Computer Control Systems
Distributed Control Systems (DCS)
Microcomputer-based subsystems are standard in most computer control systems available today. The digital subsystems are interconnected through a digital communications network. Such systems are referred to as distributed digital instrumentation and control systems because of the network approach used to monitor and control the progress.
Figure 18.63 depicts a representative distributed control system (Seborg, Edgar, and Mellichamp, 2004). The DCS system consists of many commonly used DCS components, including multiplexers (MUXs), single-loop and multiple-loop controllers, PLCs, and smart devices. A system includes some or all of the following components:
1. Control network. The control network is the communication link between the individual com- ponents of a network. Coaxial cable and, more recently, fiber-optic cable have often been used. A redundant pair of cables (dual redundant highway) is normally supplied to reduce the possibility of link failure.
2. Workstations. Workstations are the most powerful computers in the system, and act both as an arbitrator unit to route internodal communications and the database server. Various peripheral devices are coordinated through the workstations. Computationally intensive tasks, such as real- time optimization or model predictive control, are implemented in a workstation.
3. Real-time clocks. Process control systems must respond to events in a timely manner and should have the capability of real-time control.
4. Operator stations. Operator stations typically consist of color graphics monitors with special key- boards to perform dedicated functions. Operators supervise and control processes from these workstations. Operator stations may be connected directly to printers for alarm logging, printing reports, or process graphics.
5. Engineering Workstations. They are similar to operator stations but can also be used as program- ming terminals to develop system software and applications programs.
6. Remote control units (RCUs). These components are used to implement basic control functions such as PID control. Some RCUs may be configured to acquire or supply set points to single-loop controllers. Radio telemetry (wireless) may be installed to communicate with MUX units located at great distances.
7. Programmable logic controllers (PLCs). These digital devices are used to control batch and sequential processes, but can also implement PID control.
8. Application stations. These separate computers run application software such as databases, spread- sheets, financial software, and simulation software via on OPC interface. OPC is an acronym for object linking and embedding for process control, a software architecture based on standard inter- faces. These stations can be used for e-mail and as webservers, for remote diagnosis, configuration, and even for operation of devices that have an IP (Internet protocol) address. Applications stations can communicate with the main database contained in on-line mass storage systems.
9. Mass storage devices. Typically, hard disk drives are used to store active data, including on-line and historical databases and nonmemory resident programs. Memory resident programs are also stored to allow loading at system start-up.
FIGURE 18.63 A typical distributed control system (DCS).
10. Fieldbuses/smart devices. An increasing number of field-mounted devices are available that support digital communication of the process I/O in addition to, or in place of, the traditional 4–20 mA current signal. These devices have greater functionality, resulting in reduced setup time, improved control, combined functionality of separate devices, and control-valve diagnostic capabilities.
Supervisory Control/Real-Time Optimization
The selection of set points in a distributed control network is called supervisory control. These set points may be determined in a control computer and then transmitted to specific devices (digital or analog controllers) in each control loop. Most supervisory control strategies are based on real-time optimization (RTO) calculations, wherein the set points (operating conditions) are determined by profitability analysis, that is, maximizing the difference between product value (income) and operating costs. For more details on real-time optimization methods such as linear and nonlinear programming or other search techniques see Seborg, Edgar, and Mellichamp (2004). RTO is often used in conjunction with model predictive control.
Batch Control
The increased availability of digital computer control and the emerging specialty chemical business has made batch control a very important component in process plants. Batch operations are of a start- stop nature; however, startup and shutdown of continuous plants must be treated in a similar fashion. Feedback control is of some value for batch processing, but it is more useful for operation near a set point in continuous operations.
The majority of batch steps encompass a wide variety of time-based operating conditions, which are sequential in nature. They can only be managed via a computer control system. Consider the operation of a batch reactor. The operation may include charging the reactor with several reactants (the recipe), applying the heat required to reach the desired reaction temperature, maintaining a specified level of operation until the reaction reaches completion, stopping the reaction, removing the product, and preparing the reactor for another batch.
Discrete functions are implemented via hardware or software to control discrete devices such as on/off valves, pumps, or agitators, based on status (on/off) of equipment or values of process variables. Interlock control can be provided via automatic actuation of a particular device only if certain process conditions sensed by various instruments are met. The two categories of interlocks are safety and permissive interlocks. Safety interlocks are designed to ensure the safety of operating personnel and to protect plant equipment. These types of fail-safe interlocks are associated with equipment malfunction or shutdown. Permissive interlocks establish orderly startup and shutdown of equipment. This prevents accumulation of material in tanks before it is needed. The triggers in the instructions can be time related or process related (temperature, pressure, etc.). Sequencing requires an end condition to be reached before the system can proceed to the next step. More details on batch control system design have been reported by Rosenhof and Ghosh (1987) and Seborg, Edgar, and Mellichamp (2004).
Process Control Software
The introduction of high-level programming languages such as Fortran and Basic in the 1960s was con- sidered a major breakthrough in the area of computer control. For process control applications, some companies have incorporated libraries of software routines for these languages, but others have developed speciality pseudo-languages. These implementations are characterized by their statement-oriented lan- guage structure. Although substantial savings in time and efforts can be realized, software development costs can be significant.
The most successful and user-friendly approach, which is now adopted by virtually all commercial systems, is the fill-in-the-forms or table-driven process control languages (PCL). The core of these languages is a number of basic functional blocks or software modules. All modules are defined as database points. Using a module is analogous to calling a subroutine in conventional programs.
In general, each module contains some inputs and an output. The programming involves softwiring outputs of blocks to inputs of other blocks. Some modules may require additional parameters to direct module execution. The users are required to fill in the sources of input values, the destinations of output values, and the parameters in the blanks of forms/tables prepared for the modules. The source and destination blanks may be filled with process I/Os when appropriate. To connect modules, some systems require filling the tag names of modules originating or receiving data. The blanks in a pair of interconnecting modules are filled with the tag name of the same data point. A completed control strategy resembles a data flow diagram. All process control languages contain PID controller blocks. The digital PID controller is normally programmed to execute in velocity (difference) form. A pulse duration output may be used to receive the velocity output directly. In addition to the tuning constants, a typical digital PID controller contains some entries not normally found in analog controllers:
✁ When a process error is below certain tolerable deadband, the controller ceases modifying the output. This is referred to as gap action.
✁ The magnitude of change in a velocity output is limited by a change clamp.
✁ A pair of output clamps is used to restrict a positional output value from exceeding specified limits.
✁ The controller action can be disabled by triggering a binary deactivate input signal, during process startup, shutdown, or when some abnormal conditions exist.
Although modules are supplied and their internal configurations are different from system to system, their basic functionalities are the same Another recent development has been the availability of flexible, yet powerful, software packages that support the controller design, controller testing, and implementation process. Probably, the most widely used program for this purpose is MATLAB® (The MathWorks, Inc.; www.mathworks.com), due to its flex- ibility. It allows one to implement and test controllers by either solving differential equations, using Laplace transforms, or with block diagrams. MATLAB® also provides a variety of routines that are commonly used for different controller design problems, e.g., optimal control, nonlinear control, optimization, etc. One of the main advantages of MATLAB® is that it is a programming language which provides control-related subroutines. This gives the process engineer flexibility with regard to the use of the software as well as how to extend or reuse already existing routines. It is also possible to exchange data with other software packages from within MATLAB®.
Digital Field Communications
A group of computers can become networked once intercomputer communication is established. Prior to the 1980s, all system suppliers used proprietary protocols to network their systems. The recent introduction of standardized protocols is based on the ISO-OSI∗ seven-layer model. The manufacturing automation protocol (MAP), which adopted the ISO-OSI standards as its basis, specifies a broadband backbone local area network (LAN). Originally intended for discrete component systems, MAP has evolved to address the integration of DCSs used in process control as well. TCP/IP (transmission control protocol/internet protocol) has been adopted for communication between nodes that have different operating systems.
Microprocessor-based process equipment, such as smart instruments and single-loop controllers, are now available with digital communications capability and are used extensively in process plants. A fieldbus, which is a low-cost protocol, provides efficient communication between the DCS and these devices.
Presently, there are several regional and industry-based fieldbus standards, including the French standard (FIP), the German standard (Profibus), and proprietary standards by DCS vendors, generally in the U.S., led by the Fieldbus Foundation. As of 2004, international standards organizations had adopted all of these fieldbus standards rather than a single unifying standard.
Several manufacturers provide fieldbus controllers that reside in the final control element or measurement transmitter. A suitable communications modem is present in the device to interface with a proprietary PC-based, or hybrid analog/digital bus network. Case studies in implementing such digital systems have shown significant reductions in cost of installation (mostly cabling and connections) vs. traditional analog field communication.
An example of a hybrid analog/digital protocol that is open (not proprietary) and in use by several vendors is the highway addressable remote transducer (HART) protocol. Digital communications utilize the same two wires that provide the 4 to 20 mA process control signal without disrupting the actual process signal. This is done by superimposing a frequency-dependent sinusoid ranging from −0.5 to +0.5 mA to represent a digital signal.
Defining Terms
Adaptive control: A control strategy that adjusts its control algorithm as the process changes.
Batch control: Control of a process where there is no inflow/outflow.
Cascade control: Nested multiloop strategy that uses intermediate measured variables to improve control.
Controlled variables: Measurable variables that quantify process performance.
Distributed control system: The standard computer architecture in process control, which involves multiple levels of computers.
Feedback control: A control structure where the controlled variable measurement is compared with the setpoint, generating an error acted upon by the controller.
Feedforward control: A control structure where the disturbance is measured directly and a control action is calculated to counteract it.
Fieldbus: A new communication protocol for digital communication between instruments and a central computer.
Manipulated variables: Input variables that can be adjusted to influence the controlled variables.
Model predictive control: An advanced control strategy that uses a model to optimize the loop performance.
Multivariable control: A control strategy for multiple inputs and multiple outputs.
Statistical process control: A control strategy that keeps a process between upper and lower limits (but not at the setpoint).
Supervisory control: The selection of set points, usually to maximize profitability or minimize costs.
Three-mode (PID) control: A feedback control algorithm that uses proportional, integral, and derivative action on the error signal.
References
Astrom, K.J. and Wittenmark, B. 1994. Adaptive Control, 2nd ed. Addison-Wesley, Reading, MA. Bequette, B.W. 2003. Process Control: Modeling, Design and Simulation. Prentice-Hall, Upper Saddle River, NJ.
Chien, I.L. and Fruehauf, P.S. 1990. Consider IMC tuning to improve controller performance. Chem. Engr.
Prog. 86:33.
Edgar, T.F., Smith, C.L., Shinsky, F.G., Gassman, G.W., Schafbuch, P.J., McAvoy, T.J., and Seborg, D.E.
1997. See. 8, Process control. Perry’s Chemical Engineering Handbook, 7th ed. McGraw-Hill, New York.
Grant, E.L. and Leavenworth, R.S. 1996. Statistical Quality Control, 7th ed. McGraw-Hill, New York.
Maciejowski, J.M. 2002. Predictive Control with Constraints. Prentice-Hall, Upper Saddle River, N.J.
McAvoy, T.J. 1983. Interaction Analysis. Instrument Society of America, Research Triangle Park, NC.
Montgomery, D.C. 2001. Introduction to Statistical Quality Control, 4th ed. Wiley, New York.
Ogunnaike, B. and Ray, W.H. 1995. Process Dynamics: Modeling and Control. Oxford Press, New York.
Qin, S.J. and Badgwell, T.A. 2003. A Survey of Industrial Model Predictive Control Technology, Control Engineering Practice, 11:733.
Rivera, D.E., Morari, M., and Skogestad, S. 1986. Internal model control. 4. PID controller design. Ind.
Eng. Chem. Process Des. Dev. 25:252.
Rosenhof, H.P. and Ghosh, A. 1987. Batch Process Automation. Van Nostrand Reinhold, New York.
Seborg, D.E., Edgar, T.F., and Mellichamp, D.A. 2004. Process Dynamics and Control, 2nd ed. Wiley, New York.
Shinskey, F.G. 1996. Process Control Systems, 4th ed. McGraw-Hill, New York.
Smith, C.A. and Corripio, A.B. 1997. Principles and Practice of Automatic Process Control, 2nd ed. Wiley, New York.
Further Information
Recent research results on specialized topics in process control are given in the following books and conference proceedings:
Astrom, K.J. and Wittenmark, B. 1984. Computer Controlled Systems—Theory and Design. Prentice-Hall, Englewood Cliffs, NJ.
Buckley, P.S., Luyben, W.L., and Shunta, J.P. 1985. Design of Distillation Column Control Systems. Instrument Society of America, Research Triangle Park, NC.
Camacho, E.F. and Bordons, C. 1999 Model Predictive Control. Springer-Verlag, New York.
Kantor, J.C. Garcia, C.E., and Carnahan, B. 1997. Chemical Process Control, CPC-V, AIChE Symp. Series, 93(316).
Liptak, B.G. 1995. Instrument Engineers Handbook. Chilton Book, Philadelphia, PA.
Marlin, T. 1995. Process Control: Designing Processes and Control Systems for Dynamic Performance.
McGraw-Hill, New York.
Rawlings, J.B. and Ogunnaike, B.A., eds. 2002. Chemical Process Control-CPC VI, AIChE Symp. Series, 98(208).