observe ID results on the fly and make adjustments to step size as appropriate. The option to

filter steps is still available but used less often. There were also improvements in the ability to assess data quality when stepping MVs

simultaneously as the engineer could stop stepping MVs to isolate a single MV or group, and then resume them. The ability to view

periodic ID results allowed the engineer to make adjustments early to get better quality data, and to perform the model validation on the

fly. Finally, the engineer no longer had to guess when to stop testing as the procedure indicates when quality models are obtained and

stepping can cease.

These and other advances reduced times and experience levels required, with the software’s built in intelligence helping guide the

engineer. The biggest issues remaining were the risk of not specifying a long enough settling time or specifying steps sizes that were

too small. However, both could be changed online. At worst stepping was prolonged.

Another method introduced in 2005 was to use automated stepping software in conjunction with an online MPC application to perform

closed loop stepping. Stepping software that connected easily to an existing MPC application was developed so re-stepping of MVs

could take place to repair errant sub models while the MPC application was running. This extension to the stepping software allowed

new models to be acquired without having to turn the MPC application off and conduct open loop stepping.

In addition to the benefits delivered for open loop stepping, the stepping software coordinated step changes to the MVs with changes

required by the MPC application to keep CVs from violating limits. This eliminated the need for human intervention unless a severe

process upset occurred. The engineer could also moderate the interaction between the steps being made to the MVs and the MPC

application making countering moves to prioritize obtaining better quality step data. Overall the new closed loop method reduced the

time to acquire and re-commission new models by up to 75% compared with turning the MPC application off and re-conducting open

loop stepping. This new method also opened the door to the next big evolution in step testing methodologies.

These methods, introduced around 2010, extended the capability of closed loop step testing to open loop stepping by using a seed

MPC application. The engineer first defines the application’s CVs, MVs and DVs and adds seed models for key CV/MV pairs;

implements the seed MPC application online and conducts simple model validation; and then connects the stepping software to the

seed MPC application and follows the rest of the closed loop procedure.


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In addition to the benefits of closed loop stepping, the engineer now has the ability to change seed model gains on the fly if the seed

MPC is not addressing CV constraint violations appropriately. The engineer can also download newly acquired models from the

stepping software into the seed MPC application without turning the application off. As new models are downloaded to the seed MPC

application, the engineer can begin to observe how well the seed MPC application handles CV constraint violations, reducing the need

to do this after commissioning commences. As key models in the seed MPC application are obtained, the engineer can configure the

seed MPC to operate closer to ideal economic conditions and key operating constraints while the remainder of the step testing is

conducted. This enables engineers to build the MPC model structure as they go, extending the closed loop step testing method

previously described.

The following table summarizes the project timeline for each method.

Further enhancements are still being made to reduce project times and reduce skill levels required. Progress is also being made to

lessen the impact step testing has on the normal process operation and to reduce errant practices that creep into step testing

procedures. The final line on the evolution of step testing has yet to be written.

Author

John A. Escarcega

Business Consultant for APC and Optimization

Honeywell Process Solutions

WP-14-13-ENG

June 2014

© 2014 Honeywell International Inc.

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