Spectroscopic Blend End-Point Detection Using Rolling F-Test Blocks
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Solution Overview
Problem
Conventional methods for detecting the end point of a blending process based on spectral properties often result in unreliable and early detection, failing to accurately determine when a steady state has been reached, leading to inefficiencies and resource waste.
Innovation Solution
A method and device that utilize a rolling F-test with moving dual blocks to identify a pseudo steady state end point, generating raw and statistical detection signals based on spectroscopic data, without relying on historical data, to determine when a blending process has achieved a steady state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to detect the end point of blending process based on spectral properties, then detection is performed continuously throughout the process, but the detection results are unreliable and indicate end point too early, leading to inaccurate determination of steady state achievement
Solution Approach 1:
The blending process is divided into distinct phases: unsteady state, pseudo steady state, and steady state. The detection method segments the spectral data into moving dual blocks that correspond to different process phases, allowing accurate identification of transitions between phases. This segmentation enables reliable end point detection by focusing analysis on the pseudo steady state transition rather than the entire blending process.
Solution Approach 2:
The method performs preliminary identification of the pseudo steady state end point using an F-test on moving dual blocks before conducting the final steady state determination. This preliminary action prepares the detection system by establishing reference points and filtering out early false positives, ensuring that the final end point detection is based on reliable steady state criteria rather than transient fluctuations.
2Productivity
If conventional end point detection methods are used, then the process may be terminated early based on unreliable signals, but this leads to inefficient resource utilization and waste due to incomplete blending
Solution Approach 1:
The method implements continuous feedback through the F-test on moving dual blocks, which monitors spectral variance throughout the blending process. The feedback mechanism provides real-time information about the blending state, allowing the system to distinguish between transient fluctuations and true steady state achievement. This reliable feedback ensures process termination occurs at the correct end point, optimizing both efficiency and resource utilization.
Solution Approach 2:
The detection method uses dynamic moving dual blocks that adapt to the changing spectral characteristics during blending. The block size and positioning are optimized to capture the transition from pseudo steady state to steady state, allowing the system to dynamically respond to the actual blending progress rather than relying on fixed time-based or arbitrary spectral thresholds.
3Reliability
If the blending process is monitored continuously to ensure accurate steady state detection, then detection reliability is improved, but the complexity of the detection system and data processing increases
Solution Approach 1:
The method extracts only the essential information needed for end point detection by using the F-test on moving dual blocks to identify spectral variance patterns. Rather than analyzing the entire spectral dataset continuously, the system extracts key features during the pseudo steady state transition, significantly reducing computational complexity while maintaining high detection reliability through focused analysis of critical process phases.
Data Source
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AI summary
In some implementations, a device (100) may identify (104,420), based on spectroscopic data (102), a pseudo steady state end point indicating an end of a pseudo steady state associated with the blending process. The device may identify (106,430) a reference block and a test block from the spectroscopic data based on the pseudo steady state end point. The device may generate (108,440) a raw detection signal associated with the reference block and a raw detection signal associated with the test block. The device may generate (450) a statistical detection signal based on the raw detection signal associated with the reference block and the raw detection signal associated with the test block. The device may determine (460) whether the blending process has reached a steady state based on the statistical detection signal.