Advanced Process Control
Driving Operational Excellence Through Predictive Optimization
The Challenge: Beyond Regulatory Control
In modern process industries, standard PID controls are no longer sufficient to maintain stability. Processes face constant disturbances, equipment constraints, strong relationship between variables, and optimizations requirements. Traditional controls react after a disturbance occurs or are unable to see the future system’s behavior.
Advanced Process Control (APC) changes this paradigm. By utilizing dynamic process models and multivariable prediction, APC enables units to operate closer to constraints, reject disturbances before they affect quality, and optimize performance in real-time.
Our Main Core Competencies:
Model Predictive Control (MPC)
MPC utilizes a dynamic model of the process to predict future behavior over a defined horizon. At each control interval, an optimization algorithm solves for the optimal set of manipulated variable moves that will keep the process within constraints while driving toward economic objectives.
Key Applications:
- High-purity distillation columns
- Reactor temperature control
- Integrated unit optimization
- Constraint pushing for maximum performance
“MPC transforms reactive control into predictive optimization.”
Cascade Control
Cascade control improves dynamic response by nesting secondary measurements inside primary control loops. The secondary loop reacts quickly to local disturbances, preventing them from ever affecting the primary controlled variable.
Key Applications:
- Temperature control with fast pressure disturbances
- Level control with varying inlet flow
- Composition control with reflux fluctuations
“Cascade control eliminates disturbances before they propagate.”
Other Control competencies: Split range control, PID controller with gain scheduler, Override control, Feedforward control, Smith predictor, Ratio control.
Our approach
Effective plant operation requires continuous vigilance over control loop performance. Control Performance Monitoring (CPM) provides operators with automated, real-time assessment of critical loop metrics to identify degradation before it impacts production.
Monitoring process gain reveals gradual wear or recurring external disturbances, while overshoot analysis identifies poorly tuned controller gain. The settling ratio (the quotient of rise time to settling time) must exceed 25%; values below this threshold indicate integral action is too slow, causing sluggish approach to setpoint. Finally, scatter plot analysis of controlled versus manipulated variables reveals nonlinearities such as valve stiction, which appears as a characteristic parallelogram pattern rather than the ideal elliptical cloud. Together, these metrics enable targeted, early intervention to maintain control performance, reduce energy consumption, and maximize process availability.
Case Study: Distillation Column APC Implementation
A distillation column in a sugar company was selected for APC evaluation. The column is subject to feed composition disturbances and experiences interactions between bottom level, reflux rate, and product purity. Standard PID control struggles to maintain specification during feed upsets, resulting in off-spec product resulting in continuous oscillation and slow disturbance rejection.
