In the process industry, it’s often difficult to fully understand the characteristics of the controlled systems. Building a strict mechanistic model is time-consuming and costly. To reduce operational costs and manage uncertainties, the industry heavily relies on feedback control. Feedback allows the system to remain stable in the face of external disturbances and internal fluctuations, making it an essential and widely adopted method in process automation.
The Role of PID in Reducing Uncertainty
In a process with a strong cause-effect relationship and single-variable control, PID (Proportional–Integral–Derivative) control remains the most commonly used algorithm to reduce uncertainty. It incorporates:
P (Proportional) – reacting to the present,
I (Integral) – learning from the past, and
D (Derivative) – anticipating the future.
This three-parameter linear control algorithm encapsulates a profound control philosophy. Although PID has been criticized for being “too simple” or “outdated,” its effectiveness in single-variable feedback control is undisputed. Its long history and widespread use are testaments to its practicality, not its limitations.
The power of the feedback control framework is so robust that in most cases, PID alone can meet engineering control requirements. However, real-world industrial processes are rarely limited to single-variable control. More often, we deal with multi-variable control problems that are more complex and require deeper consideration.
Challenges of Multi-Variable Control and the Need for APC
To apply PID in multi-variable control scenarios, engineers typically decompose the multi-variable system into several single-variable loops. However, this decomposition is not always straightforward. Poorly defined control loops can lead to suboptimal performance or even instability, making it hard to automate the process effectively.
This is where APC (Advanced Process Control) comes into play. APC helps manage the complexity of multi-variable systems and reduces the uncertainty associated with control strategies. It enables better coordination between control variables, handles constraints, and provides optimization capabilities, making it a preferred approach in increasingly dynamic and flexible production environments.
When the multi-variable problem can be reasonably simplified into independent loops, PID still works well. But for uncertain, highly interactive multi-variable systems, APC has unique advantages.
PID vs. APC: Different Strengths for Different Problems
PID excels at reducing uncertainty caused by external disturbances in single-variable processes.
APC excels at reducing strategy uncertainty in multi-variable processes where interactions and constraints are complex.
In practice, both methods are often used together: PID handles fast local loops, while APC manages higher-level coordination and optimization.
Practical Examples of PID Strategies for Multi-Variable Systems
To illustrate how PID is adapted in multi-variable control scenarios, consider the table below. It lists typical cases where one controlled variable (CV) is influenced by two manipulated variables (MV1 and MV2), and the corresponding PID-based control strategy:
| Control Requirement | Use Case Example | PID-Based Solution |
|---|---|---|
| MV1 and MV2 change sequentially | Pressure control of a storage tank (venting and nitrogen makeup) | Cascade or step-wise control |
| MV1 and MV2 change simultaneously | Temperature control using two variable-speed air coolers | Split-range control |
| MV1 and MV2 change in fixed ratio | Two reactors feeding materials proportionally | Load control + Ratio control |
| MV1 + MV2 remains constant | Heat exchanger temperature with bypass line | Cross split-range control |
| MV1 as primary, MV2 as secondary | Flow control using a large and a small valve | Valve position control |
Choosing the right control scheme is key to leveraging PID’s strengths. However, defining and tuning these strategies can be difficult. While split-range control is a common approach, it’s not always optimal for complex systems.
What Makes APC Different from PID-Based Solutions
In APC systems, control problems are often abstracted to a model-based structure where multiple CVs and MVs are optimized simultaneously. The model is the same, but the difference lies in the control logic and parameter tuning. APC allows for more advanced strategies, including:
Constraint handling
Prediction-based optimization
Intelligent response to abnormal conditions
In contrast, PID control either combines MVs through logic or expands CVs to simplify the system into single-variable problems. APC, however, turns it into a global optimization problem that ensures CVs meet targets while efficiently using the available degrees of freedom.
Conclusion
Both PID and APC are essential tools in the process industry. Each has its strengths, and they are not mutually exclusive, but rather complementary. Understanding when and how to use each is critical for effective and reliable process control:
Use PID for disturbance rejection in simple, well-defined loops.
Use APC for complex, interactive, and constraint-rich systems.
By selecting the right approach—or combining both—engineers can significantly reduce process uncertainty, improve stability, and optimize performance.