Ask any experienced plant operator and they'll tell you: no two days are exactly the same. Even "steady-state" processes drift continuously. Yet most control systems are designed as if the process never changes. This mismatch between reality and assumption costs the industry billions annually in suboptimal performance.
The Sources of Process Drift
Chemical processes change over multiple timescales, from hours to years. Understanding these sources of variation is the first step toward addressing them.
Feedstock Variability
Raw materials are never perfectly consistent. Natural gas composition varies by source and season. Crude oil properties differ between wells and shipments. Agricultural feedstocks change with weather and growing conditions.
These variations affect reaction rates, product yields, and quality. A control system tuned for one feedstock composition may perform poorly when that composition changes—which happens constantly.
Catalyst Deactivation
Most industrial reactions use catalysts to speed reactions and improve selectivity. Catalysts deactivate over time due to poisoning, coking, sintering, and other mechanisms. As activity drops, operators must compensate—typically by raising temperature or increasing catalyst loading.
This isn't a sudden change but a gradual drift. A reactor might lose 0.1% activity per day—barely noticeable day-to-day, but significant over weeks and months. Fixed control parameters become increasingly suboptimal as the catalyst ages.
Heat Exchanger Fouling
Heat exchangers accumulate deposits over time, reducing heat transfer efficiency. A reactor that starts with excellent temperature control may struggle to maintain setpoints as fouling progresses. The process hasn't changed—but its behavior has.
Ambient Conditions
Outdoor temperature affects cooling capacity. Humidity impacts certain reactions. Barometric pressure influences distillation columns. These environmental factors cause systematic variations that repeat daily and seasonally.
Equipment Wear
Pumps lose efficiency. Valves develop stiction. Sensors drift. Agitator bearings wear. Every piece of equipment in a chemical plant degrades over time, subtly changing process behavior.
The Limitation of Fixed Control
Traditional PID controllers are designed around a single operating point. The tuning process determines controller gains based on process behavior at that point. But what happens when the process moves away from that point?
Performance Degradation
A perfectly tuned controller becomes progressively less effective as the process drifts. The controller is solving yesterday's problem, not today's. This shows up as increased variability, slower response to disturbances, and operation further from optimal setpoints.
The Retuning Treadmill
Some plants address drift through periodic retuning—manual adjustment of controller parameters based on observed performance. This approach has problems:
- Retuning is time-consuming, so it happens infrequently
- Performance degrades between retuning intervals
- Skilled engineers capable of tuning are expensive and scarce
- Manual tuning can't keep up with rapid changes
Conservative Operation
Unable to maintain tight control as processes drift, operators compensate by running more conservatively. Safety margins increase. Throughput decreases. Energy consumption rises. The plant runs—but far from its potential.
What Adaptive Control Looks Like
Truly adaptive control maintains performance despite process changes. It does this by continuously updating its understanding of the process and adjusting its behavior accordingly.
Online Model Adaptation
Adaptive systems maintain models of process behavior and continuously compare predictions to actual measurements. When discrepancies appear, the model updates automatically. This isn't just parameter adjustment—it's genuine learning about how the process has changed.
Intelligent Gain Scheduling
Rather than fixed controller gains, adaptive systems vary their aggressiveness based on current process state. When the process is far from setpoint and well- understood, the controller acts decisively. When near setpoint or facing uncertainty, it becomes more cautious.
Feedforward from Measurable Disturbances
When disturbances can be measured—feedstock composition, ambient temperature, upstream flow rates—adaptive systems use this information proactively. Rather than waiting for the effect to show up in controlled variables, they anticipate and compensate.
Continuous Optimization
Static setpoints assume the optimal operating point doesn't change. But if catalyst activity is declining, the optimal temperature might be rising. If feedstock is lighter, flow rates might need adjustment. Adaptive systems continuously reevaluate what "optimal" means under current conditions.
The Acaysia Approach to Adaptation
Our platform implements adaptation at multiple levels:
Gray-Box Model Updates
Our hybrid models combine physical structure with learned components. The physical structure remains constant—mass and energy must balance. But the learned components update continuously as new data arrives. This allows adaptation while maintaining physical consistency.
Uncertainty-Aware Control
Our MPPI-based controller explicitly tracks prediction uncertainty. When the model is confident, it optimizes aggressively. When uncertainty increases— perhaps because conditions have moved outside the well-characterized region— it automatically becomes more conservative. No manual intervention required.
Anomaly Detection
Not all changes should trigger adaptation. Equipment failures and sensor malfunctions need different responses than normal process drift. Our system distinguishes between expected variation and genuine anomalies, adapting to the former while alerting operators to the latter.
Human-in-the-Loop
Adaptation doesn't mean operators lose control. Our system explains why it's adapting and what it's learning. Operators can override adaptations, constrain how much the system can change, or trigger manual retraining when they know something the system doesn't.
Real-World Impact
In pilot deployments, we've seen dramatic reductions in performance degradation over time:
- Yield remains within 0.2% of optimal even as catalyst ages
- Temperature control stays tight despite fouling progression
- No need for manual retuning between turnarounds
- Faster recovery from grade changes and transitions
The economic impact compounds over time. A system that maintains 98% of optimal performance for a year delivers far more value than one that starts at 100% but drifts to 90% by month six.
Conclusion
The chemical industry has accepted performance degradation as inevitable. It's not. With modern sensing, computation, and machine learning, we can build control systems that adapt as fast as processes change.
The technology exists today. The question is whether the industry is ready to adopt it. At Acaysia, we're convinced the answer is yes. Plants that embrace adaptive control will operate closer to their theoretical potential, day in and day out, while those that don't will be left wondering where the margin went.
Want to see how adaptive control performs on your process? Request a technical discussion.