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Multivariable Control Performance: A Case for Model-less Control

This excerpt is from the July/August 2014 issue of InTech magazine and was written by Allan Kern,  a process control and automation consultant with more than 35 years of experience in the oil refining, chemical, petrochemical and power generation industries. 

Multivariable control is usually thought of as a product of the computer age, but multivariable control has always been an integral part of industrial process operation. Before the computer era, the operating team did multivariable control manually, by adjusting the available Jul-Aug-INT-Process-Automation-blog1controllers and valves to keep related process variables within constraint limits and to improve economic performance. This basic approach to managing the multivariable nature of industrial processes remains a prominent aspect of operation today, whether in lieu of, or in conjunction with, modern automated multivariable controllers.

With the advent of computers in process control, it became possible to automate and “close the loop” on multivariable control, with obvious potential to improve the quality of constraint control and optimization. Multivariable control technology that combined mathematical models of process interactions, economic optimization routines, and matrix-based solution techniques soon appeared to accomplish this, and the rest is history. Since the 1980s, model-based predictive multivariable control (MPC) has thoroughly dominated the field of advanced process control (APC). Today, the terms are usually synonymous.

But MPC has not been without difficulties. Although a limited number of applications are delivering high value, and many are delivering partial success, MPC performance levels overall have remained low. “Degraded” MPC performance and MPC applications that have “fallen into disuse” are well-known, if rarely highlighted, industry concerns. Users have assumed this situation would correct itself with time, but today installation costs remain high, a manageable ownership model has not emerged, and performance levels continue to be low. Industry enthusiasm for MPC, once unbridled, has become circumspect, and decision makers are increasingly reluctant to allocate the high levels of financial and human resources that once seemed warranted for MPC.

Industry is thus faced with a question it thought was settled: Is MPC the technology of choice for automated multivariable control going forward, or is a reevaluation indicated at this juncture? This article explores the role of models in traditional MPC, their part in its cost and performance history, the necessity of models going forward, and the viability of an alternative model-less approach to multivariable constraint control and optimization, based on industry’s experiences and lessons of the past 20 years.

Experience points the way forward

The MPC paradigm that has become deeply rooted in industry in past decades can make it difficult to imagine multivariable control without models. But the above discussion has identified several perspectives that suggest model-less multivariable control may be both possible and preferable in many cases. Model-less control already exists in the form of many detuned MPCs that largely ignore model detail, and it has always existed in the form of manual multivariable control.

An initial response to the idea of model-less multivariable control is often that without accurate gain values, how can the combined gain of moving multiple DCVs and the combined economic effects be known? In other words, how can the multivariable constraint control and optimization problem be solved? This is a good question when multivariable control is approached as a mathematical problem, without a process operation perspective. However, this question never arises from actual operating teams, because they already know the correct control actions for any given situation, based on their process knowledge, training, experience, and usually common sense. MPC projects often seem to bring new wisdom to process operation, by virtue of a more global solution involving many models, but in almost every application, actual post-deployment controller behavior is bent to the established wisdom of the operating team (through the use of detuning and other improvised practices), not vice versa. A more reliable approach, this experience suggests, would be to design controllers based on proven operating practice in the first place.

Framing multivariable control as a global optimization problem dependent upon dozens (often hundreds) of detailed models, rather than framing it as automating the more common-sense logic and methods already employed by the operating team, may have seemed like innovative use of new-found computer power in the 1980s. In retrospect, it made the problem much bigger, and the solution much less reliable, than necessary. Several accompanying assumptions that also seemed reasonable at the time—such as the ease of achieving model fidelity, the idea that more models improve the result, and the assumption that ideal tuning is naturally preferable to detuned behavior—unfortunately also turned out to be largely incorrect. Consequently, this path had very limited success and has left industry lacking an appropriately scaled, affordable, and agile tool for the majority of straight-forward industrial multivariable process control applications.

A model-less multivariable controller would function similarly to historical manual multivariable control, except more timely and reliably, thereby capturing the benefits industry expects (if not always achieves) from MPC. This behavior is also similar to an appropriately detuned, and otherwise well-designed, MPC controller. The DCVs move persistently but cautiously, based primarily on gain direction, to effect constraint management and optimization, and movement stops based on process feedback as the constraint limits or optimization targets are approached.

This method does not require or depend on detailed models. It depends on only three pieces of process knowledge: gain direction of the primary interactions, preselected conservative move sizes for each DCV, and optimization priorities for each variable. Importantly, this is all common knowledge among the operating team and can be captured in a meeting, without a plant test or large-scale engineering effort. The “primary” interactions are those that are already proven and employed in operation for constraint management and optimization, i.e., the “small matrix” philosophy. Preferred conservative move rates for key variables are always well-known within operations, and are often documented in existing operating procedures. And within most MPC practice, actual stream pricing was abandoned in favor of a simpler, more practical, and more reliable optimization priority scheme years ago.

This concept of model-less multivariable control has yet to surface in industry as an available technology, but its potential efficacy and advantages are not difficult to perceive, and commercial products are sure to follow, especially as the lessons of model-based control become clear. Dispensing with the entire aspect of detailed modeling would be a paradigm shift with the promise to reduce costs and complexity at every life-cycle stage of multivariable control, including procurement, design, deployment, training, operation, maintenance, modification, and performance monitoring. It also has the potential to move multivariable control from the domain of specialists, third parties, and large budgets, into the domain of routine operational competency. As a result, design, deployment, and operation can be accomplished by the operating team and in-house control engineers, based on standard DCS control system capabilities. This would transform multivariable control from a specialized, high-cost, high-maintenance technology, into an agile and affordable tool, appropriately scaled in terms of cost and complexity, for the widespread needs of the process industries.

Click here to view the full article at InTech magazine.

About the Author
Allan KernAllan Kern, P.E., has 35 years of process control experience. He has authored numerous papers on topics ranging from field instrumentation, safety systems, and loop tuning to multivariable control, inferential control, and expert systems. From 2001 to 2008, Kern served as automation leader at a major Middle Eastern refinery, where his responsibilities included deployment and performance of multivariable control systems. Since 2005, Kern has published more than a dozen papers on multivariable control performance. In 2012, he became an independent process control consultant serving clients worldwide.
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