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Record W4251417491 · doi:10.32920/ryerson.14657715.v1

Modeling, Simulation and Optimal Control of Ethylene Polymerization in a High-Pressure Tubular Reactor

2021· preprint· en· W4251417491 on OpenAlexaff
Frederick Zuojiang Yao

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsToronto Metropolitan UniversityDow Chemical (Canada)
FundersStrong
KeywordsPolymerizationMonomerLow-density polyethyleneMaterials scienceAutoclavePolyethyleneBulk polymerizationEthylenePolymerPolymer chemistryChemical reaction engineeringChemical engineeringThermodynamicsRadical polymerizationChemistryComposite materialOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

<p>Low-density polyethylene (LDPE) is a very important material for the manufacture of engineering and commodity plastics. The products are usually produced in high-pressure free radical polymerization either by autoclave or by tubular processes. This study presents a kinetic model based on a postulated reaction mechanism to describe the evolution of monomer, initiator consumption and the average molecular weights of ethylene polymerization in a high-pressure polymerization tubular reactor. The variations in the density and viscosity of the reacting mixture along multiple injection points for an initiator are used along reactor length is explicitly taken into account. A simulation program is developed to solve the mathematical model and the results are compared with those reported earlier. Further, the optimal control of the LDPE reactor is carried out using genetic algorithms to maximize monomer conversion using the jacket temperature of heat-exchange fluid as a control function along reactor length. The results indieate a 22-40% increase in the reduction of monomer concentration.</p>

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.250
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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