Interface, control systems

Wire break on control system interface to equipment... 

OQ check alarm when control system interface disconnected... 

Modern communications are much more transaction based (a trend developed in the commercial and business sector), and these are less suited to traditional control system interfaces. Control systems have traditionally operated on a real-time basis, polling for data on a routine basis or being interrupt driven by alarms and events. These traditional systems are not well suited to transactional-based communications, and the use of object broker interfaces will overcome some of these problems. The new object-oriented interfaces will again be easier to validate since the object broker interfaces will be configured rather than programmed and will use standard software blocks to build the interfaces. 

C. Control System Interfaces. Figures 11.6 and 11.7 show that most operations require multiple filters. These systems, as well as the ion exchangers discussed below, operate in a cyclic manner, with each component periodically taken out of service for regeneration or redressing. Sequential controls are necessary in addition to the process line controls shown in this chapter. These are separate digital controls that are frequently supplied by the unit manufacturer in a PLC. 

Novel multiphase technology has been coupled with dynamic process simulation, open control system interfaces and a custom user interface to provide a necessary tool for process operators. We hope to be able to report interesting experience and observations from the commissioning of the system. 

For loop checks we need the PES fully installed and running. Add in the installation of the PES. The PES can function with its own engineering interface, but if the process control system interface is to be used we need a tie in to the installed PLC/Scada or DCS preferably by the time we start loop checks. 

Fig. 2. Distributed control system stmcture, where PIU = process interface unit LCUj = local control unit, model 1 LCU2 = local control unit, model 2 ...

The process controller is the master of the process-control system. It accepts a set point and other inputs and generates an output or outputs that it computes from a rule or set of rules that are part of its internal configuration. The controller output seiwes as an input to another controller or, more often, as an input to a final control element. The final control element is the device that affects the flow in the piping system of the process. The final control element seiwes as an interface between the process controller and the process. Control valves and adjustable speed pumps are the principal types discussed. 

Distributed Control System (DCS) A system that divides process control functions into specific areas interconnected by communications (normally data highways) to form a single entity. It is characterized by digital controllers, typically administered by central operation interfaces and intermittent scanning of the data highway. 

As project structures are temporary, there needs to be a system in place that controls the interfaces between the line functions and project team. Such a system would include ... 

The first set of case studies illustrates errors due to the inadequate design of the human-machine interface (HMI). The HMI is the boundary across which information is transmitted between the process and the plant worker. In the context of process control, the HMI may consist of analog displays such as chart records and dials, or modem video display unit (VDU) based control systems. Besides display elements, the HMI also includes controls such as buttons and switches, or devices such as trackballs in the case of computer controlled systems. The concept of the HMI can also be extended to include all means of conveying information to the worker, including the labeling of control equipment components and chemical containers. Further discussion regarding the HMI is provided in Chapter 2. This section contains examples of deficiencies in the display of process information, in various forms of labeling, and the use of inappropriate instrumentation scales. 

Computer monitoring and control systems have recently been introduced. These are designed to operate in place of conventional instrumentation. Using intelligent interface outstations connected to a desktop computer, many plant functions may be programed into the computer and controlled centrally. 

In a discussion of these results, Bertrand et al. [596,1258] point out that S—T behaviour is not a specific feature of any restricted group of hydrates and is not determined by the nature of the residual phase, since it occurs in dehydrations which yield products that are amorphous or crystalline and anhydrous or lower hydrates. Reactions may be controlled by interface or diffusion processes. The magnitudes of S—T effects observed in different systems are not markedly different, which indicates that the controlling factor is relatively insensitive to the chemical properties of the reactant. From these observations, it is concluded that S—T behaviour is determined by heat and gas diffusion at the microdomain level, the highly localized departures from equilibrium are not, however, readily investigated experimentally. 

Products in Group 3 seem to us to represent the future of practical batch process control. In such systems, modern workstations perform the single-user functions (e.g control system design, set-up, and maintenance operator interface data collection historical reporting) for which they were designed, while powerful multitasking controllers perform actual control. As computer hardware and software standards continue to evolve toward distributed networks of processors optimized for specific kinds of tasks, such systems will, we feel, proliferate rapidly. 

An extruder for a polymer was controlled by a microprocessor based data acquisition and control system. The CAMILE system (Control And Monitoring Interface for Laboratory Experiments) connects the sensors and control elements of the extruder to a host MS-DOS computer. While a variety of variables are measured and controlled, this paper will consider only temperature control. 

Powerful mouse/menu controlled graphical interface creates system block diagrams, generates error-free simulation models, executes the simulation, and displays graphical results. 

The reactor pressure is reduced to 0 psig to flash off any remaining water after a desired temperature is reached. Simultaneous ramp up of the heat source to a new setpoint is also carried out. The duration spent at this second setpoint is monitored using CUSUM plots to ensure the batch reaches a desired final reactor temperature within the prescribed batch time. The heat source subsequently is removed and the material is allowed to continue reacting until the final desired temperature is reached. The last stage involves the removal of the finished polymer as evidenced by the rise in the reactor pressure. Each reactor is equipped with sensors that measure the relevant temperature, pressure, and the heat source variable values. These sensors are interfaced to a distributed control system that monitors and controls the processing steps. 

Introduction The chemical processing industry relies on many types of instrumented systems, e.g., the basic process control systems (BPCSs) and safety instrumented system (SIS). The BPCS controls the process on a continuous basis to maintain it within prescribed control limits. Operators supervise the process and, when necessary, take action on the process through the BPCS or other independent operator interface. The SIS detects the existence of unacceptable process conditions and takes action on the process to bring it to a safe state. In the past, these systems have also been called emergency shutdown systems, safety interlock systems, and safety critical systems. 

The third case study consists of a well-instrumented experimental distillation column that has been interfaced to an industrial distributed control system. In this... 

Finally, a well-instrumented experimental distillation column that has been interfaced to an industrial distributed control system was used to show the implementation of the techniques described in previous chapters in an actual on-line framework, using industrial hardware. In this case, the usefulness of data reconciliation, prior to process modeling and optimization, was clearly demonstrated. 

Direct digital control systems appeared in the mid-1980s and displaced older analog closed-loop schemes for temperature control. These digital systems improved both accuracy and reliability. The earlier systems were modeled after existing system architectures and did not contain intelligent, standalone field devices. There were numerous interfaces to the various building systems and the major decisions were made at a central computer. 

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