# Plant-Wide Control Approach

## Herding Cats: Successfully Manage All Those Process Variables With a Systematic Approach to Plant-Wide Control

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A chemical plant may have thousands of measurements and control loops. By the term "plant-wide control," I do not mean the tuning and behavior of each of these loops, but rather the control philosophy of the overall plant with emphasis on the structural decisions:

• Selection of controlled variables (CVs, "outputs")
• Selection of manipulated variables (MVs, "inputs")
• Selection of (extra) measurements
• Selection of control configuration (structure of overall controller that interconnects the controlled, manipulated and measured variables)
• Selection of controller type (PID, decoupler, MPC, linear, quadratic, Gaussian (LQG), aka, optimal control, ratio, etc.).

In practice, the control system is usually divided into several layers, separated by the time scale (Figure 1).

Control structure design (plant-wide control) thus involves all the decisions necessary to make a block diagram or process and instrumentation diagram that includes the control system for the entire plant, but does not involve the actual design of each individual controller block.

In any mathematical sense, the plant-wide control problem is a formidable and almost hopeless combinatorial problem, involving a large number of discrete decision variables. In addition, the problem is poorly defined in terms of its objective. Usually in control, the objective is that the CV (output) should remain close to its setpoint.

However, what should we control? The answer lies in considering the overall plant objective, which is to minimize cost, that is, maximize profit, while satisfying operational constraints imposed by the equipment, marked demands, product quality, safety, environment and so on.

Actually, the "original" mathematical problem is not so difficult to formulate, and with today’s computing power, it may even be solvable. It would involve obtaining a detailed dynamic and steady-state model of the complete plant; defining all the operational constraints; defining all available measurements and manipulations; defining all expected disturbances; defining expected, allowed or desirable ranges for all variables; and then designing a nonlinear controller that satisfies all the constraints and objectives, while using the possible remaining degrees of freedom to minimize the cost. This would involve a single centralized controller that, at each time step, collects all the information and computes the optimal changes in the manipulated variables.

Although such a single, centralized solution is foreseeable on some very simple processes, it seems to be safe to assume that it will never be applied to any normal-sized chemical plant. There are many reasons for this, but one important one is that, in most cases, one can achieve acceptable control with simple structures, where each controller block only involves a few variables, and such control systems can be designed and tuned with much less effort, especially when it comes to the modeling and tuning effort. After all, most plants operate well with simple control structures.

#### Real-World Control

So how are real systems controlled in practice? The main simplification is to deconstruct the overall control problem into many simple control problems. This deconstruction involves two main principles:

• Decentralized (local) control. This "horizontal deconstruction" of the control layer is mainly based on separation in space, for example, by using local control of individual process units.
• Hierarchical control.  This "vertical deconstruction" is mainly based on time scale separation (Figure 1).

We generally have more centralization as we move upwards in the hierarchy. Such a hierarchical (cascade) deconstruction with layers operating on different time scales is used in the control of all complex systems.

The upper three layers in Figure 1 deal explicitly with economic optimization, and are not considered here. We are concerned with the two lower control layers, where the main objective is to track the setpoints specified by the upper layers. A very important structural decision, probably more important than the controller design itself, is the choice of controlled variables (CVs) that interconnect the layers. More precisely, the decisions made by each layer (boxes in Figure 1) are sent as setpoints for the CVs to the layer below.

#### A Plant-Wide Control Procedure

No matter what procedure we choose to use, the following decisions must be made when designing a plant-wide control strategy:

Decision 1—Select "economic" (primary) controlled variables (CV1) for the supervisory control layer (the setpoints CV1 link the optimization layer with the control layers);

Decision 2—Select "stabilizing" (secondary) controlled variables (CV2) for the regulatory control layer (the setpoints CV2 link the two control layers);

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