Control Strategies

G. T. Wolff, J. L. Hanisch, and K. Schere, eds.. The Scientific and Technical Issues Facing Post-1987 O ne Control Strategies, Air and Waste Management Association, Pittsburgh, Pa., 1988. 

Figure 11 shows a system for controlling the water dow to a chemical reactor. The dow is measured by a differential pressure (DP) device. The controller decides on an appropriate control strategy and the control valve manipulates the dow of coolant. The procedure to determine the overall failure rate, the failure probabiUty, and the reUabiUty of the system, assuming a one-year operating period, is outlined hereia. 

In order to operate a process facility in a safe and efficient manner, it is essential to be able to control the process at a desired state or sequence of states. This goal is usually achieved by implementing control strategies on a broad array of hardware and software. The state of a process is characterized by specific values for a relevant set of variables, eg, temperatures, flows, pressures, compositions, etc. Both external and internal conditions, classified as uncontrollable or controllable, affect the state. Controllable conditions may be further classified as controlled, manipulated, or not controlled. Excellent overviews of the basic concepts of process control are available (1 6). 

Generic Control Strategies. The two generic strategies for process control are feedback and feedforward control. Most process control strategies are based on one or a combination of these strategies (1 3). 

Some of the inherent advantages of the feedback control strategy are as follows regardless of the source or nature of the disturbance, the manipulated variable(s) adjusts to correct for the deviation from the setpoint when the deviation is detected the proper values of the manipulated variables are continually sought to balance the system by a trial-and-error approach no mathematical model of the process is required and the most often used feedback control algorithm (some form of proportional—integral—derivative control) is both robust and versatile. 

The main disadvantage of the feedback control strategy is that no corrective action is taken until after a deviation between the measured controlled... 

The feedforward control strategy (Fig. lb) addresses the disadvantages of the feedback control strategy. The feedforward control strategy measures the disturbance before it affects the output of the process. A model of the process determines the adjustment ia the manipulated variables(s) to compensate for the disturbance. The information flow is therefore forward from the disturbances, before the process is affected, to the manipulated variable iaputs. 

The primary advantage of the feedforward over the feedback control strategy is that corrective action is initiated before the controlled variable is upset. Feedforward control, however, has its own drawbacks, ie, variables used to characterize the disturbances must be measurable a model of the response of the controlled variable to the disturbance must be available (when the feedforward strategy is used alone, the control performance depends on the accuracy of the model) and the feedforward control strategy does not compensate for any disturbance not measured or modeled. 

In most process plant situations where feedforward control is appropriate, a combination of the feedforward and feedback control is usually used. The feedforward portion reduces the impact of measured disturbances on the controlled variable while the feedback portion compensates for model inaccuracies and unmeasured disturbances. This control strategy is referred to as feedforward control with feedback trim. 

Schemes to control the outlet temperature of a process furnace by adjusting the fuel gas flow are shown in Figure 13. In the scheme without cascade control (Fig. 13a), if a disturbance has occurred in the fuel gas supply pressure, a disturbance occurs in the fuel gas flow rate, hence, in the energy transferred to the process fluid and eventually to the process fluid furnace outlet temperature. At that point, the outlet temperature controller senses the deviation from setpoint and adjusts the valve in the fuel gas line. In the meantime, other disturbances may have occurred in the fuel gas pressure, etc. In the cascade control strategy (Fig. 13b), when the fuel gas pressure is disturbed, it causes the fuel gas flow rate to be disturbed. The secondary controller, ie, the fuel gas flow controller, immediately senses the deviation and adjusts the valve in the fuel gas line to maintain the set fuel gas rate. If the fuel gas flow controller is well tuned, the furnace outlet temperature experiences only a small disturbance owing to a fuel gas supply pressure disturbance.

Both control schemes react in a similar manner to disturbances in process fluid feed rate, feed temperature, feed composition, fuel gas heating value, etc. In fact, if the secondary controller is not properly tuned, the cascade control strategy can actually worsen control performance. Therefore, the key to an effective cascade control strategy is the proper selection of the secondary controlled variable considering the source and impact of particular disturbances and the associated process dynamics. 

Cascade control strategies are among the most popular and usehil process control strategies. Modem control systems have made thek implementation and operation both easier from the standpoint of operations personnel, and cost effective as they are implemented in software rather than hardwiring the connections. 

The decision to implement a feedforward control strategy should be based on the quahty of control requked, the nature and frequency of the... 

Fig. 17. Examples of selective control strategy (a) reactor hot spot (b) level override (c) prioritized and (d) constraint controls, where...

Multivariable control strategies utilize multiple input—multiple output (MIMO) controUers that group the interacting manipulated and controlled variables as an entity. Using a matrix representation, the relationship between the deviations in the n controlled variable setpoints and thek current values,, and the n controUer outputs, is... 

Adaptive Control. An adaptive control strategy is one in which the controller characteristics, ie, the algorithm or the control parameters within it, are automatically adjusted for changes in the dynamic characteristics of the process itself (34). The incentives for an adaptive control strategy generally arise from two factors common in many process plants (/) the process and portions thereof are really nonlinear and (2) the process state, environment, and equipment s performance all vary over time. Because of these factors, the process gain and process time constants vary with process conditions, eg, flow rates and temperatures, and over time. Often such variations do not cause an unacceptable problem. In some instances, however, these variations do cause deterioration in control performance, and the controllers need to be retuned for the different conditions. 

Constraint control strategies can be classified as steady-state or dynamic. In the steady-state approach, the process dynamics are assumed to be much faster than the frequency with which the constraint control appHcation makes its control adjustments. The variables characterizing the proximity to the constraints, called the constraint variables, are usually monitored on a more frequent basis than actual control actions are made. A steady-state constraint appHcation increases (or decreases) a manipulated variable by a fixed amount, the value of which is determined to be safe based on an analysis of the proximity to relevant constraints. Once the appHcation has taken the control action toward or away from the constraint, it waits for the effect of the control action to work through the lower control levels and the process before taking another control step. Usually these steady-state constraint controls are implemented to move away from the active constraint at a faster rate than they do toward the constraint. The main advantage of the steady-state approach is that it is predictable and relatively straightforward to implement. Its major drawback is that, because it does not account for the dynamics of the constraint and manipulated variables, a conservative estimate must be taken in how close and how quickly the operation is moved toward the active constraints. 

Implement sustainable real-time process control strategies... 

---