Benefits of multivariable process control

Multivariable process control can help process engineers do more with the processes and systems already on the plant floor.

By Stephen Cronauer January 18, 2023
Courtesy: Chris Vavra, CFE Media

Multivariable process control insights

  • Multivariable processes have more than one input or output required for proper control. Examples include distillation columns, reactor control and power systems.
  • The most common automated multivariable control technology is model-predictive control (MPC), which uses dynamic process models to predict future process responses.

Rising costs, shrinking margins, and supply chain issues are problems that impact us daily and manufacturers are looking for any and all ways to do more with the processes and systems that they already have. One often overlooked option for getting more out a process are multivariable process control improvements.

A multivariable process is anything with more than one input and/or more than one output required for proper control and for which multiple setpoints are involved. The complexity arises when the setpoints in the system are cross-coupled with one another, meaning that a change in one setpoint affects the process variable of another. Other complexities occur with processes that have long time delays or high order dynamic responses. Examples of multivariable systems include:

  • Distillation column

  • Reactor control (e.g. trying to control level and concentration)

  • Petroleum refining

  • Natural gas recovery

  • In-line blending

  • Power systems.

If the interactions of the cross-coupled variables are too strong, basic process control may not provide users with the most efficient and predictable system that can optimize yields, maximize production output and decrease energy use.

Chances are good engineers already have practical experience with multivariable control –the morning commute. Driving is about pressing on the accelerator to go and the brake to stop. When the accelerator is depressed, the engine helps bring the car up to the desired speed setpoint (the speed limit, of course). As users approach the setpoint, the engineers eases off the accelerator to the point where the engine has just enough gas to keep the driver at the speed limit.

Of course, not all roads are flat. To maintain speed on a hill, drivers need to depress the accelerator before the car has reached it. This feed forward approach means that the car’s speed does not take a dip in the beginning of the incline. In multivariable process control, many variables are measured, and decisions are made based on some, or all, of this data.

How multivariable process control works

The most common automated multivariable control technology is model-predictive control (MPC), a control technique that uses dynamic process models to predict the future response of the process. The models used in MPC are developed during plant tests to identify the dynamic and steady state effects on the control variables (CVs) when manipulated variables (MVs) or disturbance variables (DVs) are changed. In our driving example above, the speed is the CV, the accelerator is the MV, and the external factors are the DVs. Models developed for this control process would tell the engineer how far and quickly to adjust the accelerator based on a change in any of the inputs.

It is important to note that MPC does not “control” CVs, per se, but rather, it determines feasible combinations of all MVs such that all CV steady state targets are at or within their operating limits. If more than one feasible solution exists, the economic information programmed into the controller will be used to choose the best feasible solution. This feature not only provides stability but pushes the plant to its economic optimum.

If no feasible solution exists, the limits of the least important CVs are relaxed. Once the steady state targets of the MVs are calculated, MPC creates dynamic move plans for all MVs. The move plans are updated each execution cycle of the controller to account for changes in DVs.

– Applied Control Engineering (ACE) is a CFE Media and Technology content partner. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology,

Original content can be found at Applied Control Engineering, Inc. (ACE).

Author Bio: Stephen Cronauer, controls engineer at Applied Control Engineering, Inc.

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