Process control is a critical aspect of industrial systems, playing a pivotal role in maintaining efficiency and safety. It refers to regulating operations within an industrial process to ensure optimal performance and output. This is achieved by monitoring process variables such as temperature, pressure, flow rate, and level and adjusting parameters to maintain the desired output.
The importance of process control in industrial systems cannot be overstated. It is the backbone of any industrial operation, ensuring the processes run smoothly and efficiently. By maintaining the process variables within the desired range, process control helps to minimize waste, reduce downtime, and improve product quality.
The process control strategies range from simple feedback control loops to complex algorithms incorporating predictive models and artificial intelligence. The choice depends on the process's complexity and desired level of precision. Let’s understand the complexities of process control in industrial systems, including process dynamics, control strategies, and the different components involved.
The concept of process control has been around for centuries, with early instances dating back to the Industrial Revolution. During this period, the need for industrial process control emerged as industries sought increased efficiency and safety. The Industrial Revolution propelled advancements, culminating in Watt's flyball governor and Jacquard's programmable loom. Early process control methods were manual, requiring operators to monitor variables and adjust them. This was labor-intensive, error-prone, and often led to inefficiencies and safety issues.
The advent of automation in the 20th century marked a significant milestone in the evolution of process control. The introduction of automatic control systems, such as the PID (Proportional-Integral-Derivative) controller, revolutionized the field of process control. These systems could monitor process variables and automatically adjust the process parameters, eliminating the need for manual intervention and enhancing efficiency and safety in industrial operations.
The development of digital technology further propelled the evolution of process control. The introduction of computer-based control systems in the latter part of the 20th century brought about a new era in process control, known as the era of digital process control. These systems used computers for complex control tasks, improving precision and flexibility.
Today, process control systems utilize advanced technologies like AI and machine learning to enhance process performance, enabling process industries to achieve optimal operational efficiency, production processes, and safety.
The early process control methods were largely manual, relying on human operators to monitor and control the process variables. The seeds of process control were sown as early as the 3rd century BC with Ktesibios of Alexandria's invention of float valves regulating water levels in clocks. [1] Centuries later, 17th-century pioneers like Heron of Alexandria and Cornelis Drebbel employed water valves and bimetallic thermostats, respectively, laying the groundwork for future advancements.
James Watt's flyball governor (1769); Source: MakezineThe Industrial Revolution propelled process control forward. James Watt's flyball governor (1769) for steam engines marked a key milestone, introducing automatic feedback control for controlling the speed of steam engines. The governor applied feedback control by continually monitoring and adjusting engine speed based on desired value deviations. This was achieved by using a pair of rotating weights driven by the engine's output shaft. Centrifugal force adjusted steam supply to the engine based on its speed. The 19th century saw further inventions like Joseph Jacquard's punched card loom (1801) showcasing early programmable control.
The 20th century witnessed a paradigm shift. Electronic controllers and instrumentation emerged, paving the way for pneumatic and hydraulic control systems. The invention of feedback controllers like PID (proportional-integral-derivative) in the 1930s provided a robust theoretical framework. World War II accelerated developments, leading to the use of radars and analog computers in control systems.
While these early process control methods were effective in maintaining the process variables within a certain range, they had several limitations. They lacked precision and were susceptible to disturbances, leading to fluctuations in the process variables. Furthermore, they required constant monitoring and intervention by human operators, making them labor-intensive and prone to human error. These limitations prompted the development of automatic control systems, advancing process control significantly.
The evolution of process control systems has been significantly influenced by advancements in technology. Modern process control systems have come a long way from the early manual methods, offering a level of precision, flexibility, and automation that was unimaginable in the past.
One of the key advancements in modern process control systems is digital technology. The UNIVAC I (1951), the first commercial computer system, marked the integration of digital control. [2] Unlike the early analog systems, which used physical quantities to represent process variables, digital systems use binary numbers, allowing for more precise and efficient control. Digital control systems utilize computers to perform complex control tasks, outperforming analog counterparts in flexibility and adaptability.
For example, Programmable Logic Controllers (PLCs) are commonly used in modern industrial systems for process control. As computer-based devices, PLCs offer flexibility by allowing changes to the control strategy without physical modifications. This makes them suitable for complex industrial processes requiring high adaptability.
Modern process control systems incorporate advanced control techniques like PID control. Compared to simple on-off control, PID control loop enhances precision and stability by combining proportional, integral, and derivative actions to reduce error and ensure a stable response.
Advanced control strategies like model predictive control (MPC) and fuzzy logic enable complex process optimization. The integration of artificial intelligence (AI) and machine learning (ML) empowers systems to learn, adapt, and predict, paving the way for advanced process control.
In essence, modern process control systems represent a significant leap forward in terms of precision, flexibility, and automation. They leverage the power of digital technology and advanced control strategies to optimize process performance, offering a level of control that was unimaginable with the early manual methods.
Feedback and feedforward control are the two vital concepts in process control, ensuring stability and efficiency.
Feedback control is a dynamic process that involves continuously monitoring the output of a system and adjusting the input based on the deviation from the desired output. This concept is based on the error detection and correction principle, where the error is the difference between the actual output and the desired output. The controller uses this error to make necessary adjustments to the input, bringing the output closer to the desired value.
For example, a heating system with a desired temperature of 20°C uses a feedback control system to monitor and adjust the heat input based on the actual temperature. If the actual temperature is lower, the controller increases heat input, and if it's higher, the controller decreases heat input. This maintains the desired temperature despite external disturbances.
The key component of a feedback control system is the controller, which uses the error signal to make necessary adjustments to the input. The error signal is the difference between the actual output and the desired output, and it serves as the driving force for the control action. [3]
PID loop basic diagram; Source: setra.com
The effectiveness of a feedback control system depends on the accuracy and speed of the error detection and correction process. The controller must quickly detect deviations from the desired output and make necessary adjustments to the input to correct the error. This requires a high degree of precision and responsiveness, which is achieved through advanced control algorithms and high-speed processing capabilities.
One of the main advantages of feedback control is its ability to handle disturbances and uncertainties in the system. Since the control action is based on the actual output, the feedback control system can automatically compensate for any disturbances or changes, maintaining the output at the desired level. This makes feedback control particularly suitable for complex industrial control systems subject to a wide range of disturbances and uncertainties.
For example, in a chemical reactor, the reaction rate is influenced by several factors, such as temperature, pressure, and concentration of reactants. A feedback control system can continuously monitor the reaction rate and adjust the temperature, pressure, or reactant flow rate to maintain the desired reaction rate, despite any changes in the process conditions or disturbances in the system.
Feedforward control is another fundamental concept in process control, complementing the role of feedback control. Unlike feedback control, which reacts to deviations in the output, feedforward control is a proactive approach that anticipates changes in the output based on changes in the input or disturbances.
The principle behind feedforward control is the prediction of output deviations before they occur. This is achieved by measuring the changes in the input or disturbances that are known to affect the output, and making preemptive adjustments to the process to counteract these changes. This proactive approach allows the feedforward control system to maintain the output at the desired level, even in the face of disturbances or changes in the input.
For instance, consider a heating system where ambient temperature affects the heat input. A drop in the ambient temperature would cause a decrease in the heat input, leading to a decrease in the output temperature. A feedforward control system can measure the drop in the ambient temperature and increase the heat input in anticipation, thereby maintaining the output temperature at the desired level.
The effectiveness of a feedforward control system depends on the accuracy of the prediction and the speed of the control action. The system must be able to accurately predict the effect of the input changes or disturbances on the output, and make the necessary adjustments in a timely manner.
Feedforward control excels at handling disturbances that are hard to measure at the output. It uses input or disturbance measurements to counteract disturbances before they impact the output, making it suitable for processes with hard-to-measure outputs or fast disturbances.
However, feedforward control is not a standalone solution and is often used with feedback control to achieve optimal control performance. While feedforward control handles the disturbances that can be measured at the input, feedback control handles the disturbances that can only be detected at the output. Together, they provide a comprehensive control solution that ensures the stability and efficiency of the process.
Recommended Reading: Mastering PID Tuning: The Comprehensive Guide
A process control system is composed of several key components, each playing a crucial role in the overall functioning of the system. These components include the process, sensors, controllers, and actuators. [4]
Process is the system being controlled, such as a reactor or manufacturing line. Certain variables, such as temperature, pressure, or flow rate, need controlling to reach the desired output.
Sensors are the devices that measure the process variables and provide feedback to the controller. They are crucial for an effective control system, as they provide the data needed for informed decisions. The accuracy and reliability of these sensors are essential.
Controllers are the core of the control system that get sensor data, compare it to the setpoint, and decide on the control action to reduce error. They use control algorithms like PID control to make calculations.
Actuators implement control actions by adjusting process inputs to influence process variables. These can be valves, motors, heaters, or other devices.
Let’s understand the details of the major components of a process control system.
Controllers are a central component of any process control system. They are responsible for determining the control action needed to keep the process variables at their desired levels. Controllers receive the process variable data from the sensors, compare it with the desired setpoint, and calculate the error. They then use a control algorithm to determine the necessary control action to minimize this error.
Several types of controllers are used in process control systems, each with its own characteristics and applications. The most common type is the PID (Proportional-Integral-Derivative) controller, which uses a combination of proportional, integral, and derivative actions to control the process variable.
The proportional action of a PID controller adjusts the control action in proportion to the error. This means that the larger the error, the larger the control action. The integral action accumulates the error over real time and adjusts the control action accordingly, helping to eliminate steady-state error. The derivative action predicts the future behavior of the error based on its rate of change and adjusts the control action preemptively, providing stability to the system.
Controllers can be implemented as standalone devices, such as Programmable Logic Controllers (PLCs), or as software running on a computer. They can also be integrated into larger systems, such as Distributed Control Systems (DCS) or Supervisory Control and Data Acquisition (SCADA) systems, which provide centralized control and monitoring of large industrial processes.
The choice of controller and control algorithm depends on the specific requirements of the process, including the dynamics of the process, the precision required, and the presence of disturbances or uncertainties. The design and tuning of the controller is a critical aspect of process control, as it directly affects the performance of the system.
Sensors and transmitters are integral components of a process control system. They play a critical role in monitoring the process variables and providing the necessary data to the controller.
Sensors measure physical quantities such as temperature, pressure, flow rate, or level. They convert these physical quantities into electrical signals that the controller can process. The accuracy and reliability of the sensors are crucial for the effectiveness of the control system, as they directly affect the quality of the data that the controller uses to make decisions.
Pressure Sensor employed in an Industrial Process; Source: electronicdesign.com
Various types of sensors are used in process control, each designed to measure a specific process variable. For example, thermocouples and resistance temperature detectors (RTDs) are used to measure temperature; pressure transducers are used to measure pressure, and flow meters are used to measure flow rate. The choice of sensor depends on process requirements, including the range of the process variable, the required accuracy, and the environmental conditions.
Transmitters convert electrical signals from sensors into a standardized format suitable for transmission to controllers. Current transmitters, the most common type, convert sensor signals into a 4-20 mA current signal for accurate processing and long-distance transmission.
In addition to measuring the process variables, sensors and transmitters play a crucial role in safety. They can detect abnormal conditions, such as high temperature or pressure, and trigger safety mechanisms to prevent accidents. Therefore, the design and selection of sensors and transmitters is a critical aspect of process control, directly affecting the performance and safety of the system.
Control valves are a critical component of a process control system. They serve as the final control element, implementing the control action determined by the controller. Control valves adjust the flow of the process medium, such as gas, liquid, or steam, influencing the process variables such as pressure, temperature, or level.
Control valves have two main parts: the valve body and the actuator. The valve body provides the flow path for the process medium, including the valve seat and the valve plug, which moves to control the flow. The actuator is the device that moves the valve plug, and it can be powered by various means, such as pneumatic, hydraulic, or electric power.
The operation of a control valve is based on the control signal received from the controller. The controller calculates the necessary control action and sends a control signal to the actuator. The actuator then moves the valve plug to the desired position, adjusting the flow of the process medium.
Various types of control valves are used in process control, each with its own characteristics and applications. For example, globe valves are commonly used for applications that require precise flow control, while butterfly valves are used for applications that require large flow capacities. The choice of control valve depends on the specific requirements of the process, including the range of the process variable, the required accuracy, and the environmental conditions.
Control valves also play a crucial role in safety. They can be used to shut off the flow of the process medium in case of an emergency, preventing accidents. Therefore, the design and selection of control valves is a critical aspect of process control, directly affecting the performance and safety of the system.
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Process control strategies are the methodologies used to manage and regulate the operations of a process system. These strategies determine how the controller responds to the error between the setpoint and the process variable. The choice of strategy considers process nature, performance needs, and variable characteristics.
The On-Off control strategy is the simplest form of process control. In this strategy, the controller switches the actuator, such as a control valve or a heater, on or off based on the error. When the process variable is below the setpoint, the controller turns the actuator on, and when the process variable is above the setpoint, the controller turns the actuator off.
This strategy is more suitable for simple processes where precision is not critical. However, it can lead to oscillations around the setpoint, as the controller continually switches the actuator on and off. This is known as cycling, which can cause wear and tear on the actuator and instability in the process.
The On-Off control strategy is often used in systems where the process variable can only be in one of two states, such as a light switch that can be either on or off. It is also used in systems where the actuator can only be in one of two states, such as a valve that can be either fully open or fully closed. [5]
Despite its simplicity, the On-Off control strategy is not suitable for processes that require a smooth and precise control of the process variable. More advanced control strategies, such as PID control, are used for these processes.
The Proportional-Integral-Derivative (PID) control strategy is a more advanced form of process control. It combines three different control actions: proportional, integral, and derivative, to provide a smooth and precise control of the process variable.
PID control loop block diagram; Credits: plcynergy.com
The proportional action (P) adjusts the control action in proportion to the error. This means that the larger the error, the larger the control action. The proportional action provides a quick response to changes in the process variable. Still, it cannot eliminate the steady-state error, which is the difference between the process variable and the setpoint in a steady state.
The integral action (I) accumulates the error over time and adjusts the control action accordingly. This helps to eliminate the steady-state error, ensuring that the process variable reaches the setpoint in a steady state. However, the integral action can respond slowly to sudden changes in the process variable.
The derivative action (D) predicts the future behavior of the error based on its rate of change and adjusts the control action preemptively. This provides stability to the system, preventing oscillations around the setpoint. However, the derivative action can be sensitive to noise in the process variable, leading to erratic control actions.
PID control, popular in process control for its adaptability and efficiency, is implementable in various processes. Tuning involves adjusting proportional, integral, and derivative gains for desired performance.
Despite the benefits of process control, PID control strategy requires a good understanding of the process dynamics and careful tuning of the controller parameters. It may not be suitable for processes with complex dynamics or significant time delays. More advanced control strategies, such as model predictive control, may be used for these processes.
Recommended Reading: PID Loops: A Comprehensive Guide to Understanding and Implementation
Implementing process control in industrial systems is a multifaceted process that involves careful planning, design, installation, and maintenance. The goal is to create a system that can effectively manage and regulate the operations of industrial processes to maintain efficiency, safety, and quality control procedures.
The design of a process control system is a critical step that lays the foundation for its effectiveness and reliability.
GD Process Control System; Source: gdprocessdesign
System design involves several key considerations:
Understanding Process Requirements: The design process begins with a thorough understanding of the industrial process that needs to be controlled. This involves identifying the process variables to monitor and control, e.g. temperature, pressure, and chemical composition.
Selecting Appropriate Sensors and Actuators: Appropriate sensors and actuators are selected based on the process requirements. Sensors accurately measure variables, while actuators adjust the process accordingly. [6]
Choosing the Right Controller: The controller, a crucial part of process control systems, processes sensor data, executes control algorithms, and sends control signals. Its selection is influenced by process complexity, response speed, and automation level.
Ensuring Compatibility and Integration: Process control components should be compatible and communicate effectively, ensuring sensors, controllers, and actuators connect and exchange data reliably.
Incorporating Safety Mechanisms: Safety is paramount in industrial systems. The design must include fail-safes, alarms, and emergency shutdown mechanisms to protect the process, the equipment, and the personnel in case of malfunctions or hazardous conditions.
Planning for Scalability and Flexibility: Control systems should be adaptable to accommodate potential changes and future modifications in industrial processes.
Compliance with Standards and Regulations: The design should abide by industry standards and regulations for safety, reliability, and quality, adhering to local and international process control and safety codes.
By addressing these considerations, the system design sets the stage for a process control system that is robust, efficient, and capable of meeting the demands of the industrial process it is intended to manage.
System installation is the next critical step in implementing process control in industrial systems. This involves physically installing the components of the process control system, including sensors, controllers, actuators, and communication networks, and integrating them into the industrial process.
The installation of sensors and actuators requires careful planning and execution. They must be installed in the appropriate locations to accurately measure the process variables and effectively implement the control actions. The installation must also ensure that the sensors and actuators are protected from environmental conditions affecting their performance or lifespan, such as high temperatures, corrosive chemicals, or mechanical vibrations.
The controller is typically installed in a control room or a control cabinet, where it is protected from the harsh conditions of the industrial environment. The controller must be connected to the sensors and actuators through a communication network, which can be wired or wireless, depending on the requirements of the process and the capabilities of the controller.
The communication network is a critical component of the process control system. It must be reliable and robust, capable of transmitting data between the sensors, the controller, and the actuators without delays or errors. The network must also be secure, protecting the process control system from cyber threats.
The installation of the process control system must comply with industry standards and regulations, ensuring safety and reliability. This includes following proper installation procedures, using certified equipment, and conducting thorough testing and validation of the system.
Once the process control system is installed, it must be commissioned, which involves testing the system under normal operating conditions to verify that it is functioning correctly and effectively controlling the process. The commissioning process also includes tuning the controller to optimize the performance of the control system.
The installation of a process control system is a complex task that requires expertise in process control, electrical and mechanical engineering, and information technology. It is often carried out by specialized teams or contractors, who have the necessary skills and experience to ensure a successful installation.
After installing a process control system, system testing and optimization are crucial to ensure that the system operates correctly and efficiently. This phase involves a series of tests to validate the functionality of each component and the system as a whole.
System Testing: The first step is to perform functional testing of the sensors and actuators to confirm they are correctly measuring and controlling the process variables. This includes calibration of sensors to ensure their readings are accurate and reflect the actual conditions of the process. Actuators are tested to verify that they respond correctly to the control signals from the controller.
Once individual components are verified, the entire control system is tested. This involves simulating various operating conditions to ensure the system behaves as expected. The system should maintain the process variables within the set limits under all tested conditions. Any discrepancies or malfunctions identified during these tests must be addressed and corrected.
Optimization: With the system confirmed to be functional, the next step is optimization. This process involves fine-tuning the control parameters to achieve the optimum possible performance. For PID controllers, this means adjusting the proportional, integral, and derivative gains to minimize the error between the process variable and the setpoint.
PID Control System Optimization; Source: advancedcontrol
Optimization also includes assessing the system's response to disturbances. The goal is to ensure that the system can quickly and effectively return to the desired operating conditions after a disturbance. This requires a balance between responsiveness and stability to prevent overshooting or oscillations.
Performance Metrics: During optimization, various performance metrics are used to evaluate the system's effectiveness. These metrics may include the rise time, which is the time taken for the process variable to reach the setpoint; the settling time, which is the time taken for the process variable to stabilize within a certain range around the setpoint; and the overshoot, which is the extent to which the process variable exceeds the setpoint.
Continuous Improvement: System testing and optimization are not one-time activities. They are part of a continuous improvement process where the system is regularly reviewed and updated to adapt to changes or to incorporate new technologies and best practices.
In summary, system testing and optimization are critical for ensuring that a process control system is reliable, efficient, and capable of maintaining the desired control over the industrial process. These activities require a systematic approach and a deep understanding of both the controlled process and the control system itself.
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The process control standards provide a guideline to ensure the safety and reliability of process control systems. Some of the common process control standards are:
ISA-95 is a standard for integrating enterprise and control systems that provides a framework for exchanging information between enterprise and control systems.
ISA-88 is a standard for batch control systems that provides a framework for designing, implementing, and operating batch control systems.
IEC-61511 is a standard for the safety instrumented systems used in the process industries that provides guidelines for the design, implementation, and operation of safety instrumented systems.
ANSI/ISA-84 is a standard for the safety instrumented systems used in the chemical and petrochemical industries. ANSI/ISA-84 provides guidelines for designing, implementing, and operating safety instrumented systems in hazardous environments.
Implementing effective process control in industrial systems requires a series of planned steps and expertise in control, engineering, and technology. The choice of control strategy, like On-Off or PID, is based on the process nature and requirements. PID control provides more precise and smooth control. The success of a process control system relies on continuous testing, optimization, and adaptation to process changes and new technologies.
Q. What is process control?
A. Process control is a field of engineering that deals with the regulation of processes in industrial systems. It involves monitoring and adjusting the process variables, such as temperature, pressure, flow rate, and chemical composition, to maintain the desired operating conditions.
Q. What are the main components of a process control system?
A. The main components of a process control system are sensors, controllers, and actuators. Sensors measure the process variables, controllers process the sensor data and execute control algorithms, and actuators implement the control actions.
Q. What is a PID controller?
A. A PID controller is a type of controller that uses a Proportional-Integral-Derivative control strategy. It adjusts the control action based on the current error, the accumulated error, and the rate of change of the error, providing a smooth and precise control of the process variable.
Q. What is system testing and optimization?
A. System testing and optimization involve validating the functionality of the process control system and fine-tuning the control parameters to achieve the best possible performance. This includes functional testing of the sensors and actuators, testing of the entire control system under various operating conditions, and adjusting the control parameters to minimize the error between the process variable and the setpoint.
[1] Copernicus. Historical Development of Water-powered Mechanical Clocks [Cited 2024 February 13] Available at: Link
[2] CiteSeerX. The UNIVAC system [Cited 2024 February 13] Available at: Link
[3] Wevolver. PID Loops: A Comprehensive Guide to Understanding and Implementation [Cited 2024 February 13] Available at: Link
[4] Dynamic Measurement and Control Solutions. Process Control Systems [Cited 2024 February 13] Available at: Link
[5] Scholarly Community Encyclopedia. Control Strategy for High-Speed On/off Valves [Cited 2024 February 13] Available at: Link
[6] ISA Interchange. Process Control Systems for Industrial Applications [Cited 2024 February 13] Available at: Link
Following the steps below will help you improve the way your process control system operates and reduce your energy costs.
Monitor your system's performance and walk around your site looking for signs of poor control. These include inconsistencies in product quality, variations in the energy used per unit of product, controls set to manual instead of automatic, and production upsets.
Ensure that all your existing instruments and regulators have been correctly installed and are working well. You should repair any faulty equipment, fix 'sticky' valves and check that measurement devices like sensors and probes have been properly installed, replacing them if necessary.
Check that control loops are not always set to manual and make sure operators are aware that working in automatic mode is much more efficient. Review control loops to check they're correctly configured and tuned and adjust controllers to minimise time delays and dead time.
Draw up a list to show where you could save energy by improving your systems control. You should prioritise low-cost actions and enhancements and think about when to start larger projects. Identify the capital cost and payback period for each and put together an action plan for implementation.
Put together the business case for the implementation of your plan. This should include a feasibility study of costs and savings, a detailed risks/benefits assessment and information about the project and the project team. A pilot study could help decision-makers to give support to the project.
Ensure that all new control equipment is installed and calibrated properly. You should train operators to use the new systems and how to deal with unexpected performance after the changes have been made. You should also set new energy consumption targets and draw up a preventive maintenance schedule to keep controls running well and run a campaign to increase awareness of the new systems and changes that have been made among your staff.
Set up a regular system of checks to identify any areas of poor control as early as possible, perhaps when investigating your current system. This should include reviewing operator logs for symptoms of poor control, setting performance targets and comparing them with independent benchmarks, and reviewing targets regularly to make sure they're appropriate.