Adaptive Process Control Limits Using Instance-Based Learning
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Solution Overview
Problem
Industrial processes and assets often operate inefficiently due to fixed control limits that do not account for dynamic operational changes, leading to inaccuracies and disruptions, especially when employing data-driven models with non-linear data and limited training data.
Innovation Solution
Implement instance-based learning to provide adaptive recommendations for process control limits, using intelligent mining of cleansed historical operating data to optimize asset and process operations without relying on fixed settings, and employing real-time calculations for closed-loop optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed control limits are used for industrial assets and processes, then operational simplicity is maintained, but process efficiency deteriorates due to inability to adapt to dynamic operating conditions
Solution Approach 1:
The patent implements dynamic control limits that automatically adjust based on real-time operating conditions and historical data analysis. The system transitions from static fixed limits to dynamic adaptive limits that evolve with changing process conditions, thereby maintaining ease of operation while significantly improving process efficiency through context-appropriate control parameters.
Solution Approach 2:
The system incorporates feedback mechanisms where process data is continuously collected, analyzed against historical patterns, and used to adjust control limits. This closed-loop feedback enables the system to learn from past operations and adapt control parameters dynamically, resolving the contradiction between operational simplicity and process efficiency.
2Measurement precision
If data-driven models with non-linear data are employed, then measurement precision improves, but device complexity increases due to limited training data requirements
Solution Approach 1:
The system uses instance-based learning that requires only limited historical data stored in a repository, rather than comprehensive training datasets. By selecting and comparing relevant historical instances rather than processing entire datasets, the system achieves high measurement precision with reduced computational complexity and data requirements.
Solution Approach 2:
The system creates simplified digital representations (digital signatures) of historical operating conditions that capture essential patterns without requiring full complexity of original data. These copied representations enable accurate comparisons and insights while significantly reducing the computational burden and data storage requirements.
3Adaptability or versatility
If instance-based learning is implemented for adaptive control recommendations, then adaptability improves, but loss of time increases due to real-time data processing requirements
Solution Approach 1:
The system pre-processes and stores historical operating data in a structured repository during periods when real-time processing is not critical. By preparing historical instances and digital signatures in advance, the system minimizes real-time processing requirements while maintaining high adaptability through rapid comparison of current conditions against pre-analyzed historical patterns.
Data Source
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AI summary
Various embodiments described herein relate to advanced process control for assets and/or processes using instance-based learning. In this regard, an event indicator related to a change event associated with operation of an asset is received. In response to the event indicator, one or more insights for one or more real-time settings for the asset are determined based at least in part on a comparison between a current operating condition digital signature for the asset and historical operating condition digital signature for the asset. Additionally, the one or more real-time settings for the asset are adjusted based on the one or more insights to provide one or more adjusted settings for the asset.