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Record W4312067919 · doi:10.1002/mren.202200057

A Thermodynamic Simulation Package for Catalytic Polyolefin Reactors: Development and Applications

2022· article· en· W4312067919 on OpenAlexaff
Arash Alizadeh, Vasileios Touloupidis, João B. P. Soares

Bibliographic record

VenueMacromolecular Reaction Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAutoclavePolyolefinPolymerizationMaterials scienceThermodynamicsSlurryProcess engineeringPolymerChemical engineeringEngineeringPhysicsComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract A thermodynamic simulation package is developed for the catalytic polymerization of olefins in autoclave slurry, loop slurry, gas‐phase, and autoclave solution reactors. The number of components in the reactors may vary from two to six. The simulator uses the Sanchez–Lacombe theory, one of the major thermodynamic models in the polymer industry. Step‐by‐step instructions on how to specify the system, derive and solve the resulting nonlinear equations, and estimate the required thermodynamic properties are given. The software is used to describe ethylene/1‐hexene copolymerizations with hydrogen in different reactors under industrial conditions. These simulations demonstrate why thermodynamic effects must be included in olefin polymerization models.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.847

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 head0.007
GPT teacher head0.206
Teacher spread0.200 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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