Introduction
Process control has undergone significant evolution, transitioning from simple feedback control methods to advanced process control (APC) strategies. Initially, control systems were designed to correct deviations, aligning with human problem-solving habits. This fundamental approach remains at the core of feedback control.
The early days of process control were driven by practical needs rather than theoretical developments. The Proportional-Integral-Derivative (PID) controller, one of the most widely used control algorithms, predates the formalization of control theory. At the time, even understanding how to implement PID and tune its parameters posed substantial challenges. Although digital computing has made PID implementation straightforward, PID tuning remains a complex issue, particularly in large-scale industrial systems where interactions between control loops complicate parameter adjustments.
Challenges in PID-Based Control Systems
Modern industrial processes exhibit characteristics such as high material recirculation, extensive heat integration, strict environmental and safety regulations, and the pursuit of extreme performance efficiency. These complexities introduce several control challenges:
Interdependent control loops: Isolating the performance of individual control loops without considering system-wide interactions often leads to suboptimal PID tuning.
Degrees of freedom: Many industrial systems formally have more measured variables than manipulated variables, making direct control strategies infeasible.
Process optimization: Beyond ensuring stability and performance, modern control systems must also optimize process efficiency.
These challenges highlight the need for control strategies that extend beyond traditional PID-based methods.
Degrees of Freedom and Control Scheme Design
A critical aspect of control system design is the concept of degrees of freedom (DOF). In industrial practice, certain control schemes suffer from insufficient DOF, making it impossible to achieve desired control objectives. Examples include:
Branch temperature balancing: Strict setpoint control for each branch may lead to infeasibility due to process constraints.
Flow distribution under uncertain total flow: Imposing fixed setpoints on individual branches creates conflicts when total flow fluctuates.
Hotspot temperature control in multiple reactors: Enforcing strict temperature setpoints can be infeasible when operational conditions vary.
To address these issues, it is essential to increase effective degrees of freedom by modifying the control strategy. This can be achieved by:
Reconfiguring control objectives: Instead of enforcing rigid setpoints, employing relative constraints or optimization-based approaches.
Incorporating optimization layers: Using steady-state optimization targets as part of control algorithms.
Using weighting factors and priorities: Adjusting the importance of control variables dynamically to handle infeasibilities.
Conversely, in systems with excessive degrees of freedom, additional flexibility allows for optimization. In such cases, control schemes must not only satisfy control objectives but also optimize remaining degrees of freedom. Approaches include:
Hierarchical control (cascaded): Implementing multiple layers of control for enhanced stability.
Range control (split-range): Allocating control actions dynamically between multiple actuators.
Ratio control: Maintaining fixed proportions between related process variables.
Valve position control: Ensuring optimal valve operation to reduce energy consumption and improve efficiency.
Selecting the appropriate control strategy depends on a combination of theoretical understanding and practical experience.
The Rise of Advanced Process Control (APC)
Despite widespread adoption, PID-based control has limitations in handling multivariable interactions. To overcome these limitations, the industry introduced Model Predictive Control (MPC) in the 1980s. MPC extends beyond PID by utilizing:
Mathematical models of process dynamics: Predicting future process behavior instead of reacting to past deviations.
Constraint handling: Explicitly incorporating system constraints into the control algorithm.
Multivariable control: Managing multiple manipulated and controlled variables simultaneously.
While MPC theoretically outperforms PID in complex systems, it has not completely replaced PID in industrial applications. This is because the closed-loop performance of single-variable MPC and well-tuned PID controllers can be comparable. Instead, MPC’s key strength lies in its ability to manage multivariable control problems, which are increasingly prevalent in modern industrial settings.
Optimization in APC Systems
MPC and other advanced control algorithms must go beyond simply minimizing control errors. A robust optimization framework is necessary, incorporating:
Steady-state optimization layers: Integrating economic and operational objectives into the control system.
Dynamic constraint adjustments: Adapting priorities and weightings dynamically based on process conditions.
Flexible problem formulation: Converting infeasible control tasks into solvable optimization problems.
For example, in branch temperature balancing, rather than strictly enforcing temperature setpoints, an optimization-driven approach can dynamically adjust flow distribution to achieve a balanced temperature profile while considering process constraints.
Why Advanced Process Control Matters
APC’s increasing adoption in the industrial sector is driven by multiple factors:
Rising computational power: Modern computing enables real-time optimization and predictive control.
Growing process complexity: Industrial systems now require more sophisticated control solutions to maintain efficiency and compliance.
Cost-effectiveness: APC strategies reduce operating costs, improve yield, and enhance energy efficiency.
However, successful APC implementation depends on proper engineering understanding. Many APC projects fail not because of flaws in the algorithms but due to:
Misalignment with process characteristics: Poorly chosen APC strategies that do not account for system dynamics.
Lack of expertise: Engineers must understand both the control techniques and the underlying process.
As Professor Sigurd Skogestad has recently emphasized, there is a growing need for research into practical APC design methods. While academic work often focuses on novel control algorithms, industry requires practical methodologies that translate into tangible performance improvements.
Conclusion
The evolution from PID to APC reflects the increasing complexity of industrial process control. While PID remains a cornerstone of control engineering, it has inherent limitations when dealing with multivariable, constrained, and highly interactive systems. MPC and other APC strategies provide solutions to these challenges by leveraging optimization and predictive modeling techniques.
However, APC is not just about the core algorithm—it is about integrating a comprehensive optimization framework that considers real-world constraints and economic objectives. With the right expertise and application, APC can significantly enhance industrial performance and operational efficiency.
Ultimately, the success of APC does not depend solely on technological advancements but also on how well it is applied. Engineers must bridge the gap between control theory and industrial practice to unlock the full potential of advanced process control.