Impact of Addition of a Catalyst or Its Support on Reactor Wall Coating Due to Electrostatic Charging during Fluidization of Polyethylene
Bibliographic record
Abstract
In this work the degree of fluidized bed electrification and reactor wall coating were investigated where a metallocene catalyst and its silica support were added to a bed of polyethylene resin and fluidized in a carbon steel reactor, 0.1 m in diameter and operating under ambient conditions. Tests were conducted to simulate industrial polyethylene reactors where both polyethylene and catalyst particles are present during the fluidization process. Since majority of the catalyst surface area is occupied by its support, the influence of the support was also examined. The electrostatic charge measurement technique used was similar to that described by Sowinski et al.1 Results demonstrated that both the catalyst and the silica support augmented the fluidized bed reactor wall coating due to their inherent large specific charge, which was partly gained during their passage through the injection tube. This finding demonstrates the possibility that in commercial gas-phase polyethylene fluidized bed reactors the catalyst particles could be one of the sources leading to reactor wall coating formation and growth. Results from both catalyst and its supports were very similar implying that the choice of the catalyst support could have a large impact on the polyethylene reactor electrostatic charging behavior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".