Process control is the science of maintaining key process parameters in manufacturing processes at their desired set points. Process controls can tune any controllable element of a process including heating and cooling, material flow rates, and pressure, and automatically make adjustments to system conditions to correct any measured deviations back to their expected values, also known as setpoints. The figure below shows an example of the logic used by a PID (proportional-integral-derivative) controller to make adjustments to a process. How this logic works is explained in detail in the section Feedback Control Modes.
Process control systems typically consist of a combination of measurement devices, final control elements, and computers. Basic process control elements include flow meters, pressure and temperature sensors, control valves, and other tools that measure process parameters or directly affect the process. Process control systems are programmable and can operate autonomously or respond to operator input. Higher-order control elements, such as input/output modules and supervisory computers, adjust the process parameters automatically when they see a process variable deviate from its set point.
Process controls are an added layer of safety to mitigate or prevent incidents such as overpressure, fires and explosions, and runaway reactions. They can also mitigate the effects of external disturbances such as temperature deviations. The level of automation process control allows for process optimization beyond what is possible with strictly manual control. This overview describes types of manufacturing processes and various types of control schemes, from controls of individual units to Distributed Control Systems (DCS) that control an entire plant.
Numerous forms of process control exist in the chemical industry. More simple processes may implement more basic forms of control such as feedback or PI control, while large, complex plants may integrate hundreds or thousands of control strategies into a distributed control system. Process control schemes exist for both economic optimization and as a safety measure, as controllers can be programmed to alert operators when process variables reach unsafe values. Controllers can be tuned for both continuous and batch processes, and different controllers are more useful in different process schemes.
Manufacturing processes can be categorized as a batch, continuous, or discrete. In a batch process, a fixed quantity of raw material is converted to a batch of the product, with definite beginning and endpoints. In a continuous process, on the other hand, continuous input flows are converted to outputs in a process that could operate 24 hours per day. In discrete processes, parts are assembled to produce individual items.
Batch manufacturing is typical for moderate quantities of products; foods and pharmaceuticals are typical industries that employ batch processing. Continuous manufacturing is designed for high-volume production; industrial chemicals and plastics typically employ continuous manufacturing. Discrete manufacturing is used in the production of items such as phones, cars, or other solid items. Each type of manufacturing requires a different form of process control.
Two typical forms of process control systems are single input – single output (SISO) and multiple-input – multiple-output (MIMO). SISO is the simplest form of a control system: A measurement tool measures one process parameter, such as the system temperature, then converts it to an electrical signal. The actuator reads that signal to adjust a different process parameter, such as a coolant flow rate. Single-unit operations typically use SISO control schemes because the additional computing power required for a MIMO system is unnecessary. MIMO devices are more complex, as they are programmed to read numerous process parameters and adjust numerous operating parameters as needed. Distributed control systems (DCS), described in more detail below, consist of many MIMO devices spread across numerous units.
Control elements are typically described as feedback, feedforward, or a combination of both. Feedback control involves measuring the exit variable you wish to control, then manipulating input variables accordingly. In feedforward control, one measures a disturbance variable instead of the controlled variable to mitigate the effects of external factors, such as outdoor temperature, that may affect a process.
For example, consider the mixer shown below, in which two streams are combined in a ratio set by the process control system to create an exit stream with the desired composition:
In feedback control, shown first, one would measure the composition of the output stream, convert that measurement to an electrical signal, then an actuator would read that signal and adjust the flow rate of one of the input streams as needed. A feedforward control loop, shown second, would measure the flow rate of the second input stream, then send an electrical signal to an actuator that would adjust the flow rate of the input stream to correct for any deviations and produce the desired output composition. A combination feedback-feedforward control loop consists of a device to measure the composition of an output stream and a ratio controller for two separate input flows that adjust both variables.
The main advantage of feedback control is that it is more practical and easier to implement than feedforward control. Disturbances are typically more difficult to measure than outputs, and feedforward control schemes only measure one disturbance. Feedforward control also requires a mathematical model of how each disturbance affects the output variable. Feedback control, on the other hand, measures the process output, which is affected by every process disturbance. The main advantage of feedforward control is that it measures disturbances before they can affect the output. In essence, feedforward control prevents deviations from the setpoint while feedback control responds to such deviations. Feedforward control is essential for time-sensitive operations where it is critical to prevent deviations from the setpoint before they occur.
Feedforward control is normally impractical to implement by itself, as it is impossible to measure every possible disturbance for a process, and disturbances can change over time. As such, hybrid feedback-feedforward control systems are typically used when a process requires feedforward control. The most critical disturbances are feedforward controlled, while the feedback control loop from the product stream accounts for other disturbances.
Alarms and trips are basic safety features that make use of process controls. An alarm is an audio-visual cue to a control room operator that an abnormal deviation has occurred. Operators will then follow standard operating procedures (SOPs) to respond to the alarm. If the deviation exceeds a secondary limit or continues for an extended period of time, a safety trip will occur. Safety trips result in an automated response, such as pressure relief or emergency shutdown.
For example, if a set point for an exit flow rate is 300 L/min, an alarm may be configured to activate if a flowmeter reads above 330 or below 270 L/min. An alarm will notify the operators that a significant but not critical deviation has occurred and should be handled immediately. A safety trip sensor may activate if the reading exceeds 360 L/min or falls below 240 L/min. The emitted signal would automatically adjust a final control element such as a valve, coolant stream, or motor to correct the discrepancy. The figure below shows an ultrasonic flowmeter, a device that uses sonic waves to measure fluid flow rates. For more information about how this instrument works, see the velocity flowmeters article.
The three basic feedback control modes are proportional, integral, and derivative control. A feedback controller will incorporate one or more of those modes depending on the required degree of responsiveness to an error, where an error is defined as the difference between the measurement of the controlled variable and its setpoint. In proportional control, the controller output is proportional to the error, in integral control the controller output is proportional to the integral of the error over time, and in derivative control, the controller output is proportional to the derivative of the error with time. Feedback controllers may involve any of these control modes, but virtually all feedback controllers incorporate proportional control and the most common forms are Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers, which involve combinations of the various modes of control.
The controller output is a linear function of the error signal. The setpoint is pre-determined by the operator, then the sensor measures the output and compares it to the set point. The controller uses a gain constant that denotes the ratio of output to input variables. e.g. for each degree Celsius over a setpoint temperature, increase the coolant flow rate by 5 L/min. A proportional controller with a high gain will yield a fast response but can lead to instability because of the severity of the response to small errors. A low gain is more likely to maintain process stability but may not be sensitive enough to eliminate disturbances.
The most significant disadvantage of proportional-only control is there is no way to eliminate the error stemming from a setpoint change. Error stemming from a setpoint change is referred to as a steady-state error because the error is the result of calibration inaccuracies and not the result of a process disturbance. Proportional-only control is desirable for processes in which controlling steady-state error is less important. For example, a level controller for a tank may be more concerned with preventing overflow than maintaining the level at an exact set point.
The controller output is proportional to the integral of the error signal over a predetermined period of time. Essentially, the controller analyzes past errors to determine the output signal. Unlike proportional control, integral control takes into account the duration of the error and can eliminate steady-state error. Because integral control takes into account all errors over a period of time prior to producing a control signal, it is a much slower control method than proportional control. In addition, since integral control responds to the sum of all disturbances and setpoint changes, it runs the risk of overshooting the setpoint when producing its control signal. For these reasons, integral control is rarely used by itself; it is almost always used in conjunction with proportional control. Most industrial processes need to account for steady-state error; it is not typically tolerable for a process to produce a constant, unchecked error. As such, integral control is widely used in industrial control processes.
The controller output is proportional to the derivative of the error signal over a period of time. The purpose of derivative control is to predict future errors. Neither proportional nor integral control responds to immediate changes in a variable over a short period of time. Proportional control reacts to a deviation without regard to duration, while integral control only responds to changes over a long period of time. Derivative control responds to destabilizing disturbances and often offsets the destabilizing effects of integral control. Derivative control is never used by itself; it is always used in conjunction with proportional control and is often used in conjunction with integral control.
A distributed control system integrates many elements to control a whole section of the plant, composed of many processes. Before the distributed control system (DCS) was invented, every process within a plant was controlled by the single mainframe computer. This presented a safety concern because one technological failure could result in all processes losing control. A DCS is a hierarchical network of computerized process control elements. A typical DCS is both geographically and logically distributed, meaning that multiple control computers exist and are located throughout the plant.
The major priorities in setting up an automated Distributed Control System (DCS) are safety, production rate, product quality, cost, and stability. A DCS improves process safety by constantly monitoring process parameters such as temperature, pressure, or pH, to ensure they stay within their safe operating window. Failure to stay within these limits could lead to fire, explosion, or damage if left unchecked. Because a DCS can measure process parameters dozens of times per second, a DCS can alert operators and attempt to control problems much more quickly than a human can. A DCS can also constantly monitor product volume and adjust process parameters if production demands are not met or if the product is not sufficiently pure. A DCS can also be programmed to optimize process variables, such as minimizing energy usage and raw material demands. Furthermore, the automated nature of the DCS allows human operators to remotely operate a chemical process, which is particularly important for hazardous processes.
A DCS tends to be used on continuous, safety-critical processes because of the expense associated with numerous layers of control. A DCS will typically have four or five levels of control:
Programmable logic controllers (PLC) differ from other controllers in that they primarily deal with digital (or binary) input rather than analog input. Because a PLC system is less expensive than a DCS, plants that can achieve satisfactory control with solely PLCs typically opt to do so, but they can also be used in conjunction with a DCS. While a PID controller will react to an analog electrical signal that can vary between 4 and 20 mA, a PLC will respond to numerous digital inputs. PLCs can handle thousands of digital inputs and outputs. These digital inputs may include whether a valve is open or shut or whether a motor is on or off. Particularly large-scale PLCs will double as analog controllers and provide PID control to processes as well. Much like a PID controller, a PLC will send an output signal to an actuator to adjust process variables depending on how it is programmed.
The process control strategies in previous sections are typically used in continuous processes. Batch processes are different than continuous processes because the process rarely reaches a steady state. Furthermore, since batch processes typically involve a much smaller quantity of product than continuous processes, equipment and control mechanisms are selected so that many types of batch processes can be performed using the same equipment. A programmable logic controller (PLC), is often used to account for variations in set points depending on the product being made.
The process control elements mentioned so far adjust process parameters to minimize variation from a set point. Real-time optimization (RTO) is a strategy to adjust setpoints based on process data. RTO devices are higher-order than the DCS devices discussed above, and RTO takes place over a longer period of time than other process control strategies. While sensors collect data multiple times per second and actuators can adjust process variables multiple times per minute, set-point changes typically occur on the order of hours to days. RTO can be thought of as controlling a distributed control system, with the controlled variable being the DCS set points.
RTO is typically used to maximize the economic potential of a process. Every process variable has a maximum and minimum value; for example, pumps have a maximum throughput, and tanks have a maximum storage capacity. Variables are sorted into those that contribute to the process profit streams (end products), cost streams (raw materials), and the operating costs of the equipment. Operators then program the optimizer to maximize the profit stream while minimizing the cost streams. RTO is most effective when as many variables as possible are considered; large chemical plants take into account tens or even hundreds of thousands of input variables and optimize dozens of set points.
Because RTO implements so many variables, the models for each variable need to be relatively simple so the computer system can function quickly. RTO optimizers are typically linear, and the profit and cost streams are typically simple linear combinations of those models. More sophisticated models, such as quadratic or exponential, may reflect the actual process conditions more accurately, but the computing time required to optimize the setpoints would be too high to be effective.
An industrial process control or simply process control in continuous production processes is a discipline that uses industrial control systems and control theory 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 automotive, mining, dredging, oil refining, pulp and paper manufacturing, chemical processing and power generating plants.[1]
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 industrial 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.[2]
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.
History
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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.[3]
Later process controls inventions involved basic physics principles. In 1620, Cornelis 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.[3] 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.[3]
For continuously variable process control it was not until 1922 that a formal control law for what we now call PID control or three-term control was first developed using theoretical analysis, by Russian American engineer Nicolas Minorsky.[4] Minorsky was researching and designing automatic ship steering for the US Navy and based his analysis on observations of a helmsman. He noted the helmsman steered the ship based not only on the current course error, but also on past error, as well as the current rate of change;[5] this was then given a mathematical treatment by Minorsky.[6] His goal was stability, not general control, which simplified the problem significantly. While proportional control provided stability against small disturbances, it was insufficient for dealing with a steady disturbance, notably a stiff gale (due to steady-state error), which required adding the integral term. Finally, the derivative term was added to improve stability and control.
Development of modern process control operations
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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.Process control of large industrial plants has evolved through many stages. Initially, control would be from panels local to the process plant. However this required a large manpower resource to attend to these dispersed panels, and there was no overall view of the process. The next logical development was the transmission of all plant measurements to a permanently-staffed central control room. Effectively this was the centralization of all the localized panels, with the advantages of lower manning levels and easier overview of the process. Often the controllers were behind the control room panels, and all automatic and manual control outputs were transmitted back to plant. However, whilst providing a central control focus, this arrangement was inflexible as each control loop had its own controller hardware, and continual operator movement within the control room was required to view different parts of the process.
With the coming of electronic processors and graphic displays it became possible to replace these discrete controllers with computer-based algorithms, hosted on a network of input/output racks with their own control processors.[7] These could be distributed around plant, and communicate with the graphic display in the control room or rooms. The distributed control system (DCS) was born.
The introduction of DCSs allowed easy interconnection and re-configuration of plant controls such as cascaded loops and interlocks, and easy interfacing with other production computer systems. It enabled sophisticated alarm handling, introduced automatic event logging, removed the need for physical records such as chart recorders, allowed the control racks to be networked and thereby located locally to plant to reduce cabling runs, and provided high level overviews of plant status and production levels.
Hierarchy
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Functional levels of a manufacturing control operationThe 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 industrialized 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
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To determine the fundamental model for any process, the inputs and outputs of the system are defined differently than for other chemical processes.[8] 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. The state variable (x) 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) is a specified variable that commonly include flow rates.
It is 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) 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) is the metric used to determine the behavior of the system. The control output can be classified as measured, unmeasured, or unmonitored.
Types
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Processes can be characterized as batch, continuous, or hybrid.[9] Batch 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).
A continuous 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 PID controller A PID Controller includes proportional, integrating, and derivative controller functions.
Applications having elements of batch and continuous process control are often called hybrid applications.
Control loops
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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 diagram.
Commonly used control systems include programmable logic controller (PLC), Distributed Control System (DCS) or SCADA.
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
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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.[10] 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.[10] 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.[10] 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.[10] Effective process control strategies increase the competitive advantage of manufacturers who employ them.
See also
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References
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Further reading
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