A process controller is an electronic device that takes input signals from sensors measuring process variables (like temperature, pressure, or flow) and sends output signals to actuators to adjust the process. Controllers are the “brains” of the control system, processing real-time data and making decisions to keep the system operating at desired setpoints.
2.1 Types of Controllers
Controllers can vary significantly in complexity, from simple manual controllers to sophisticated computer-based systems.
a. On-Off Controllers
On-off controllers are the simplest type of control system. They operate based on whether a process variable is above or below a set point.
Working Principle: When the measured variable deviates from the setpoint, the controller switches the actuator either fully “on” or fully “off”. This type of controller is like a thermostat that turns a heater on when the temperature drops below a certain point and turns it off when the temperature rises above the setpoint.
Advantages:
– Simple and cost-effective for basic control needs.
– Ideal for applications that don’t require precise control.
Applications:
– Home thermostats and heating systems.
– Industrial alarm systems where a binary response is needed (e.g., high-pressure shutdown).
b. Proportional Controllers (P-Control)
Proportional controllers adjust the actuator output in direct proportion to the difference between the measured variable and the setpoint, known as the error.
Working Principle: The greater the error (deviation from the setpoint), the larger the correction applied by the actuator. The output is proportional to the error, which helps reduce the overshoot associated with on-off control.
Advantages:
– Provides smoother control compared to on-off controllers.
– Reduces oscillations and allows for finer adjustments.
Applications:
– Temperature control in ovens and furnaces.
– Pressure regulation in pipelines.
c. Proportional-Integral Controllers (PI-Control)
PI controllers combine proportional control with an integral function to eliminate steady-state errors.
Working Principle: In addition to proportional action, the integral action sums the error over time and adjusts the output accordingly, ensuring the process variable reaches the setpoint exactly.
Advantages:
– Eliminates steady-state error, achieving more accurate control.
– Suitable for systems where minor fluctuations are unacceptable.
Applications:
– Flow control in industrial piping systems.
– Maintaining constant pressure in hydraulic systems.
d. Proportional-Integral-Derivative Controllers (PID Control)
PID controllers are the most widely used controllers in industrial automation. They add derivative control to proportional and integral functions for enhanced stability and responsiveness.
Working Principle: The proportional term provides an immediate response to error, the integral term eliminates steady-state error, and the derivative term predicts future error by analyzing the rate of change of the process variable.
Advantages:
– Provides fast, accurate, and stable control.
– Minimises overshoot and oscillations, making it suitable for dynamic systems.
– Can be fine-tuned for nearly any process, offering the highest precision.
Applications:
– Temperature control in furnaces, reactors, and HVAC systems.
– Speed control in motors and conveyors.
– Level control in tanks and silos.
2.2 Advanced Controllers
a. Adaptive Controllers
Adaptive controllers modify their behaviour in real-time based on the changing characteristics of the process. They are useful for processes that vary over time, such as systems affected by environmental changes or wear and tear.
Advantages:
– Self-adjusting, meaning they can adapt to changes without manual intervention.
– Provide robust control in dynamic, unpredictable environments.
Applications:
– Control of chemical reactors with varying reaction rates.
– Climate control systems where external conditions change frequently.
b. Model Predictive Controllers (MPC)
Model Predictive Control (MPC) uses a mathematical model of the process to predict future behaviour and optimise control actions over a specified time horizon.
Working Principle: MPC uses a model of the system to predict the future states of the process. The controller then calculates the best course of action to optimise performance while respecting constraints (such as avoiding overshoot or minimising energy use).
Advantages:
– Handles multi-variable systems and processes with complex interactions.
– Ideal for constrained optimization, where multiple goals must be achieved simultaneously (e.g., minimising energy while maintaining quality).
Applications:
– Oil refinery processes, where multiple variables (temperature, pressure, flow) must be managed.
– Chemical processing plants, optimising yield while minimising energy consumption.
