MétaCan
Menu
Back to cohort
Record W2126164989 · doi:10.1002/ceat.201000297

Development of a Computational Framework to Model the Scale‐up of High‐Solid‐Content Polymer Latex Reactors

2010· article· en· W2126164989 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Engineering & Technology · 2010
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsPolytechnique MontréalQueen's University
Fundersnot available
KeywordsComputational fluid dynamicsLaminar flowMixing (physics)RheologyScale (ratio)Flow (mathematics)Materials sciencePopulationMechanicsProcess engineeringMechanical engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract A computational framework, consisting of a laminar computational fluid dynamics (CFD) simulation model coupled to a multizonal population balance, is developed to assist in the scale‐up of high‐solid‐content (HSC) latex production and processing. Poly3D CFD software is used to generate flow fields inside a series of reactors; this information is then sent to the process model to assess the impact of nonhomogeneous mixing on the evolution of the latex particle size distribution (PSD) when concentrated latex suspension is altered via the addition of a coagulant. As the general shape of the PSD evolves, the model monitors changes in the rheological parameters in each zone of the reactor; the flow field is recomputed if a significant change in any of the properties is detected. The details of the framework are presented and its utility is demonstrated. Preliminary results indicate that nonhomogeneity inside the reactor will have an effect on the final latex PSD obtained.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.682

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 integrity0.0000.001
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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