Model-Based Controller Variability Detection at Constraint Limits
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Solution Overview
Problem
Model-based industrial process controllers experience a decline in performance over time due to factors like inaccurate models, misconfiguration, and operator actions, leading to increased variability and control giveaway, which reduces their effectiveness by up to fifty percent.
Innovation Solution
An apparatus and method that identify impacts and causes of variability or control giveaway by analyzing data from industrial process controllers, calculating standard deviations, and determining control giveaway values to generate graphical displays showing the effects on model-based controller performance, allowing for early detection and reduction of performance losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based controllers are used to control industrial processes, then controller performance and precision are improved, but performance declines over time due to model inaccuracy, misconfiguration, and operator actions
Solution Approach 1:
The system performs preliminary actions by continuously monitoring controlled variables and calculating control giveaway values before significant performance degradation occurs. This enables early detection of model inaccuracy, misconfiguration, or operator actions that may lead to performance decline, allowing corrective measures to be taken proactively rather than reactively.
Solution Approach 2:
The system implements feedback mechanisms by continuously evaluating control giveaway values and comparing them against thresholds or historical data. This feedback loop enables the system to detect performance degradation trends and trigger alerts or corrective actions, maintaining reliability over time despite the natural decline in model-based controller performance.
2Loss of time
If control giveaway values are calculated frequently to detect performance decline, then early detection capability is improved, but computational resources and processing time are consumed
Solution Approach 1:
The system applies partial action by calculating control giveaway values selectively based on identified periods when controlled variables are near their limits, rather than continuously computing for all time periods. This approach provides sufficient detection capability while reducing unnecessary computational overhead and energy consumption.
Solution Approach 2:
The system changes parameters by adapting the frequency and intensity of control giveaway calculations based on process conditions. When controlled variables are close to limits, the system increases monitoring intensity; when they are well within acceptable ranges, monitoring intensity is reduced, optimizing the balance between detection speed and resource consumption.
Data Source
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AI summary
A method includes obtaining (504) data identifying values of one or more controlled variables associated with an industrial process controller (106). The method also includes identifying (506) periods when at least one of the one or more controlled variables has been moved to an associated limit by the controller. The method further includes, for each identified period, (i) identifying (510) a standard deviation of predicted values for the associated controlled variable and (ii) determining (512) a control giveaway value for the associated controlled variable based on the standard deviation. The control giveaway value is associated with an offset between the associated controlled variable's average value and the associated limit. In addition, the method includes identifying (516) variances in the one or more controlled variables using the control giveaway values and generating (520) a graphical display (1100, 1200, 1300) identifying one or more impacts or causes for at least some of the variances.