Valves at limit. When valves are fully open or fully closed, they’re not controlling. The process can wander without any ability to control it. When a valve is 100% open most of the time, it’s often a sign of an under-sized valve.
Develop a list of these under-sized valves; you’ll have a great starting point for a debottlenecking study. Many plants have been able to increase production rate simply by focusing on key bottlenecks caused by under-sized equipment.
A control-loop-monitoring system can greatly simplify this task by using real-time data to calculate the percentage of time each valve is fully open.
Operator actions. Another way to determine the control system’s effectiveness is to evaluate how many operator actions are required to keep the process running. Count each of the following as an operator action:
• acknowledging an alarm or message;
• altering a set point;
• changing controller mode; and
• varying control output position.
This provides a simple, yet effective way to find where big problems lie. In most plants, less than 10% of control loops account for 80% of operator actions. In some cases, these “bad actors” become a regular distraction, keeping operators away from more important tasks.
At SABIC Innovative Plastics, Selkirk, N.Y., operator Gene Lezatte says that one distillation column routinely caused major operational headaches: “We were chasing it around. Sometimes we’d have two operators to manage just that one column.” After some intervention by the controls engineer, Lezatte says, “That column is rock-steady.”
Traditional statistical metrics such as variability can help provide overall perspective on the process. After all, one of the primary functions of a control system is “to reduce variability.” Some simple, classic measures include:
• variability or variance;
• standard deviation; and
• average, minimum and maximum.
Trends in any of these values can indicate problems in a process. Establish a baseline for variability of key factors and pay close attention when variability dramatically increases or decreases. When normalized by instrument spans or byproduct specification limits, the absolute value of these metrics also may hold some meaning.
Professors at engineering schools have emphasized certain control performance measures over the years. These metrics offer some value as indicators, particularly when doing an “apples to apples” comparison. However, be extremely careful about using them in a real-world process.
Integral of absolute error (IAE). Almost every process control textbook talks about IAE as a measure of control effectiveness. IAE is the “sum of the absolute error,” i.e., the difference between the process variable (PV) and the set point (SP) over time.
On the surface, IAE appears a very good measure of performance. Indeed, for a controller humming along in automatic, this metric can provide some insight. But in a real-world process, many factors can directly affect it: