MPC Model Adaptation Using Automated Data Screening and Repair
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Solution Overview
Problem
Multivariable Predictive Control (MPC) performance degrades over time due to process changes, requiring frequent model updates and data screening, which is inefficient and labor-intensive, especially in identifying and excluding unsuitable data for model quality estimation and identification.
Innovation Solution
An automated four-tier data screening and selection system that detects and excludes unsuitable data segments, repairs bad data samples using internal MISO models, and maximizes data usage by interpolating missing values, thereby minimizing data loss and improving model quality estimation and adaptation in MPC applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data screening and selection is performed by control engineers, then data quality for model identification can be ensured, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system performs automated data screening and selection using algorithms that independently evaluate data quality metrics, identify bad data segments, and select suitable data for model identification without requiring manual engineer intervention. The automated system serves itself by implementing the entire data screening workflow through computational methods.
Solution Approach 2:
The manual mechanical process of engineers visually inspecting time series plots and marking bad data is replaced with automated computational algorithms that programmatically evaluate data quality, detect anomalies, and select appropriate data segments based on predefined criteria and metrics.
2Reliability
If manual data screening is performed to exclude bad data segments, then model identification reliability is improved, but productivity decreases due to hours to days of engineering work
Solution Approach 1:
The system autonomously performs data screening, evaluation, and selection tasks that were previously requiring engineer expertise and manual effort. The automated algorithms independently identify bad data segments and select suitable data for model identification, eliminating the need for manual intervention while maintaining reliability.
Solution Approach 2:
The system changes the approach from manual visual inspection to automated computational evaluation by introducing new parameters such as data quality metrics, anomaly detection thresholds, and automated selection criteria that enable rapid and reliable data screening without manual effort.
3Measurement precision
If bad data segments are excluded from model identification, then data quality is improved, but data loss increases reducing available data for modeling
Solution Approach 1:
The system extracts and excludes only the specific bad data segments that meet predefined quality criteria and anomaly detection thresholds, rather than excluding large portions of data. This targeted extraction approach removes harmful data while preserving as much useful data as possible for model identification.
Solution Approach 2:
The system applies different quality assessments to different segments of the data, identifying and excluding only the local bad segments while preserving the quality of the surrounding good data. This localized approach ensures that data exclusion is precise and minimizes overall data loss.
4Ease of manufacture
If conventional data screening approaches are used, then implementation is simple, but they are not suitable for frequent online model adaptation runs
Solution Approach 1:
The system transitions from static manual screening processes to dynamic automated screening that can be executed frequently and rapidly for online model adaptation. The automated algorithms enable the system to adapt to changing process conditions in real-time by performing repeated data screening and model identification runs as needed.
Solution Approach 2:
The system introduces new parameters and computational methods that enable frequent online execution, including automated data quality metrics, rapid anomaly detection algorithms, and efficient data selection criteria that can be evaluated quickly for each model adaptation run.
Data Source
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AI summary
A computer-based apparatus and method for automated data screening and selection in model identification and model adaptation in multivariable process control is disclosed. Data sample status information, PID control loop associations and internally built MISO (Multi-input, Single-output) predictive models are employed to automatically screen individual time-series of data, and based on various criteria bad data is automatically identified and marked for removal. The resulting plant step test/operational data is also repaired by interpolated replacement values substituted for certain removed bad data that satisfy some conditions. Computer implemented data point interconnection and adjustment techniques are provided to guarantee smooth/continuous replacement values.