Modeling of ethylene polymerization with difunctional initiators in tubular reactors
Bibliographic record
Abstract
Abstract The severe thermodynamic conditions of the high‐pressure ethylene polymerization process hinder ethylene from going to full conversion. One remedy to improve the monomer conversion is to make effective use of difunctional peroxides. Multifunctional peroxides can accelerate the polymerization rate, produce branching, and modify the rheological properties of molten polymers. This article proposes a kinetic model based on a postulated reaction mechanism for ethylene polymerization initiated by difunctional initiators in a high‐pressure tubular reactor. Three peroxides suitable for ethylene polymerization were compared for their effectiveness. Compared to dioctanoyl peroxide, the two difunctional peroxides considered performed much better for the higher temperature regions of the reactor and gave ethylene conversions nearly twice as high for only half of the initial amount of dioctanoyl. They also generated low‐density polyethylene polymer with a broader molecular weight distribution and longer chain branching. These two important polymer characteristics can influence the end‐product rheological properties. Injecting fresh ethylene at different points along the reactor improved the conversion and produced more branched polymer. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".