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Maintaining Your Advanced Control Applications: Industrial Experiences and Best Practices

Executive Summary

Model Predictive Control (MPC) has become the de facto standard for implementing advanced control strategies.

Ensuring that the implemented base of MPC applications continue to perform well and deliver expected benefits requires

a combination of (a) appropriate monitoring technology and (b) best practices for maintaining the advanced control

applications. In this paper we will review some of the key issues in monitoring and maintaining advanced control

applications in an industrial setting

.

Maintaining Your Advanced Control Applications: Industrial Experiences and Best Practices 2

Table of Contents

Executive Summary ................................................................................................................................................................... 1

Introduction ................................................................................................................................................................................ 4

APC Impact ................................................................................................................................................................................. 5

Performance Monitoring Best Practices ................................................................................................................................. 6

Monitoring Your APC Applications .......................................................................................................................................... 8

Performance Monitoring Metrics .............................................................................................................................................. 8

Performance Monitoring – Roles and Responsibilities ......................................................................................................... 11

Conclusions .............................................................................................................................................................................. 13

References ................................................................................................................................................................................ 14


Table of Figures

Figure 1: Illustration of Multivariable Constrained Control for a 2 CV x 2 MV Process .................................................... 4

Table 1: Savings in Production Costs from Categories of Process Control (Brisk , 2004) ............................................. 4

Figure 3: Benefit Through Variability Reduction and Changing Operating Point ............................................................. 5

Figure 2: A representative Schematic of the Different Steps in the Monitoring, Diagnostics and

Remediation of an APC Application ...................................................................................................................... 8

Table 2: Typical Root Causes and Remedial Actions ........................................................................................................ 7

Figure 4: Model Gain Matrix for 40 MV x 94 CV x 3FF Crude Column ................................................................................ 10

Table 3: Recommended Targets for Performance Metrics as a Function of Time........................................................... 11

Table 4: Responsibility Matrix for APC Team Members

(R- responsible, A- Accountable, I – Informative, C- Consultative) .................................................................... 12

Table 5: Performance Monitoring Timescales for Different Roles .................................................................................... 13


Introduction

Since their inception in early 1980s, multivariable model based controllers have become the technology of choice for implementing

advanced process control (APC) applications. More than 5,000 applications exist, worldwide (Qin and Badgewell, 2003) and this

number continues to increase. There are a variety of commercial technologies available that are capable of implementing model based

predictive control. The common theme between the different technologies is the presence of a model of the process. The model lies at

the heart of the controller and has a great deal of influence on the performance of the controller.

Table 1: Savings in production costs from categories of process control (Brisk , 2004)

The benefits associated with advanced control are often derived by (a) reducing variability in key process variables through

multivariable model based control and (b) subsequently operating at the most profitable constraints through a constrained optimization

scheme that is often a part of the APC application. Figure 1 below illustrates this idea.

Figure 1: Illustration of Multivariable Constrained Control for a 2 CV x 2 MV process


As illustrated by Figure 1, once the application is commissioned, various reasons may cause the APC application to move away from

the economic optimum. This results in deterioration of the benefits from the advanced controller. Based on the authors’ experience in

commissioning and supporting a large number of APC applications over the last 20 years, some of the main reasons are:

lack of APC resources

lack of operator training

changes in Process Dynamics

inability to quantify APC performance

inability to link APC benefits to process performance

lack of APC best practices

inability to visualize model quality

inability to visualize and assess performance of the supporting cast – PID controllers/valves/inferentials

inability to rationalize and explain APC behavior in real time

lack of management support

The purpose of this article is to give the readers a broad overview of the most commonly encountered issues and best practices when

trying to sustain the performance of their APC asset base. Examples are used wherever possible to illustrate the issues.

APC Impact

Implementation of an APC application in an operating facility has an immediate impact in a number of ways.

Figure 3: Benefit through variability reduction and changing operating point.

Figure 3 illustrates that APC reduces variability in key operating parameters by moving that variability to available manipulated

variables. This reduction in variability may, in and of itself be valuable, but by reducing variability an opportunity is created to shift the

operating point of the process closer to limits and constraints. This shift in operating point is normally the key contributor to value

delivered by APC applications, as illustrated in Figure 1. A recent survey by Bauer and Craig (2008) of industrial practitioners and

vendors indicated that the economic assessment of advanced control applications is still in an ad-hoc fashion.

Moving and holding to this new operating point changes the performance requirements of many other aspects of the business. If these

changes in performance are not achieved, the benefit delivered by the APC application will be less than its potential. These elements

include:

Instrumentation: Faulty instruments will increase instability, or cause part or all of the APC application to be turned off, or

otherwise reduce the value delivered by the application. To ensure the application delivers full value, identification of

instrumentation issues needs to be improved, and prioritization of repair efforts needs to be modified to account for this impact.

Computer Systems: Issues with computer hardware, software, or network will prevent communication between the APC

application and the process. This will directly reduce the application uptime. Adding APC and control monitoring systems also

increases the number of systems that need to be maintained. It may also increase the complexity of the system maintenance

problem.


Process: Many aspects of the process will reduce the application effectiveness. Some of these include: unmeasured or

infrequently measured limits, unmeasured disturbances, process behavior changes (such as heat exchanger fouling, or catalyst

decay), and process modifications. To enable an application to continue to deliver high value, effort is required to identify when

aspects of the process are limiting the value delivered and either adapt the application or repair the process. Changes in process

economics may invalidate some of the application’s control objectives.

Control System: The control system itself can be the cause of poor control performance. As constraints become active for the first

time, because the process has moved to a new operating region, poor quality models that were once unimportant become

important. Also, improvements in the systems fault tolerance are required in order to enable the application to run when other

problems (instrument, process, computer, or other) exist.

Staff: The skills, knowledge, and capabilities of staff assigned to support APC applications have a large impact on the success of

the applications. Having work processes and tools in place to better support that staff is important to allow for succession and

reducing the threshold for new people to take on the job. Satisfying these more stringent requirements is difficult. Having tools that

automatically generate performance statistics will allow properly trained staff to use good work practices and achieve high

application performance.

Satisfying these more stringent requirements is difficult. Having tools that automatically generate performance statistics will allow

properly trained staff to use good work practices and achieve high application performance.

Performance Monitoring Best Practices

The precise activities, and the people performing those activities involved in monitoring and maintaining an APC application depends

upon the organizations particular requirements. These requirements include:

desired core competencies

value delivered by applications

number of applications needing support

Where an organizations requirements lead to having a control engineer performing the monitoring and maintenance tasks, tactical high

value job activities might include:

daily monitoring of application performance and issue identification

regularly challenge the application limits, seeking ways to allow it to do more.

perform regular maintenance on the application to improve fault tolerance and control performance

Strategic high value job activities would include:

Document procedures and activities to better


Table 2 shows APC application performance categories, and adaptive or improvement action that should be taken.

Enable job succession

Support regulatory control

Examine and challenge performance benchmarks and target operating points

Table 2. Typical Root Causes and Remedial Actions


Monitoring Your APC Applications

There are four different aspects of analyzing a multivariable constrained control scheme that is model-based in nature:

1. Utilization: This is the first level of analysis. If the controller is not being used, there is likely a fundamental problem. Similar

comments apply to the utilization of individual MV/CV variables. If critical variables are being turned off it affects the ability of the

controller to deliver performance or benefits

2. Performance: A controller may be ON, yet performing poorly. In such cases one has to analyze the performance of the various

controller variables (CVs), constraint variables, etc. A MV may be ON but at its limits which means it is not being effectively utilized.

3. Diagnostics: If performance is not meeting the expectations, there could be several reasons – model plant mismatch, improper

tuning, instrumentation issues, etc. If crucial CVs are being under or over predicted, the responsiveness of the application to address

any disturbances or target changes is going to be affected.

4. Remediation: Having understood the reasons for unsatisfactory performance, one often needs to prioritize and make those changes

to the controller/process that will result in the improvement of performance without excessive cost or effort.

Figure 2 shows a representative schematic of the typical workflow involving the assess, diagnose and remedy steps for any

Figure 2. A representative schematic of the different steps in the monitoring, diagnostics and remediation of an APC application.

Measurement of APC performance is a non-trivial problem. In the following section we will talkabout the specific metrics that help

assess whether a controller is delivering satisfactory performance.

Performance Monitoring Metrics

Measurement of APC performance is a non-trivial problem. Three key aspects of APC performance are reviewed below – (1) variability

(2) constraint performance and (3) model performance.

Variability is an important measure as that is the basis on which APC applications are implemented. It is important to ensure variability

is being reduced by the multivariable controller up to the level anticipated as per initial estimates. A multivariable controller is able to

derive benefits by operating the unit against the right constraints. Therefore it is important to monitor the active constraints and ensure

there are sufficient degrees of freedom for the controller. Lastly the model is at the heart of the predictive controller and is the single

most important tuning parameter. Tracking model performance and ensuring that it is satisfactory ultimately ensures good health of

the controller.


Variability: A commonly used approach to establishing variability measurement is relative a to a historical baseline data set. Prior to

commissioning, a historical baseline is selected. Typical estimated reduction in process variability due to multivariable control is

estimated in the neighbourhood of 50%. Post commissioning the variability (i.e. standard deviation of key controlled Degradation of

Control Performance variables) is compared to the baseline and the improvements noted. The post-commissioning variability then

becomes effectively the new benchmark for further comparison. The expectation from any application is that it achieve at least the post-

commissioning variability. Seasonal changes account for long term patterns in underlying variability. For example, summer conditions

may often mean a distillation column gets cooling limited and the variability is higher. In technical terms there are two benchmarks that

are considered here (1) Open Loop variability – prior to controller commissioning (2) Closed Loop variability – post controller

commissioning.

A shortcoming of using absolute variability as a benchmark is its dependence on underlying process disturbances. Variability can be

compared for APC applications at two points in time in a fair manner under the assumption that nothing else has changed – the

disturbance magnitude and dynamics are the same. This can lead to both false positives and false negatives. Variability may decrease

as a result of the overall process being more stable or variability may increase on account of process changes being made elsewhere.

A more accurate representation of closed loop performance is based on closed loop performance requirements – such as settling time,

rise time, overshoot, IAE, ISE etc. These measures are invariant to the scale of the disturbance and are thus normalized. Some

commercially available APC technologies directly incorporate such a measure into their controller tuning stage.

Desired variability could be established through historical benchmarking (post-commissioning) or design considerations (closed-loop

response requirements) as discussed above. Relative variability can be calculated at individual CV level and aggregated to the

controller level.

Constraint Performance: An operator or engineer interacting with a APC application would like to know if (1) the right constraints are

being targeted, (2) key constraints are not being compromised, (3) there are adequate degrees of freedom for (1),(2) to be achieved.

Quite often one comes across an APC application that is being utilized i.e. it is ON but the limits on critical manipulated variables (MVs)

have been tightened. This leads to a situation where the controller is effectively in open due to a lack of degrees of freedom. The

reason for tightening of the MV limits may be due to a lack of performance and/or a lack of training. In either case, the real measure of

controllability of any application is an effective utilization rather than merely having individual variables or the application being utilized

(ON). We propose a measure, effective utilization, that measures the relative degrees of freedom available to a multivariable controller,

This measure can be calculated over any time scale and captures the degrees of freedom or manipulated variables available to the

controller. It can be calculated at the variable level and aggregated to the controller level using a weighting scheme. Availability of the

process unit should be considered while computing this measure.


Model Performance: The key questions a practitioner is interested in answering are (1) which models are at fault (2) what is the impact

of model error on performance and (3) what model parameters does one need to update – gain, time constant or time delay in order to

recover performance. In the ideal case, one would like to answer these questions based on operating data without having to perform

intrusive testing. Commonly used measures of model quality are based on prediction errors and a visual comparison of the

measurements with unbiased predictions (or simulation).

A disturbance model other than the integrated white noise model is rarely used in commercial APC technologies and thus one is

focused mainly on comparing the error in the deterministic portion of the model. An example that vividly the scale and complexity of the

problem is shown in the form of a 40 MV x 94 CV x 3 FF (feed forward) variable model matrix from a crude column APC application

below. The color coding indicates positive (red) and negative gain (blue) models.

Figure 4. Model Gain Matrix for 40 MV x 94 CV x 3FF crude column

An operator or APC engineer who is interacting with this application on a daily basis has to solve the puzzle of which (MV-CV) model(s)

are causing a particular behavior – particular constraint tradeoffs or variability increases - and the appropriate actions that are needed

to rectify that model behavior.

As a start one, one can measure the relative variability of the model prediction compared to the CV measurements.

where the prediction is the raw or unbiased prediction for each CV. This allows tracking of the process predictability in relative terms

and flag significant deviations from benchmark performance. A good benchmark is obtained from the post-commissioning data where

the model performance is considered to be satisfactory.

Table 3 proposes some guidelines for the key metrics for any APC application. The metrics presented here in are at the overall

application level and related to individual CV/MV performance metrics through a weighted average based on the relative importance of

each CV/MV. Variability is relative to design performance or desired variability. Model performance is relative to benchmarked model

performance.


Table 3. Recommended Targets for Performance Metrics as a function of time.

These targets take into account that there will be short term variations in the different performance metrics on account of operating

point changes/disturbances. It is essential that these goals are agree upon by the ownership team for the APC applications.

Performance Monitoring – Roles and Responsibilities

An APC application is often commissioned by a team involving the vendor and the ownership team comprising of process control

engineers, operators, managers. The process control engineer with appropriate APC training is the eventual owner of the application,

responsible for maintaining and troubleshooting the controller as necessary. The operator uses the application on shift basis to ensure

that the process is operating at the right state, it is stable and the key constraints are being met. The process control engineer gets

involved with application on a daily basis to ensure any issues raised by the operators are addressed, known issues with the application

are addressed and the controller is being run in recommended fashion by the operators. The technical/process control/plant managers

are often concerned with adequate uptime of the application given the level of effort/resources that have been invested in

commissioning the application. If it is a well running application, the operators/shift supervisors/plant managers will treat the APC just

like any other plant asset – it is essential to the smooth operation of the facility and needs to be running well.

Based on the profiles of the ownership team described above, the kind of information each member would like to know is described

below. Note that the composition and roles of APC ownership discussed here are representative, each organization will have its own

set of roles and responsibilities based on resource availability and organizational structure.

Operator: An operator wants to know from a real time interface, what the controller is doing at any given instant and why it is doing so.

In more technical terms the knowledge of (1) active constraints and (2) the cause-effect behavior between manipulated variables and

controlled variables that cause constraints to be active or given up on.

Shift Supervisor: A shift supervisor is concerned if the operators are having to pay too much attention to one or more controllers and

they are having to take the application off. In other words (1) the level of operator interactions (2) process stability (3) controller uptime

is of concern to this group.

Plant Manager: The plant manager has a reason to be concerned if (1) the controller uptime is not satisfactory and (2) process stability

is not being achieved by the controller.

Production Engineer: The responsibility of the production engineer is to achieve the production targets that have been set by the

planning/scheduling team and the plant manager. Safety is a key priority for this individual as well. They tend to be concerned with the

APC application if it is engaging in behavior that for example (1) cuts feed rates and compromises the production targets or (2) is

causing off-spec incidents resulting in emissions or off-spec product. This information is usually provided to them by either the

operators or shift supervisors.


Process Engineer: A process engineer is involved in the design phase of the application to ensure that the key variables are being

included and the controller targets the “optimal” constraints. At some sites a real time optimizer layer may sit above the APC layer

sending down targets/setpoints. The process engineer wants to ensure that these target changes which may be sent down by the RTO

every few hours are being achieved by the APC application. The process engineer may also get involved in the troubleshooting if the

underlying process behavior is causing a problem with the application.

Technical/Process Control Manager: This group is concerned with (1) application uptime and (2) benefits. Issues requiring additional

resources will be brought to their attention by one of the other groups as necessary. For example if the process has undergone some

changes recently and a full step test is required to update the plant models, this group will be involved in justifying the resource

allocation.

Process Control Engineer: Any issues observed by the rest of team are important to the Process Control Engineer. In addition to (1)

controller uptime (2) active constraints (3) process stability, the process control engineer is also the one who is concerned about the

“why” part. This means that things like process variability relative to a benchmark, cycling in the controller, degrees of freedom

available, constraint give ups, underlying PID controller performance, inferential/analyzer performance model performance, operator

interactions, tuning problems, and process nonlinearities that impact application behavior all become important as this individual has

the responsibility for addressing these issues.

Instrument & Electrical Technician: The person will only get involved if an issue raised by one of the above people needs to be

addressed in order to recover APC performance. An example is a sticky valve or a malfunctioning sensor or analyzer. As such this

group needs information on the diagnostics pertaining to the instruments/valves that have been flagged. Table 4 shows a

representative responsibility matrix for the different aspects of an APC application. In this article we have reviewed a few key metrics

pertaining to the utilization, performance and diagnostics. Table 5 outlines the typical timescales for monitoring different facets of the

APC application as per roles above.

Table 4. Responsibility Matrix for APC Team Members (R- responsible, A- Accountable, I – Informative, C- Consultative)


Table 5. Performance Monitoring Timescales for Different Roles

Conclusions

Maintaining high performance multivariable control applications is a challenging problem that involves different groups of individuals

and work processes. This paper describes the problem in an industrial context and presents some best practices for monitoring

advanced control applications. Model Predictive Controllers have made a significant impact in the process industry and led to

substantial benefits in improved operability of individual process units. Reduction in energy usage, increased throughput, improved

quality lead to substantial monetary benefits that are of the order of 1.4% of operating costs. However, the initial process improvements

and benefits may not be sustained unless appropriate steps are taken to monitor and continuously adapt APC performance to changing

conditions. The appropriate steps involve a combination of the right metrics and work processes being put in place to detect, diagnose

and remedy any significant performance degradation of the APC application in real time.


For more information:

For more information about Control

Performance Monitor, visit our website

www.honeywell.com/ps or contact your

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www.honeywell.com/ps

References

[1] M L. Brisk, “Process Control: Potential Benefits and Wasted Opportunities” in 5th Asian Control Conference. vol. 1, 2004, pp.20-23

[2] S. Qin and T. Badgewell, “An Overview of Industrial Model Predictive Control Technology”. Control Engineering Practice, Vol 11, a

2003, pp. 733-764.

[3] Celaya, P., Tkatch, B., Zhu, Y. and Patwardhan, R. S , “Closed Loop Identification at the Hovensa Refinery”, NPRA Decision

Support and Automation Conference, 2004, San Antonio.

[4] M. Bauer and T. Craig , “Recent Economic Assessment of Advanced Process Control – A Survey and Framework”. Journal of

Process Control, Vol 18, 2008, pp. 2-18.

WP 737

August 2011

© 2011 Honeywell International Inc.

http://www.matrikon.com/

http://www.honeywell.com/ps

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