Supervisory computer control

The advantages of DDC/supervisory computer control over conventional analog systems have been listed by Popovic and Bhatkar<51) as ... 

Supervisory computer control As we discussed earlier, one of the incentives for process control is the optimization of the plant s... 

These decisions can be made rationally with the aid of a digital computer, which in turn will communicate these decisions to the supervisory computer controllers. Finally, the supervisory controllers will implement these decisions on the chemical plant through the DDCs. 

In subsequent chapters we will deal predominantly with the DDC and a little with supervisory computer control, but we will not concern ourselves with the scheduling computer control, which is the subject matter of a different field. 

Chapter 2 treats the topic of steady-state optimization. Necessary conditions for extrema of functions are derived using variational principles. These steady-state optimization techniques are used for the determination of optimal setpoints for regulators used in supervisory computer control. 

The plant components selected should be of proven design supported by strong reference installations. Modem trends are moving towards reduction in manning levels, and the use of central computer control and supervisory systems also ensures maximum efficiency of plant operation. 

Using the elaborated theoretical and computing approaches a most effective pipeline management system has been conceptually elaborated with control modes at local level, station level and pipeline level. Local control is manually implemented from the device itself. The automatic or manual control of an installation is affected from its local micro-SCADA system. The supervisory pipeline control is effected from the active pipeline control centre. 

A pilot plant can be computer controlled in one of two ways supervisory or direct digital control as shown in Fig. 10. In the first, a computer supervises another primary control element. In the second, the interfacing element is eliminated and the controller (e.g., a valve) is regulated directly. New computer-based automatic instrumentation is likely to be supervisory because it is faster and less expensive. (See also Refs.P - l.)... 

Supervisory control—A method of computer control whereby the computer provides only set-points to individual controllers which independently perform the actual control algorithms. 

Increasingly the senior technicians function as supervisors for computer control systems such as Distributed Control Systems (DCS) or Supervisory Control and Data Acquisition Systems (SCADA). These technicians need procedures that help them work with the electronic interface, generally as troubleshooters for when the electronic systems go awry, or for when the control systems fail. 

As process variables change it may be advantageous to alter setpoints in certain loops. This is accomplished by the supervisory computer which monitors process variables and uses a mathematical model of the process to estimate optimal settings. Sophisticated control strategies are possible because settings of several loops can be altered simultaneously. The task of the plant... 

Obviously, from a purely mathematical point of view, it would be optimal to use a centralized on-line optimizing controller with continuous update of its model parameters and continuous reoptimization of aU variables. However, for a number of reasons, we almost always decompose the control system into several layers, which in a chemical plant typically include scheduling (weeks), site-wide optimization (day), local optimization (hour), supervisory/predictive control (minutes) and regulatory control (seconds). Therefore, we instead consider the implementation shown in Figure 1 with separate optimization and control layers. The two layers interact through the controlled variables c, whereby the optimizer computes their optimal setpoints (typically, updating them about every hour), and the control layer attempts to implement them in practice, i.e. to get c Cj. The main issue considered in this chapter is then What variables c should we control ... 

For maximum advantage a supervisory computer should be programmed to have the control algorithms discussed in Chapter 12. These are position rather than velocity algorithms. It is our opinion that using such a computer to imitate unenhanced two- and three-mode analog controls is poor practice. Some worthwhile applications fi>r computers will be discussed later. 

A supervisory (computer) system, gathering (acquiring) data on the process and sending commands (control) to the process. 

This risk has led to the development of various detection systems to assure the user that not only was adhesive applied but the correct amount was deposited. The systems used to detect the adhesive range from sensing material flow through the nozzle to complex vision monitoring and real time adaptive control systems. These detectors work in conjunction with the automation and in many instances make full use of the automation s own computer control and can be linked into management control/supervisory systems initiating alarms etc. if an unsatisfactory condition arises. 

In this short initial communication we wish to describe a general purpose continuous-flow stirred-tank reactor (CSTR) system which incorporates a digital computer for supervisory control purposes and which has been constructed for use with radical and other polymerization processes. The performance of the system has been tested by attempting to control the MWD of the product from free-radically initiated solution polymerizations of methyl methacrylate (MMA) using oscillatory feed-forward control strategies for the reagent feeds. This reaction has been selected for study because of the ease of experimentation which it affords and because the theoretical aspects of the control of MWD in radical polymerizations has attracted much attention in the scientific literature. 

The correct interpretation of measured process data is essential for the satisfactory execution of many computer-aided, intelligent decision support systems that modern processing plants require. In supervisory control, detection and diagnosis of faults, adaptive control, product quality control, and recovery from large operational deviations, determining the mapping from process trends to operational conditions is the pivotal task. Plant operators skilled in the extraction of real-time patterns of process data and the identification of distinguishing features in process trends, can form a mental model on the operational status and its anticipated evolution in time. 

Bakshi, B. R., and Stephanopoulos, G., Representation of process trends. Part IV. Induction of real-time patterns from operating data for diagnosis and supervisory control. Comput. Chem. Eng. 18 303 (1994b). 

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