Phase partition and <scp>KPI</scp> ‐related process monitoring for batch processes using a novel canonical correlation analysis strategy
Bibliographic record
Abstract
Abstract Key performance indicators (KPI)‐related process monitoring has been of great significance to ensure product quality and economic benefits for batch processes. Considering that different phases exhibit different characteristics, one of the key issues is how to partition the whole batch process into different phases and characterize them separately by multiple phase models. In order to model and monitor batch processes more accurately and efficiently, a novel canonical correlation analysis (CCA) strategy is proposed in this paper. The phase partition algorithm is designed based on the joint canonical variable matrix (JCVM). Different from previous methods, it considers the time sequence of operation phases and can distinguish the phase switches from dynamics anomalies. Using this algorithm, phases are separated in order from a KPI‐related perspective, revealing high correlation among variables. After phase partition, a novel multi‐phase local neighbourhood standardization CAA (MPLNSCCA) method focusing on KPI is set up for online monitoring, which could further address the misclassification problems. The advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli fermentation, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".