Improved operating scenarios for the production of acrylonitrile‐butadiene emulsions
Bibliographic record
Abstract
Abstract This article focuses on important aspects related to the production of acrylonitrile‐butadiene rubber (nitrile rubber or NBR) emulsions using a train of eight continuously stirred tank reactors. The first aspect we discuss concerns miscellaneous operating procedures, targeted toward achieving desired properties of the NBR emulsion and at the same time reducing the amount of off‐spec material during the transient period. In one of the operating procedures discussed, the first reactor is started full of “batch recipe,” whereas the remaining reactors in the train are half full with all reaction ingredients. In another case, we discuss an alternative design scenario, where the first reactor in the train is of a much smaller volume compared with the following reactors. The benefits of these two operating (production) scenarios are illustrated by comparison of different polymer and latex properties obtained with the commonly used procedures. Second, we discuss a novel criterion that can quantitatively distinguish between Cases I and II kinetics emulsion polymerization behavior with respect to continuous reactor stability (oscillatory behavior). Finally, additional feed policies are addressed, such as splitting the more reactive monomer and/or other reaction ingredients among the reactors of the train for better control of certain desired properties. POLYM. ENG. SCI., 2013. © 2012 Society of Plastics Engineers
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".