Modeling and optimization of ethylene copoplymerization in high pressure reactor using difunctional initiators
Bibliographic record
Abstract
Free radical (co-)polymerization of low-density polyethylene (LDPE) is carried out commonly in high pressure autoclaves or tubular reactors. The severe thermodynamic conditions of the process hinder ethylene from going to full conversion. One remedy to improve the monomer conversion is to investigate the effectiveness of initiators, such as difunctional organic peroxides. In the present work, a kinetic model based on a postulated reaction mechanism for free radical ethylene (co-) polymerization initiated by difunctional initiators is applied to analyze the dynamic behavior of a continuous LDPE isothermal autoclave reactor and a non-isothermal tubular reactor. The model describes the rates of initiation, propagation and the population balance equations. It predicts variations of the initiator and monomer concentrations and reaction temperature as well as molecular weight distribution of reactive macromolecular species. Variations of the pressure, velocity and transport/physical properties of the reacting mixture were accounted for in the tubular reactor. Model predictions are compared to experimental data collected from literatures for one monofunctional (dioctanoyl) and two difunctional initiators namely, (2,2-bis(tert-butylperoxy)-butane and 2.5-dimetyl hexane-2t-butylperoxy-5perpivalate). In comparison with dioctanoyl peroxide, polymerization with difunctional initiators requires a lesser amount of initiators and gives higher ethylene conversion in a shorter time. The modeling of LSPE with difunctional initiators was then extended to ethylene copolymerization with vinyl acetate and butyl acrylate. The model helps to determine the influence of reactivity ratio on the end-use product properties. Details of modeling a multiple feed LSPE tubular reactor are included for both homo- and co-polymerization reactions. The effect of monomer and initiator injections on the productivity and (co)polymer rheology and composition are investigated as well. Finally, an optimization method was applied to determine the optimal values of control variables via maximization of an objective function expressed in terms of monomer conversion, number average molecular weight, polydispersity and final desired composition of copolymer product. The results show that we can obtain a polymer with desired characteristics by proper manipulation of the control variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".