Regulatory control systems

Economic Incentives for Automation Projects Industrial applications of advanced process control strategies such as MPC are motivated by the need for improvements regarding safety, product quality, environmental standards, and economic operation of the process. One view of the economics incentives for advanced automation techniques is illustrated in Fig. 8-41. Distributed control systems (DCS) are widely used for data acquisition and conventional singleloop (PID) control. The addition of advanced regulatory control systems such as selective controls, gain scheduling, and time-delay compensation can provide benefits for a modest incremental cost. But... 

For regulatory control, repeatability is of major interest. The basic objective of regulatory control is to maintain uniform process operation. Suppose that on two different occasions, it is desired that the temperature in a vessel be 800°C. The regulatory control system takes appropriate actions to bring the measured variable to 800°C. The difference between the process conditions at these two times is determined by the repeatability of the measurement device. 

We have presented a very brief discussion of typical control schemes for a number of important unit operations furnaces, compressors, decanters, steam/power processes, liquid-liquid extractors, and evaporators. Each of these units requires the control of certain key variables if it is to do its job. A basic regulatory control system must be in place on each unit. 

A plantwide control design procedure was used to develop a simple but effective regulatory control system for the Eastman process with an on-demand product control objective. With this strategy, control of production rate is essentially instantaneous. Drastic upsets and disturbances are handled by simple proportional-only overrides. 

An effective base-level regulatory control system has been developed and tested using a rigorous, nonlinear dynamic simulation of the entire system tubular reactor, heat exchangers, and three distillation columns. 

We could go on for many pages about the many interesting dynamic and steady-state effects observed in this complex nonlinear process. The primary message, however, is that a base-level regulatory control system has been designed that provides effective stable control of this complex process. 

The plantwide process control design procedure was applied to this complex process it yielded an effective base-level regulatory control system. 

We showed through nonlinear dynamic simulations how the process reacts to various disturbances and changes in operating conditions. We have not shown any attempts to optimize process performance, to improve the process design, or to apply any advanced control techniques (model-based, nonlinear, feedforward, valve-position, etc.). These would be the natural next steps after the base-level regulatory control system had been developed to keep the process at a stable desired operating point. 

If the regulatory control system were perfect, the target could be set exactly equal to the constraint (that is, the target for the pressure controller could be set at the vessel relief pressure"). However, no regulatory control system is perfect. Therefore, the value specified for the target must be on the safe side of the constraint, thus giving the control system some elbow room. How much depends on the following ... 

Automatic regulatory control systems (Fig. 15) have been in use in the process industries for over fifty years. 

Design of Optimal Regulatory Control Systems 650 Things to Think About 654... 

According to the discussion presented in the introduction, under the assumption that an effective regulatory control system can be designed for the system under consideration, not... 

Following selection of the independent and dependent variables, a pre-test of the unit s regulatory control system was conducted. The objective of the pre-test was to collect information on the settling time of the process and the tuning of the regulatory controllers. Since the multivariable model predictive controller was to be eventually superimposed over the basic regulatory... 

Finally, as Rinard (1990) has poignantly noted, The regulatory control system affects the size of your pay-check the safety control system affects whether or not you will be around to collect it. ... 

In Step 1 of the MFC calculations, new process data are acquired via the regulatory control system (DCS) that is interfaced to the process. Then new output predictions are calculated in Step 2 using the process model and the new data (see Eqs. 20-21 and 20-22, for example). 

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