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Record W4285802397 · doi:10.1002/mren.202200027

Modeling Propylene Polymerization in a Two‐Reactor System: Model Development and Parameter Estimation

2022· article· en· W4285802397 on OpenAlexaff
Thanutchoke Charoenpanich, Siripon Anantawaraskul, João B. P. Soares, Phonpimon Wongmahasirikun, Supawadee Shiohara

Bibliographic record

VenueMacromolecular Reaction Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolypropylenePolymerizationPolymerDeconvolutionMolar mass distributionProcess engineeringMaterials scienceNattaYield (engineering)Work (physics)Chemical engineeringPolymer chemistryComputer sciencePolymer scienceThermodynamicsComposite materialAlgorithmEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract A series of two reactors is commonly used to produce commercial polypropylene resins to make products with a broad range of microstructures using different feeding policies and/or operating conditions in both reactors. A mathematical model describing the molecular weight distribution (MWD) and yield of polypropylene made with a Ziegler–Natta catalyst in a two‐reactor system is developed in this work. A new methodology to estimate the polymerization kinetic parameters combining the simultaneous MWD deconvolution and analysis of polymer yields in both reactors is also proposed. This pragmatic approach can be applied without knowledge of detailed polymerization rates and is, therefore, very convenient to quantify polymerizations in continuous pilot and/or commercial reactors, where such information is generally not available.

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 score0.414
Threshold uncertainty score0.559

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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