Nitrile Rubber Reactor Operation Troubleshooting with Principal Component Analysis
Bibliographic record
Abstract
Principal Component Analysis (PCA) is employed as a tool in order to demonstrate yet another application of the technique, and, most importantly, to show that results from the statistical multivariate technique do make physico-chemical sense. The operation of a typical emulsion copolymerization of acrylonitrile and butadiene (nitrile butadiene rubber, NBR) is used as an example of process troubleshooting. In more general terms, a statistical tool is used to aid process data analysis and process operation (recipe, product property) troubleshooting. The goal is to produce consistent Mooney Viscosity (MV) among different batches. The observation is that varying induction times lead to Mooney Viscosity inconsistencies. Firstly, we show results from the application of PCA to process data. Secondly, we deal with an even more important (and often ignored) question by examining whether the trends indicated by PCA make process sense.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".