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Process Manufacturing

Understanding multivariable control: The missing metric

Advanced process control: An improved understanding of the role of multivariable control in industrial process operations will lead to more cost-effective solutions and engage a wider circle of people in the process automation enterprise.

By Allan Kern April 4, 2020
Courtesy: APC Performance LLC

Multivariable control almost always has been explained in complex terms, invoking concepts such as detailed process models, real-time optimization, and matrix math. This means few peopleoutside the tight circle of advanced process control (APC) engineers, have understood it well. Greater understanding of multivariable control in industrial process operations brings more people into the process automation enterprise. Operational benefits include timeliness, consistency, and fewer alarms. 

Spreading APC knowledge 

Most other stakeholders have been left at least partially in the dark, often having to buy off on APC projects when they may not fully understand the objectives, benefits, implications and odds of success. Moreover, this situation again left industry at the mercy of APC engineers to explain APC’s many unexpected shortfalls such as high cost, short lifecycle, and high maintenance, which in most cases, have not been satisfactorily explained. 

With the benefit of nearly four decades of multivariable control experience, a more qualitative and intuitive understanding of multivariable control and the role it plays in industrial process operation is (finally) emerging. This can have several beneficial impacts for APC and process automation, including simpler and more robust software tools, better defined applications, and greater participation of all stakeholders. 

A traditional constraint diagram (Figure 1) shows the difference between manual and automatic multivariable control. In manual, operators keep a buffer, or margin for error, between ongoing operation and constraint limits, in case there is an unexpected process change or disturbance. The buffer typically translates into an economic penalty relative to fully optimized operation. Courtesy: APC Performance LLC

A traditional constraint diagram (Figure 1) shows the difference between manual and automatic multivariable control. In manual, operators keep a buffer, or margin for error, between ongoing operation and constraint limits, in case there is an unexpected process change or disturbance. The buffer typically translates into an economic penalty relative to fully optimized operation. Courtesy: APC Performance LLC

What is multivariable control? 

Multivariable control can be defined as automation of the single-loop controller setpoint and output adjustments that are otherwise left to the operating team to manually implement. When operators make setpoint and output adjustments in the course of a shift, that’s manual multivariable control. Automatic multivariable control technologies, such as model-predictive control (MPC) or model-less multivariable control (XMC) automate this task. 

Automatic multivariable control — or closing multivariable loops  brings the same benefits as closing single loops including greater timeliness and consistency, fewer alarms and constraint violations, and greater optimization. And it often includes significant operational and/or economic benefits. 

Role of multivariable control in process operations 

The role of multivariable control in industrial process operations can be understood as the difference between automated multivariable control and manual multivariable control. Industry has always had manual multivariable control, because almost every process operation is a multivariable control proposition — just ask any operator. 

Automatic multivariable control automates, or takes over, the task of making setpoint and output adjustments for groups of related controllers. This often results in more consistent and timely adjustments, fewer alarms and constraint violations, and greater optimization. These benefits can also be understood as the intrinsic benefits of closed-loop vs. open-loop control, which have always been well understood in the single-loop control world and apply equally (or geometrically) to multivariable control. 

The traditional constraint diagram (Figure 1) illustrates the difference. With manual multivariable control, operators keep a buffer, or margin for error, between ongoing operation and constraint limits, in case there is an unexpected process change or disturbance. The buffer typically translates into an economic penalty relative to fully optimized operation. 

With automatic or closed-loop multivariable control, operation can be held closer to actual constraints and the buffer region is captured as advanced control benefits. This is possible because multivariable control means an automatic response can be relied upon to take action in the event of changing process conditions. In the same way, multivariable control can automatically pursue receding constraints to capture greater earnings and optimization — it works in both directions. 

Effective metrics provide a meaningful measurement, are intuitive, and reflect progress over time. Has industry been overlooking metrics that could justify automatic multivariable controls? A chart of the 25 worst controllers (those requiring the most operator intervention) is easy to make on any modern control system console. Courtesy: APC Performance LLC

Effective metrics provide a meaningful measurement, are intuitive, and reflect progress over time. Has industry been overlooking metrics that could justify automatic multivariable controls? A chart of the 25 worst controllers (those requiring the most operator intervention) is easy to make on any modern control system console. Courtesy: APC Performance LLC

Where are the applications? 

Many multivariable control applications have remained “below the radar” of the conventional large matrix MPC paradigm, because they have not been seen as justifying the high threshold cost of MPC, and were too big for the limitations of advanced regulatory control (ARC). 

Figure 2 is a “lowaltitude radar” that reveals the multivariable control applications that have historically remained below the MPC radar. It shows the number of operator interventions, in terms of setpoint, output, or mode changes, made at an operator console, over a given time period. It shows the 25 worst actors, which are those controllers that have required the most operator interventions. This is an easy chart to make on any modern control system console. 

Micromanaged APC 

It’s a good bet that many or most of these interventions represent manual multivariable control scenarios, where the operating team becomes caught up in frequent micromanagement of groups of related controllers.  

It is the objective of multivariable control to automate these manual multivariable control scenarios to close these multivariable loops and reduce these numbers. 

Industry’s missing APC metric 

Figure 2 may look familiar. Industry adopted similar best practices at least twice in recent memory  to manage loops in manual (except now we are talking about multivariable loops in manual) and to manage bad actor alarms (except now we are talking about bad actor loops that require frequent operator intervention). 

Multivariable loops in manual and frequent operator interventions carry several undesirable implications, including more alarms and constraint violations, less operator attention to higher level tasks, and less optimization, since manual intervention, by definition, is often inconsistent, untimely, and suboptimal. 

Effective metrics provide a meaningful measurement, are intuitive, and reflect progress over time. Figure 2 meets these criteria and reflects a fundamental aspect of successful process automation and quality of console operation. Has industry been overlooking this natural and potentially important metric? 

Allan Kern, P.E., is owner, APC Performance LLC. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media,mhoske@cfemedia.com.  

KEYWORDSAdvanced process control, APC, multivariable control 

Role of multivariable control in industrial process operations 

Working behind the scene: Multivariable control applications 

Missing metrics: Measurements over time are needed to justify automated multivariable control. 

CONSIDER THIS 

Reader pollDoes your site have a metric for bad actor loops or loop interventions per hour? To respond, put in an email subject line… 

Loop metric YES 

Loop metric NO 

and email that once to mhoske@cfemedia.com before end of April 2020. If you have questions for the author or would like us to consider sharing your original related advice to other readers, please add that text in the body of the email. Results may  appear in a future article. 

ONLINE 

RCEP webcast, Jan. 9, 2020 (posted for 1 year): “Advanced Process Control: Past, present and future 


Allan Kern
Author Bio: Allan Kern, P.E., is owner, APC Performance LLC. He has over 30 years of advanced process control (APC) experience and has authored numerous papers on cost-effective APC solutions. He is the inventor of an inherently adaptive control algorithm and a model-less method of multivariable control. He is a 1981 Chemical Engineering graduate of the University of Wyoming and has professional engineering licenses in Control System Engineering and Chemical Engineering.