Modeling of ethylene copolymerization in nonisothermal high-pressure reactors using bifunctional initiators
Bibliographic record
Abstract
<p>Enhancing the performance of high-pressure LDPE process is valuable for polymer industry. However, the severe thermodynamics requirement of high pressure and temperature hinders the reaction process from getting simultaneously high monomer conversion and polymer molecular weights. Bifunctional peroxides used as initiators can boost the polymerization rate and alter rheological polymer properties. This article proposes a new kinetics model of ethylene/butyl acrylate copolymerization with bifunctional initiators in a high-pressure nonisothermal tubular reactor. Model predictions are compared with available data. A SQP optimization scheme is employed to determine a suitable wall temperature for each zone along the nonisothermal tubular reactor. In comparison with the monofunctional TBPPI peroxide, a lower amount of the bifunctional DHBPPI peroxide is needed to get a higher conversion in shorter residence time, but at the expense of higher thermal energy. The results also showed that polymers produced with bifunctional peroxides are significantly more branched. </p>
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".