Process Trajectory Monitoring for Real-Time Run Success Prediction
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Solution Overview
Problem
Existing methods for ensuring reproducibility and repeatability in executing industrial processes are costly and time-consuming, particularly in small-scale facilities, and there is a need for a more flexible solution that can be applied across various scales.
Innovation Solution
A method for identifying a preferred trajectory model by measuring and weighting variables affecting process quality over time, creating a weighted trajectory map, and comparing future runs to an ideal trajectory to determine the likelihood of success, using computer algorithms and environmental sensors to provide real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality control methods (e.g., Six Sigma) are implemented to ensure process reproducibility and repeatability, then process quality and reliability are improved, but implementation cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by establishing a reference trajectory model before actual process execution. Historical process data is analyzed in advance to create an ideal trajectory that represents successful process execution. This preliminary model serves as a benchmark against which real-time process variables are compared, enabling proactive quality assurance without requiring time-consuming post-process analysis or extensive quality control implementations.
Solution Approach 2:
The invention creates a virtual copy of the ideal process trajectory by modeling successful process executions. This digital reference trajectory captures the optimal path of process variables over time, allowing real-time comparison and deviation detection. Instead of implementing complex physical quality control systems, the method uses this virtual model to guide and monitor actual process execution, significantly reducing implementation costs and time while maintaining high reliability.
2Reliability
If comprehensive quality control methodologies are applied to monitor all process variables, then process reliability is improved, but system complexity and implementation cost increase
Solution Approach 1:
The system extracts only the essential information needed for quality control by comparing real-time process variables against the reference trajectory model. Instead of monitoring and analyzing all possible process variables simultaneously, the method identifies and focuses on deviations from the ideal trajectory, extracting only the critical deviations that indicate potential quality issues. This selective approach simplifies the monitoring system while maintaining comprehensive quality control.
Solution Approach 2:
The invention implements a feedback mechanism where real-time process variable measurements are continuously compared against the reference trajectory model, and deviations are fed back for immediate correction. This feedback loop enables the system to maintain process quality by automatically detecting and signaling deviations, eliminating the need for complex manual monitoring and control systems. The feedback principle allows simple sensors and algorithms to achieve comprehensive quality control through continuous comparison and correction.
3Reliability
If real-time monitoring of process variables is implemented to detect deviations, then process reliability is improved, but cost and resource consumption increase
Solution Approach 1:
The system applies partial action by monitoring process variables only to the extent necessary for detecting deviations from the reference trajectory. Instead of continuously analyzing all process parameters with high precision, the method uses threshold-based deviation detection that triggers detailed analysis only when necessary. This approach provides sufficient reliability for process success prediction while minimizing the energy and computational resources required for monitoring and analysis.
Data Source
AI summary
A method for determining whether a given run of a process having a defined protocol is on a trajectory for successful completion is provided. The method includes the step of initiating a run of the defined protocol of the process. During the initiated run, obtaining information reflecting variables that may affect the quality of the process. A preferred trajectory model for achieving a successful implementation of a process is also obtained. The information reflecting the variables that affect the quality of the process are compared with the preferred trajectory model. This comparison allows a determination of offset of the value of the determined variables to the value of the same variables of the preferred trajectory model. The magnitude or amount of offset is indicative of the whether the run initiated in step is on a path or trajectory for success.