Operations Management System for Real-Time Process Quality Prediction
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Solution Overview
Problem
Current process control systems are reactive and cannot remediate process quality issues in real-time, as they require offline analysis to detect and correct faults, leading to the production of products with quality issues until the process is corrected.
Innovation Solution
An operations management system (OMS) that provides in-process fault detection, analysis, and correction by receiving process control information, determining variations, calculating contribution values of measured variables, and predicting process quality, enabling immediate corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If offline analysis is used to detect and correct faults, then manufacturing precision can be improved in subsequent batches, but productivity deteriorates due to inability to remediate current batch quality issues
Solution Approach 1:
The system performs preliminary quality assessment during batch execution by continuously monitoring process variables and predicting final quality metrics before the batch completes. This allows operators to take corrective actions while the batch is still in progress, rather than waiting for offline analysis after batch completion.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring process control information, comparing actual process variables against expected values, and providing quality predictions that feed back to operators during batch execution. This enables dynamic adjustment of process parameters to maintain quality within specifications.
2Productivity
If process monitoring and quality prediction systems are implemented in real-time, then productivity improves through immediate corrective actions, but device complexity increases
Solution Approach 1:
The system combines multiple functions into a single integrated platform that performs process monitoring, quality prediction, fault detection, and corrective action guidance simultaneously. This multi-functional approach avoids the need for separate systems for each function, thereby limiting the increase in overall device complexity while achieving real-time quality remediation.
Solution Approach 2:
The system introduces a software-based intermediary layer that sits between the existing process control system and the quality prediction algorithms. This intermediary handles data collection, preprocessing, and coordination between different components, simplifying the integration of complex prediction models without requiring fundamental changes to the underlying control infrastructure.
3Manufacturing precision
If comprehensive process control information is collected and analyzed, then manufacturing precision improves through accurate quality prediction, but loss of time increases due to data processing requirements
Solution Approach 1:
The system focuses on monitoring and analyzing only the critical process variables that have the most significant impact on final product quality, rather than processing all available process data equally. By identifying and prioritizing key quality-influencing parameters, the system achieves accurate quality predictions with reduced data processing requirements and faster computation times.
Solution Approach 2:
The system pre-processes and stores historical process data and quality outcomes during non-critical periods, building predictive models and lookup tables in advance. During batch execution, the system queries these pre-computed models rather than performing complex real-time calculations, significantly reducing data processing time while maintaining prediction accuracy.
Data Source
AI summary
Example methods and apparatus to predict process quality in a process control system are disclosed. A disclosed example method includes receiving process control information relating to a process at a first time including a first value associated with a first measured variable and a second value associated with a second measured variable, determining if a variation based on the received process control information associated with the process exceeds a threshold, if the variation exceeds the threshold, calculating a first contribution value based on a contribution of the first measured variable to the variation and a second contribution value based on a contribution of the second measured variable to the variation, determining at least one corrective action based on the first contribution value, the second contribution value, the first value, or the second value, and calculating a predicted process quality based on the at least one corrective action at a time after the first time.