A lab‐scale reaction calorimeter for olefin polymerization
Bibliographic record
Abstract
Abstract A reaction calorimeter was built to follow slurry‐phase polymerizations of ethylene using different types of supported catalysts. It was shown that heat flow calorimetry, employing a high‐gain observer for the evaluation of the initial conditions was an extremely useful tool for the measurement of on‐line reaction rates, and a study of the influence of different parameters such as the stirring rate or solid content in real time. It was shown that if one uses solid contents under 30% (volume) then it is not necessary to account for the influence of this quantity on the overall heat transfer coefficient. Un calorimètre de réaction a été construit pour le suivi des polymérisations des phases de suspension de l'éthylène utilisant différents types de catalyseurs adaptés. Il a été démontré que la calorimétrie du flux thermique, utilisant un observateur à gain élevé pour l'évaluation des conditions initiales s'est avérée un outil extrêmement utile pour mesurer les taux de réaction en ligne, et pour l'étude en temps réel de l'influence de différents paramètres tels que la vitesse d'agitation ou le contenu en matière solide. Il a été démontré que si l'on utilise la matière solide en dessous de 30% (en termes de volume), alors il n'est pas nécessaire de prendre en compte l'influence de cette quantité sur le coefficient global de transfert thermique. Can. J. Chem. Eng. © 2010 Canadian Society for Chemical Engineering
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".