Automatic process control in continuous production processes is a combination of control engineering and chemical engineering disciplines that uses industrial control systems to achieve a production level of consistency, economy and safety which could not be achieved purely by human manual control. It is implemented widely in industries such as oil refining, pulp and paper manufacturing, chemical processing and power generating plants. There is a wide range of size, type and complexity, but it enables a small number of operators to manage complex processes to a high degree of consistency. The development of large automatic process control systems was instrumental in enabling the design of large high volume and complex processes, which could not be otherwise economically or safely operated. The applications can range from controlling the temperature and level of a single process vessel, to a complete chemical processing plant with several thousand control loops.
1 History 2 Development of modern process control operations 3 Hierarchy 4 Control Model 5 Types 6 Control loops 7 Economic advantages 8 List of techniques and mechanisms used 9 External links 10 References
Early process control breakthroughs came most frequently in the form
of water control devices. Ktesibios of Alexandria is credited for
inventing float valves to regulate water level of water clocks in the
3rd Century BC. In the 1st Century AD, Heron of Alexandria invented a
water valve similar to the fill valve used in modern toilets.
Later process controls inventions involved basic physics principles.
In 1620, Cornlis Drebbel invented a bimetallic thermostat for
controlling the temperature in a furnace. In 1681, Denis Papin
discovered the pressure inside a vessel could be regulated by placing
weights on top of the vessel lid. In 1745, Edmund Lee created the
fantail to improve windmill efficiency; a fantail was a smaller
windmill placed 90° of the larger fans to keep the face of the
windmill pointed directly into the oncoming wind.
With the dawn of the Industrial Revolution in the 1760s, process
controls inventions were aimed to replace human operators with
mechanized processes. In 1784, Oliver Evans created a water-powered
flourmill which operated using buckets and screw conveyors. Henry Ford
applied the same theory in 1910 when the assembly line was created to
decrease human intervention in the automobile production process.
For continuously variable process control it was not until 1922 that a
formal control law for what we now call
A modern control room where plant information and controls are displayed on computer graphics screens. The operators are seated as they can view and control any part of the process from their screens, whilst retaining a plant overview.
Functional levels of a manufacturing control operation.
The accompanying diagram is a general model which shows functional manufacturing levels in a large process using processor and computer-based control. Referring to the diagram;
Level 0 contains the field devices such as flow and temperature sensors (process value readings - PV), and final control elements (FCE), such as control valves Level 1 contains the industrialised Input/Output (I/O) modules, and their associated distributed electronic processors. Level 2 contains the supervisory computers, which collate information from processor nodes on the system, and provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and monitoring targets Level 4 is the production scheduling level.
Control Model To determine the fundamental model for any process, the inputs and outputs of the system are defined differently than for other chemical processes. The balance equations are defined by the control inputs and outputs rather than the material inputs. The control model is a set of equations used to predict the behavior of a system and can help determine what the response to change will be.
State Variable (x) - This is a measurable variable that is a good indicator of the state of the system, such as temperature (energy balance), volume (mass balance) or concentration (component balance). Input Variable (u) - This is a specified variable that commonly include flow rates. It's important to note that the entering and exiting flows are both considered control inputs. The control input can be classified as a manipulated, disturbance, or unmonitored variable. Parameters (p) - The parameters are usually a physical limitation and something that is fixed for the system, such as the vessel volume or the viscosity of the material. Output (y) - The output is the metric used to determine the behavior of the system. The control output can be classified as measured, unmeasured, or unmonitored.
Types Processes can be characterized as one or more of the following forms:
Batch – Some applications require that specific quantities of raw
materials be combined in specific ways for particular duration to
produce an intermediate or end result. One example is the production
of adhesives and glues, which normally require the mixing of raw
materials in a heated vessel for a period of time to form a quantity
of end product. Other important examples are the production of food,
beverages and medicine. Batch processes are generally used to produce
a relatively low to intermediate quantity of product per year (a few
pounds to millions of pounds).
Continuous – Often, a physical system is represented through
variables that are smooth and uninterrupted in time. The control of
the water temperature in a heating jacket, for example, is an example
of continuous process control. Some important continuous processes are
the production of fuels, chemicals and plastics. Continuous processes
in manufacturing are used to produce very large quantities of product
per year (millions to billions of pounds). Such controls use feedback
such as in the
Example of a continuous flow control loop. Signalling is by industry standard 4-20 mA current loops, and a "smart" valve positioner ensures the control valve operates correctly.
The fundamental building block of any industrial control system is the
control loop, which controls just one process variable. An example is
shown in the accompanying diagram, where the flow rate in a pipe is
controlled by a PID controller, assisted by what is effectively a
cascaded loop in the form of a valve servo-controller to ensure
correct valve positioning.
Some large systems may have several hundreds or thousands of control
loops. In complex processes the loops are interactive, so that the
operation of one loop may affect the operation of another. The system
diagram for representing control loops is a Piping and instrumentation
Commonly used controllers are programmable logic controller (PLC),
Distributed Control System
Example of level control system of a continuous stirred-tank reactor. The flow control into the tank would be cascaded off the level control.
A further example is shown. If a control valve were used to hold level in a tank, the level controller would compare the equivalent reading of a level sensor to the level setpoint and determine whether more or less valve opening was necessary to keep the level constant. A cascaded flow controller could then calculate the change in the valve position. Economic advantages The economic nature of many products manufactured in batch and continuous processes require highly efficient operation due to thin margins. The competing factor in process control is that products must meet certain specifications in order to be satisfactory. These specifications can come in two forms: a minimum and maximum for a property of the material or product, or a range within which the property must be. All loops are susceptible to disturbances and therefore a buffer must be used on process set points to ensure disturbances do not cause the material or product to go out of specifications. This buffer comes at an economic cost (i.e. additional processing, maintaining elevated or depressed process conditions, etc.). Process efficiency can be enhanced by reducing the margins necessary to ensure product specifications are met. This can be done by improving the control of the process to minimize the effect of disturbances on the process. The efficiency is improved in a two step method of narrowing the variance and shifting the target. Margins can be narrowed through various process upgrades (i.e. equipment upgrades, enhanced control methods, etc.). Once margins are narrowed, an economic analysis can be done on the process to determine how the set point target is to be shifted. Less conservative process set points lead to increased economic efficiency. Effective process control strategies increase the competitive advantage of manufacturers who employ them. List of techniques and mechanisms used
Controller (control theory)
Distributed control system
Flow control valve
Fuzzy control system
Linear parameter-varying control
Model predictive control
Piping and instrumentation diagram
Programmable Logic Controller
Regulator (automatic control)
Simatic S5 PLC
Sliding mode control
The Michigan Chemical Engineering Process Dynamics and Controls Open
PID Control Theory and Best Practices
Process Control Equipment Video Tutorials
^ a b c Young, William Y; Svrcek, Donald P; Mahoney, Brent R (2014). "1: A Brief History of Control and Simulation". A Real Time Approach to Process Control (3 ed.). Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. pp. 1–2. ISBN 978-1119993872. ^ Minorsky, Nicolas (1922). "Directional stability of automatically steered bodies". J. Amer. Soc. Naval Eng. 34 (2): 280–309. doi:10.1111/j.1559-3584.1922.tb04958.x. ^ Bennett 1993, p. 67 ^ Bennett, Stuart (1996). "A brief history of automatic control" (PDF). IEEE Control Systems Magazine. IEEE. 16 (3): 17–25. doi:10.1109/37.506394. ^ Bequette, B. Wayne (2003). Process control: Modeling, Design, and Simulation (Prentice-Hall International series in the physical and chemical engineering science. ed.). Upper Saddle River, N.J.: Prentice Hall PTR. pp. 57–58. ISBN 978-0133536409. ^ a b c d Smith, C L (March 2017). "Process Control for the Process Industries - Part 2: Steady State Characteristics". Chemical Engineering Progress: 67