Multivariate SPC for startups and grade transitions
Bibliographic record
Abstract
Abstract Process transitions (grade changeovers, startups, and restarts) are very frequent in industry, and usually lead to the loss of production time, the production of off‐grade materials, and to inconsistent reproducibility of product grades. Two aspects of using multivariate statistical methods based on PCA and PLS to improve process transition performance using historical records of transition data are discussed. First, multivariate SPC approaches are proposed to determine if the process conditions for the commencement of a transition (“startup readiness”) are correct and to assess the successful completion of a transition (“production readiness for the new grade”). The latter is illustrated using a simulated fluidized‐bed process for the production of different grades of linear low‐density polyethylene. Second, analysis tools are suggested for diagnosing the reasons for past transition problems and for monitoring new transitions to ensure repeatable high quality transitions. The latter methods are aimed at reducing the amount of off‐specification materials and reducing transition time, as illustrated on industrial data from restarts of a polymerization process.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".