Process Plant Abnormal Operation Detection Using Regression Models
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Solution Overview
Problem
Current process control systems in industrial plants often operate in abnormal states due to undetected issues, leading to suboptimal performance and significant costs, as existing diagnostic tools primarily react to problems after they occur, rather than preventing them.
Innovation Solution
A system and method that utilize configurable models with multiple regression models to detect deviations from predicted operations, allowing for early identification of abnormal situations by comparing actual plant operations to modeled behavior, enabling proactive measures to prevent issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional diagnostic tools are used to detect problems, then problems can be identified after they occur, but the system cannot prevent abnormal operations proactively
Solution Approach 1:
The system creates a digital twin model of the process plant that continuously predicts normal operation parameters in advance. By comparing actual sensor data against predicted values from the digital twin, the system can detect deviations before they manifest as actual problems, enabling proactive intervention rather than reactive diagnosis.
Solution Approach 2:
The digital twin serves as an intermediary between the physical process plant and the diagnostic system. It translates complex plant operations into predictable parameter relationships, allowing the detection system to identify abnormalities by measuring deviations from the digital twin's predictions without directly monitoring every physical parameter.
2Measurement precision
If multiple regression models are used to cover different operating regions, then detection accuracy across various conditions improves, but model complexity increases
Solution Approach 1:
The detection system divides the process plant's operating space into multiple distinct operating regions, each with its own regression model. This segmentation allows each model to be specialized for specific conditions (e.g., normal operation, startup, shutdown), improving detection accuracy within each region while managing overall complexity through modular model structures.
Solution Approach 2:
The system dynamically selects which regression model to use based on current operating parameters. By monitoring process conditions and switching between pre-configured models appropriate for different operating regions, the system maintains high detection accuracy across varying conditions without requiring a single overly complex universal model.
Data Source
AI summary
A system for detecting abnormal operation of at least a portion of a process plant includes a model to model at least the portion of the process plant. The model may be configurable to include multiple regression models corresponding to multiple different operating regions of the portion of the process plant. The system may also include a deviation detector configured to determine if the actual operation of the portion of the process plant deviates significantly from the operation predicted by the model. If there is a significant deviation, this may indicate an abnormal operation.