Constraint Violation Diagnosis for Model-Based Process Controllers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Model-based industrial process controllers experience a decline in performance over time due to factors like inaccurate models, misconfiguration, or operator actions, leading to frequent constraint violations that are difficult to analyze and prone to error, resulting in costly shutdowns and reduced efficiency.
Innovation Solution
An apparatus and method for automated identification and diagnosis of constraint violations, utilizing data analysis and graphical displays to identify probable causes and generate visualizations for prompt corrective action, leveraging a digital twin for 'what if' analyses to optimize controller operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of constraint violations is performed, then detailed investigation is possible, but it is labor-intensive and error-prone
Solution Approach 1:
The system performs automated self-diagnosis of constraint violations by analyzing process data, controller behavior, and operator actions to identify probable causes without requiring extensive manual intervention. The automated root cause analysis engine independently investigates violations and generates diagnostic reports.
Solution Approach 2:
Manual mechanical analysis by operators is replaced with an automated digital analysis system that uses software algorithms to process data, identify patterns, and determine root causes of constraint violations, eliminating human labor and associated errors.
2Productivity
If model-based controllers operate for extended periods, then productivity is maintained, but performance declines due to model inaccuracy and misconfiguration
Solution Approach 1:
The system continuously monitors controller performance and provides feedback about constraint violations and their root causes. This feedback loop enables identification of model drift and misconfiguration issues over time, allowing for performance optimization and maintenance while maintaining continuous operation.
Solution Approach 2:
The system performs preliminary analysis of constraint violations to identify probable causes before they lead to significant performance degradation or shutdowns. By detecting issues early through automated monitoring and analysis, corrective actions can be taken proactively to maintain controller reliability.
3Measurement precision
If comprehensive data analysis is performed for each constraint violation, then accurate root cause identification is achieved, but system complexity increases
Solution Approach 1:
The analysis system is segmented into distinct functional modules: data collection module, constraint violation detection module, root cause analysis engine, and reporting module. Each module handles specific aspects of the analysis, making the overall complex system manageable and maintainable while achieving comprehensive analysis.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
A method includes obtaining (404) data identifying values of one or more process variables associated with an industrial process controller (106) and identifying (408) one or more constraint violations using the data. The method also includes, for each identified constraint violation, analyzing (410) a behavior of the controller, a behavior of an industrial process being controlled, and how the controller was being used by at least one operator for a period of time. At least part of the period of time is prior to the identified constraint violation. The method further includes generating (412) a graphical display (900, 1000) based on the analysis, where the graphical display identifies one or more probable causes for at least one of the one or more constraint violations.