Process Control Systems

Types of Processes: Process control is the automated control of a process. Such systems typically deal with analog signals from sensors. The ability of a PLC to perform math functions and utilize analog signals makes it ideally suited for this type of control.

Manufacturing is based on a series of processes being applied to raw materials. Typical applications of process control systems include automobile assembly, petrochemical production, oil refining, power generation, and food processing.

A continuous process is one in which raw materials enter one end of the system and the finished product comes out the other end of the system; the process itself runs continuously. Figure 1 shows a continuous process used in an automotive engine assembly line.

Parts are mounted sequentially, in an assembly-line fashion, through a series of stations. Assembly and adjustments are carried out by both automated machine and manual operations.

In batch processing, there is no flow of product material from one section of the process to another. Instead, a set amount of each of the inputs to the process is received in a batch, and then some operation is performed on the batch to produce a product. Products produced using the batch process include food, beverages, pharmaceutical products, paint, and fertilizer.

Figure 2 shows an example of a batch process. Three ingredients are mixed together, heated, and then stored. Recipes are the key to producing batches as each batch may have different characteristics by design.

Discrete manufacturing is characterized by individual or separate unit production. With this manufacturing process, a series of operations produces a useful output product. Discrete manufacturing systems typically deal with digital inputs to PLCs that cause motors and robotic devices to be activated. The work piece is normally a discrete part that must be handled on an individual basis. Making car interiors, is one example of discrete manufacturing.

Possible control configurations include individual, centralized, and distributed. Individual control is used to control a single machine. This type of control does not normally require communication with other controllers.

Figure 3 shows an individual control application for a cut to length operation. The operator enters the feed length and batch count via the interface control panel and then presses the start button to initiate the process. Stock lengths vary so the operator needs to select the length and the number of pieces to be cut.

Centralized control is used when several machines or processes are controlled by one central controller. The control layout uses a single, large control system to control many diverse manufacturing processes and operations, as illustrated in Figure 4.

The main features of centralized control can be summarized as follows:

Each individual step in the manufacturing process is handled by a central control system controller.
No exchange of controller status or data is sent to other controllers.
If the main controller fails, the whole process stops.

A distributive control system (DCS) is a network-based system. Distributive control involves two or more PLCs communicating with each other to accomplish the complete control task, as illustrated in Figure 5.

Each PLC controls different processes locally and the PLCs are constantly exchanging information through the communications link and reporting on the status of the process. The main features of a distributive control system can be summarized as follows:

Distributive control permits the distribution of the processing tasks among several controllers.
Each PLC controls its associated machine or process.
High-speed communication among the computers is done through CAT-5 or CAT-6 twisted pair wires, single coaxial cables, fiber optics, or the Ethernet.
Distributive control drastically reduces field wiring and heightens performance because it places the controller and I/O close to the machine process being controlled.
Depending on the process, one PLC failure would not necessarily halt the complete process.
DCS is supervised by a host computer that may perform monitoring/supervising functions such as report generation and storage of data.

Structure of Process Control Systems

Process control normally applies to the manufacturing or processing of products in industry. In the case of a programmable controller, the process or machine is operated and supervised under the control of the user program. The major components of a process control system include the following:

Sensors

Provide inputs from the process and from the external environment
Convert physical information such as pressure, temperature, flow rate, and position into electrical signals

Human Machine Interface (HMI)

Allows human inputs through various types of programmed switches, controls, and keypads to set up the starting conditions or alter the control of a process

Signal Conditioning

Involves converting input and output signals to a usable form
May include signal-conditioning techniques such as amplification, attenuation, filtering, scaling, A/D and D/A converters

Actuators

Convert system output electrical signals into physical action
Process actuators that include flow control valves, pumps, positioning drives, variable speed drives, clutches, brakes, solenoids, stepping motors, and power relays

Controller

Makes the system’s decisions based on the input signals
Generates output signals that operate actuators to carry out the decisions

Human machine interface (HMI) equipment provides a control and visualization interface between a human and a process. HMIs allow operators to control, monitor, diagnose, and manage the application. Depending on the requirements and complexity of the process, the operator may be required to:

Stop and start the process.
Operate the controls and make the adjustments required for the process and monitor its progress.
Detect abnormal situations and undertake corrective action.

Graphic HMI terminals offer electronic interfacing in a wide variety of sizes and configurations. They replace traditional wired panels with a touch screen with graphical representations of switches and indicators. Types of graphical display screens include the following:

Operational Summary —used to monitor the process.
Configuration/Setup —textual in nature used to detail process parameters.
Alarm Summary —provides a list of time-stamped active alarms.
Event History —presents a time-stamped list of all significant events that have occurred in the process.
Trend Values —displays information on process variables, such as flow, temperature, and production rate, over a period of time.
Manual Control —generally available only to maintenance personnel and meant to bypass parts of the automatic control system.
Diagnostics —used by maintenance personnel to diagnose equipment failures.

Graphic terminals come fully packaged with hardware, software, and communications. In general, the tasks required to develop an HMI application include:

Establish a communication link with the PLCs
Create the tag addresses database
Edit and create graphical objects on the screens
Animate the objects

Closed Loop Process Control Systems

Most control systems are closed loop in that they utilize feedback in which the output of a process affects the input control signal. A closed-loop system measures the actual output of the process and compares it to the desired output. Adjustments are made continuously by the control system until the difference between the desired and actual output falls within a predetermined tolerance.

Figure 6 illustrates an example of a closed-loop control system. The actual output is sensed and fed back to be subtracted from the set-point input that indicates what output is desired. If a difference occurs, a signal to the controller causes it to take action to change the actual output until the difference is 0. The operation of the component parts are as follows:

Set-point —The input that determines the desired operating point for the process.
Process Variable —Refers to the feedback signal that contains information about the current process status.
Error Amplifier —Determines whether the process operation matches the set-point. The magnitude and polarity of the error signal will determine how the process will be brought back under control.
Controller —Produces the appropriate corrective output signal based on the error signal input.
Output Actuator —The component that directly affects a process change. Examples are motors, heaters, fans, and solenoids.

The process shown in Figure 7 is an example of a closed-loop continuous control process used to automatically fill box containers to a specified weight of detergent. An empty box is moved into position and filling begins. The weight of the box and contents is monitored. When the actual weight equals the desired weight, filling is halted.

Operation and block diagrams for the container-filling process are shown in Figure 8. The operation of the process can be summarized as follows:

A sensor attached to the scale weighing the container generates the voltage signal or digital code that represents the weight of the container and contents.
The sensor signal is subtracted from the voltage signal or digital code that has been input to represent the desired weight.
As long as the difference between the input signal and feedback signal is greater than 0, the controller keeps the solenoid gate open.
When the difference becomes 0, the controller outputs a signal that closes the gate.

Virtually all feedback controllers determine their output by observing the error between the set-point and a measurement of the process variable. Errors can occur when an operator changes the set-point or when a disturbance or a load on the process changes the process variable. The controller’s role is to eliminate the error automatically.

On/Off Control

With on/off controllers the final control element is either on or off—one for the occasion when the value of the measured variable is above the set-point and the other for the occasion when the value is below the set-point. The controller will never keep the final control element in an intermediate position. Controlling activity is achieved by the period of on-off cycling action.

Figure 9 shows a system using on/off control in which a liquid is heated by steam. The operation of the process can be summarized as follows:

If the liquid temperature goes below the set-point, the steam valve opens and the steam is turned on.
When the liquid temperature goes above the set-point, the steam valve closes and the steam is shut off.
The on/off cycle will continue as long as the system is operating.

Figure 10 illustrates the control response for an on/ off temperature controller. The action of the control response can be summarized as follows:

The output turns on when the temperature falls below the set-point and turns off when the temperature reaches the set-point.
Control is simple, but overshoot and cycling about the set-point can be disadvantageous in some processes.
The measured variable will oscillate around the setpoint at an amplitude and frequency that depend on the capacity and time response of the process.
Oscillations may be reduced in amplitude by increasing the sensitivity of the controller. This increase will cause the controller to turn on and off more often, a possibly undesirable result.
On/off control is used when a more precise control is unnecessary.

A deadband is usually established around the set-point. The deadband of the controller is usually a selectable value that determines the error range above and below the set-point that will not produce an output as long as the process variable is within the set limits.

The inclusion of deadband eliminates any hunting by the control device around the set-point. Hunting occurs when minor adjustments of the controlled position are continually made due to minor fluctuations.

PID Control

Proportional controllers are designed to eliminate the hunting or cycling associated with on/off control. They allow the final control element to take intermediate positions between on and off. Proportioning action permits analog control of the final control element to vary the amount of energy to the process, depending on how much the value of the measured variable has shifted from the desired value.

A proportional controller allows tighter control of the process variable because its output can take on any value between fully on and fully off, depending on the magnitude of the error signal.

Figure 11 shows an example of a motor-driven analog proportional control valve used as a final control element. The action of the control valve actuator can be summarized as follows:

The actuator receives an input current between 4 mA and 20 mA from the controller.
In response, it provides linear control of the valve.
A value of 4 mA corresponds to a minimum value opening (often 0) and 20 mA corresponds to a maximum value opening (full scale).
The 4 mA lower limit allows the system to detect opens. If the circuit is open, 0 mA would result, and the system can issue an alarm.
Because the signal is a current, it is unaffected by reasonable variations in connecting wire resistance and is less susceptible to noise pickup from other signals than is a voltage signal.

Proportioning action can also be accomplished by turning the final control element on and off for short intervals. This time proportioning (also known as pulse width modulation) varies the ratio of on time to off.

Figure 12 shows an example of time proportioning used to produce varying wattage from a 200 watt heater element as follows:

To produce 100 watts the heater must be on 50% of the time.
To produce 50 watts the heater must be on 25% of the time.
To produce 25 watts the heater must be on 12.5% of the time.

Proportioning action occurs within a proportional band around the set-point. The table of Figure 13 is an example of the proportional band for a heating application with a set-point of 500°F and a proportional band of 80°F (±40°F). Proportioning action can be summarized as follows:

Outside proportional band, the controller functions as an on/off unit, with the output either fully on (below the band) or fully off (above the band).

Within the proportional band the output is turned on and off in the ratio of the measurement difference from the set-point.
At the set-point (the midpoint of the proportional band), the output on:off ratio is 1:1; that is, the on time and off time are equal.
If the temperature is further from the set-point, the on and off times vary in proportion to the temperature difference.
If the temperature is below the set-point, the output will be on longer; if the temperature is too high, the output will be off longer.

In theory, a proportional controller should be all that is needed for process control. Any change in system output is corrected by an appropriate change in controller output.

Unfortunately, the operation of a proportional controller leads to a steady-state error known as offset, or droop. This steady-state error is the difference between the attained value of the controller and the required value that results in an offset signal that is slightly lower than the set-point value, as illustrated in Figure 14.

Depending on the PLC application, this offset may or may not be acceptable. The process of Figure 15 illustrates what effect a proportional control steady-state error might have on a tank-filling operation.

It may require an operator to make a small adjustment (manual reset) to bring the controlled variable to the set-point on initial start-up, or whenever the process conditions change significantly. The operation can be summarized as follows:

When valve B opens liquid flows out and the level in the tank drops.
This causes the fl oat to lower, opening valve A and allowing more liquid in.
This process continues until the level drops to a point at which the fl oat is low enough to open valve A, thus allowing the same input flow as output flow.
Due to the steady-state error, the level will stabilize at a new lower level, not at the desired set-point.

Proportional control is often used in conjunction with integral control and/or derivative control.

The integral action, sometimes termed reset action, responds to the size and time duration of the error signal. An error signal exists when there is a difference between the process variable and the set-point, so the integral action will cause the output to change and continue to change until the error no longer exists. Integral action eliminates steady-state error. The amount of integral action is measured as minutes per repeat or repeats per minute, which is the relationship between changes and time.
The derivative action responds to the speed at which the error signal is changing—that is, the greater the error change, the greater the correcting output. The derivative action is measured in terms of time.

Proportional plus integral (PI) control combines the characteristics of both types of control. A step change in the set-point causes the controller to respond proportionally, followed by the integral response, which is added to the proportional response. Because the integral mode determines the output change as a function of time, the more integral action found in the control, the faster the output changes. This action can be summarized as follows:

To eliminate the offset error, the controller needs to change its output until the process variable error is zero.
Reset integral control action changes the controller output by the amount needed to drive the process variable back to the set-point value.
The new equilibrium point after reset action is at point “C.”
Since the proportional controller must always operate on its proportional band, the proportional band must be shifted to include the new point “C.”
A controller with reset integral control does this automatically.

Rate action (derivative control) acts on the error signal just like reset does, but rate action is a function of the rate of change rather than the magnitude of error. Rate action is applied as a change in output for a selectable time interval, usually stated in minutes. Rate-induced change in controller output is calculated from the derivative of the error. Input change, rather than proportional control error change, is used to improve response.

Rate action quickly positions the output, whereas proportional action alone would eventually position the output. In effect, rate action puts the brakes on any offset or error by quickly shifting the proportional band.

Proportional plus derivative (PD) control is used in process control systems with errors that change very rapidly. By adding derivative control to proportional control, we obtain a controller output that responds to the error’s rate of change as well as to its magnitude.

PID control is a feedback control method that combines proportional, integral, and derivative actions. The proportional action provides smooth control without hunting. The integral action automatically corrects offset. The derivative action responds quickly to large external disturbances.

The PID controller is the most widely used type of process controller. When combined into a single control loop the proportional, integral and derivative modes complement each other to reduce the system error to zero faster than any other controller.

Figure 16 shows the block diagram of a PID control loop, the operation of which can be summarized as follows:

During setup, the set-point, proportional band, reset (integral), rate (derivative), and output limits are specified.
All these can be changed during operation to tune the process.
The integral term improves accuracy, and the derivative reduces overshoot for transient upsets.
The output can be used to control valve positions, temperature, flow metering equipment, and so on.
PID control allows the output power level to be varied.
As an example, assume that a furnace is set at 50°C.
The heater power will increase as the temperature falls below the 50°C set-point.
The lower the temperature the higher the power.
PID has the effect of gently turning the power down as the signal gets close to the set-point.

The long-term operation of any system, large or small, requires a mass-energy balance between input and output. If a process were operated at equilibrium at all times, control would be simple. Because change does occur, the critical parameter in process control is time, that is, how long it takes for a change in any input to appear in the output. System time constants can vary from fractions of a second to many hours.

The PID controller has the ability to tune its control action to specific process time constants and therefore to deal with process changes over time. PID control changes the amount of output signal in a mathematically specified way that accounts for the amount of error and the rate of signal change.

Either programmable controllers can be fitted with input/output modules that produce PID control, or they will already have sufficient mathematical functions to allow PID control to be carried out. PID is essentially an equation that the controller uses to evaluate the controlled variable.

Figure 17 illustrates how a programmable logic controller can be used in the control of a PID loop. The operation of the PID loop can be summarized as follows:

The PLC program compares the feedback to the setpoint and generates an error signal.
The error is examined by the PID loop calculation in three ways: with proportional, integral, and derivative methodology.
The controller then issues an output to correct for any measured error by adjustment of the position of the variable flow outlet valve.

The response of a PID loop is the rate at which it compensates for error by adjusting the output. The PID loop is adjusted or tuned by changing the proportional gain, the integral gain, and/or the derivative gain. A PID loop is normally tested by making an abrupt change to the setpoint and observing the controller’s response rate. Adjustments can then be made as follows:

As the proportional gain is increased, the controller responds faster.
If the proportional gain is too high, the controller may become unstable and oscillate.
The integral gain acts as a stabilizer.
Integral gain also provides power, even if the error is zero (e.g., even when an oven reaches its setpoint, it still needs power to stay hot).
Without this base power, the controller will droop and hunt for the set-point.
The derivative gain acts as an anticipator.
Derivative gain is used to slow the controller down when change is too fast.

Basically, PID controller tuning consists of determining the appropriate values for the gain (proportional band), rate (derivative), and reset time (integral) tuning parameters (control constants) that will give the control required.

Depending on the characteristics of the deviation of the process variable from the set-point, the tuning parameters interact to alter the controller’s output and produce changes in the value of the process variable. In general, three methods of controller tuning are used:

Manual

The operator estimates the tuning parameters required to give the desired controller response.
The proportional, integral, and derivative terms must be adjusted, or tuned, individually to a particular system using a trial-and-error method.

Semiautomatic or Autotune

The controller takes care of calculating and setting PID parameters.

Measures sensor output
Calculates error, sum of error, rate of change of error
Calculates desired power with PID equations
Updates control output

Fully Automatic or Intelligent

This method is also known in the industry as fuzzy logic control.
The controller uses artificial intelligence to readjust PID tuning parameters continually as necessary.
Rather than calculating an output with a formula, the fuzzy logic controller evaluates rules. The first step is to “fuzzify” the error and change-in-error from continuous variables into linguistic variables, like “negative large” or “positive small.” Simple if-then rules are evaluated to develop an output. The resulting output must be de-fuzzified into a continuous variable such as valve position.

The PID programmable controller output instruction uses closed-loop control to automatically control physical properties such as temperature, pressure, liquid level, or flow rate of process loops.

Figure 18 shows the PID output instruction and setup screen associated with the Allen-Bradley SLC 500 instruction set. The PID instruction is straightforward: it takes one input and controls one output.

Normally, the PID instruction is placed on a rung without conditional logic. The output remains at its last value when the rung goes false. A summary of the basic information that is entered into the instruction is as follows:

Control Block —File that stores the data required to operate the instruction.
Process Variable —The element address that stores the process input value.
Control Variable —The element address that stores the output of the PID instruction.
Setup Screen —Instruction on which you can double-click to bring up a display that prompts you for other parameters you must enter to fully program the PID instruction.

Motion Control

A motion control system provides precise positioning, velocity, and torque control for a wide range of motion applications. PLCs are ideally suited for both linear and rotary motion control applications.

Pick and Place machines are used in the consumer products industry for a wide variety of product transfer applications. The machine takes a product from one point to another. One example is the transfer of a product to a moving conveyor belt.

A basic PLC motion control system consists of a controller, a motion module, a servo drive, one or more motors with encoders, and the machinery being controlled. Each motor controlled in the system is referred to as an axis of motion.

Figure 19 illustrates a bottle-filling motion control process. This application requires two axes of motion: the motor operating the bottle filler mechanism and the motor controlling the conveyor speed. The role of each control component can be summarized as follows:

Programmable Logic Controller

The controller stores and executes the user program that controls the process.
This program includes motion instructions that control axis movements.
When the controller encounters a motion instruction it calculates the motion commands for the axis.
A motion command represents the desired position, velocity, or torque of the servo motor at the particular time the calculations take place.

Motion Module

The motion module receives motion commands from the controller and transforms them into a compatible form the servo drive can understand.
In addition it updates the controller with motor and drive information used to monitor drive and motor performance.

Servo Drive

The servo drive receives the signal provided by the motion module and translates this signal into motor drive commands.
These commands can include motor position, velocity, and/or torque.
The servo drive provides power to the servo motors in response to the motion commands.
Motor power is supplied and controlled by the servo drive.
The servo drive monitors the motor’s position and velocity by use of an encoder mounted on the motor shaft. This feedback information is used within the servo drive to ensure accurate motor motion.

Servo Motor

The servo motors represent the axis being controlled.
The servo motors receive electrical power from their servo drive which determines the motor shaft velocity and position.
The filler motor must accelerate the filler mechanism in the direction the bottles are moving, match their speed, and track the bottles.
After the bottles have been filled, the filler motor has to stop and reverse direction to return the filler mechanism to the starting position to begin the process again.

A robot is simply a series of mechanical links driven by servo motors. The basic industrial robot widely used today is an arm or manipulator that moves to perform industrial operations.

Figure 20 illustrates the motion of a six-axis robot arm. Each axis of the robot arm is fundamentally a closed-loop servo control system. The wrist is the name usually given to the last three joints on the robot’s arm. Going out along the arm, these wrist joints are known as the pitch joint, yaw joint, and roll joint.

There are two types of controller setups that can be used to control an industrial robot—PLC- and PC-based systems. Depending on the difficulty of the task the robotic system will be performing, you may need a PLC or just a robot controller.

Process Control Systems

Process Control Systems

Structure of Process Control Systems

Closed Loop Process Control Systems

On/Off Control

PID Control

Motion Control

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