The Three Essential Tasks in Process Control: Measure, Compare, Adjust
Introduction
In modern industries—from chemical plants and oil refineries to food processing and pharmaceuticals—process control is the backbone of efficient, safe, and consistent operations. Whether you’re automating a heat exchanger or a packaging line, every control system follows a simple, foundational principle built on three critical tasks:
Measure, Compare, and Adjust.
These three steps are at the heart of every feedback control loop, enabling systems to operate accurately, reliably, and autonomously.
In this article, we’ll explore each of these process control tasks in depth—why they matter, how they’re implemented, and real-world examples that bring the concepts to life.
What is Process Control?
Process control refers to the methods and technologies used to maintain specific conditions within an industrial process by automatically managing process variables like temperature, pressure, flow, level, pH, and speed.
At its core, a control system performs three essential actions:
- Measure the current state.
- Compare the current state to the desired setpoint.
- Adjust the system to correct any deviation.
Let’s break down each task in detail.
1. Measure: Capturing the Process Variable
🔍 What It Means:
Measurement involves collecting real-time data from the process using sensors or transmitters. The process variable (PV) is the actual value being measured, such as temperature, pressure, or flow.
📏 Common Measurement Devices:
| Process Variable | Instrument |
|---|---|
| Temperature | RTD, Thermocouple |
| Pressure | Pressure Transmitter |
| Flow | Flowmeter (Coriolis, Magnetic, Vortex) |
| Level | Ultrasonic, Radar, DP Transmitter |
| pH | pH Analyzer with Probe |
📘 Example:
In a distillation column, temperature must be precisely controlled. An RTD (Resistance Temperature Detector) measures the temperature at various stages to inform the control system of the actual operating condition.
💡 Why It’s Important:
- The quality of control depends on the accuracy and reliability of the measurement.
- Poor measurement leads to incorrect control actions, resulting in product waste, safety risks, or system instability.
✅ Best Practices:
- Calibrate sensors regularly.
- Use redundant sensors for critical measurements.
- Place sensors at strategic process points for accurate readings.
2. Compare: Evaluating Process Performance
⚖️ What It Means:
Comparison is the task of evaluating whether the process variable (PV) matches the setpoint (SP)—the desired target value. This is where the controller comes in, calculating the error: Error (e)=Setpoint (SP)−Process Variable (PV)\text{Error (e)} = \text{Setpoint (SP)} – \text{Process Variable (PV)}Error (e)=Setpoint (SP)−Process Variable (PV)
📟 Controller Types:
| Controller | Description |
|---|---|
| PID Controller | Most common; uses Proportional, Integral, and Derivative control actions |
| On/Off Controller | Basic, for simple applications (e.g., tank level) |
| Fuzzy Logic Controller | For non-linear or complex systems |
| DCS/PLC Logic | Integrated comparison logic in process automation systems |
📘 Example:
A steam boiler setpoint is 120°C. If the current temperature is 115°C, the error is +5°C. The controller will use this error to determine what corrective action to take.
🔄 Key Functions:
- Monitor process variable in real time.
- Calculate deviation from target.
- Apply a control algorithm (e.g., PID) to determine how much adjustment is needed.
⚠️ Challenges:
- Controller tuning is critical. Poor tuning leads to overshoot, oscillation, or slow response.
- In systems with time delays (dead time), special tuning or advanced algorithms (e.g., Model Predictive Control) are required.
3. Adjust: Applying Corrective Actions
🛠️ What It Means:
Adjustment is the action taken by the control system to reduce the error. This involves manipulating a control element such as a valve, pump, or motor to bring the process back to the desired condition.
⚙️ Final Control Elements:
| Device | Function |
|---|---|
| Control Valve | Regulate flow or pressure |
| Variable Frequency Drive (VFD) | Adjust motor speed for pumps/fans |
| Heater Element | Modify temperature |
| Dosing Pump | Inject chemicals (e.g., pH correction) |
📘 Example:
In a flow control loop, if the measured flow is below the setpoint, the controller sends a signal to open the control valve wider. The increased opening allows more fluid to pass through, correcting the flow rate.
🎯 Control Signal Types:
- Analog: 4–20 mA, 0–10 VDC
- Digital: Discrete signals (On/Off)
- Fieldbus/Ethernet: MODBUS, PROFINET, Foundation Fieldbus
✅ Best Practices:
- Ensure actuators are properly sized and calibrated.
- Use fail-safe configurations in case of control signal loss.
- Monitor valve response and feedback (e.g., positioners).
Real-World Process Control Loop Example
Let’s look at a simplified temperature control loop in a chemical reactor:
- Measure: An RTD sensor measures the reactor temperature.
- Compare: A PID controller compares the measured value to the setpoint (e.g., 85°C).
- Adjust: If the temperature is low, the controller signals a control valve to allow more steam into the heating coil.
This loop runs continuously, ensuring the process maintains optimal conditions for the chemical reaction.
Table Summary: The Three Control Tasks
| Task | Purpose | Device Example | Role in Loop |
|---|---|---|---|
| Measure | Detect current process variable | Sensor, Transmitter | Provides real-time feedback |
| Compare | Evaluate difference between SP and PV | PID Controller | Calculates error and action needed |
| Adjust | Implement correction | Control Valve, VFD | Changes the process variable |
Why This Matters in Industry
The “measure-compare-adjust” sequence enables:
- Automation of critical plant functions
- Energy efficiency by avoiding over- or under-correction
- Product consistency in manufacturing
- Process safety in systems with pressure or chemical hazards
Modern systems, whether using Distributed Control Systems (DCS), Programmable Logic Controllers (PLC), or edge computing, still rely on this foundational loop logic.
Common Mistakes to Avoid
| Mistake | Consequence |
|---|---|
| Using poor-quality sensors | Incorrect measurements and unstable control |
| Skipping controller tuning | Overshoot, oscillation, or sluggish response |
| Ignoring final control element calibration | Control commands may not execute properly |
| Failing to maintain loop components | Degrades performance over time |
Advanced Process Control (APC)
In more complex environments, process control evolves into Advanced Process Control, integrating:
- Model Predictive Control (MPC)
- Feedforward control
- Multivariable control
- AI-based optimization
Still, these systems inherit the same three foundational tasks—they just execute them at higher sophistication and scale.
Conclusion
The fundamental loop of measure, compare, and adjust is the cornerstone of every process control system. Whether you’re tuning a simple temperature loop or managing a complex multi-unit production plant, these three tasks form the bedrock of automation.
🛠️ The smarter your measurements, the sharper your comparisons, and the faster your adjustments, the more reliable and efficient your process will be.
Mastering this foundational concept will give you clarity and confidence in designing, operating, and troubleshooting control systems in any industry.
FAQs
Q1: What happens if the measurement is inaccurate?
Incorrect measurements lead to wrong comparisons and incorrect adjustments, often resulting in unsafe or unstable process behavior.
Q2: Do all controllers use PID?
Not all. Some use on/off, fuzzy logic, or adaptive control algorithms depending on application complexity.
Q3: How often should a control loop be calibrated?
Sensors and actuators should be calibrated periodically—monthly, quarterly, or annually—depending on the criticality of the process.