1. Introduction: Breaking the Industry Barrier
Early in my career, I worked on advanced control projects in large-scale petrochemical enterprises. Today, I’m involved in control optimization efforts spanning chlor-alkali, fertilizers, non-ferrous metals, and petrochemicals. These industries differ greatly in processes, terminology, and operational culture, which often makes it hard to even have an open and effective conversation with process engineers—let alone drive impactful improvements.
It’s common to see control engineers managing the same DCS for years, yet still unclear about the basic process flow. Why? Because meaningful communication with process teams goes beyond asking, “What are your problems or expectations?” The real pain points are often buried in routines, not explicitly articulated.
Just as a doctor doesn’t rely solely on the patient’s opinion for diagnosis, a control engineer shouldn’t start with assumptions either.
2. Data-Driven Diagnosis: Start with the Right Inputs
Before I step into the field for any project, I request two key datasets:
Control loop performance statistics
Operator intervention and event logs
These provide a snapshot of what’s happening on the ground. Then, I do a rough review of the process flow to understand the broader context.
During the first meeting with process engineers, I let them explain the process in their own words—but I guide the discussion using frequent operator actions as the pivot point. These high-frequency operations often lead us straight to the heart of difficult, nagging process issues.
Once the conversation reaches a concrete problem area, the process engineer naturally steps into their zone of expertise and helps identify the root cause. Using this structured decomposition method, we bypass the need to dive into confidential or overly detailed SOPs. Most high-frequency issues translate into:
Missing control strategies
Poorly tuned or misapplied control strategies
Improper PID tuning
And beneath it all, the real causes often trace back to mass balance mismatches or unused degrees of freedom in the process.
3. Knowledge Automation, Not Cosmetic Optimization
Many automation projects get caught in two traps:
Pursuing unrealistic process improvements
Focusing only on surface-level efficiency gains for management metrics
Instead, I focus on automating operational knowledge—the patterns and decisions that experienced operators already make instinctively. This approach not only uncovers low-hanging fruit but also wins over operators, as it respects their expertise.
The beauty of this method is that it often yields tangible improvements without modifying equipment or changing core process designs. That makes the optimization project not only technically successful but also politically and economically feasible.
4. Project Success = Process + Perspective
Since refining this methodology, I no longer worry about working across industries. The project efficiency and solution quality have both improved significantly.
Here are the key takeaways:
Start every project with the intent to extract and automate operational knowledge
Demystify automation—align client expectations with realistic, high-impact goals
Avoid “hammer-thinking”—not every problem is a nail just because you have a controller
Optimize PID and ARC layers, not just models and variables in advanced control
Deeply understand the tools and techniques, so you can apply them adaptively, not mechanically
5. Final Thoughts
Cross-industry control optimization is no longer a burden when you switch focus from what to optimize to how to uncover knowledge worth automating. When your mindset shifts from “control engineering” to “engineering human behavior into systems,” the results speak for themselves—regardless of the industry.