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Record W4320496163 · doi:10.3390/min13020254

Investigation of Particle Motion in a Dry Separation Fluidized Bed Using PEPT

2023· article· en· W4320496163 on OpenAlexafffund
Meng Zhou, Ozan Kökkılıç, Darryel Boucher, Mark Lepage, Thomas W. Leadbeater, Raymond Langlois, Kristian E. Waters

Bibliographic record

VenueMinerals · 2023
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsFluidized bedParticle (ecology)Tracking (education)Magnetosphere particle motionMaterials scienceMechanicsFluidizationParticle sizeMineralogyProcess engineeringChemical engineeringChemistryWaste managementPhysicsGeologyEngineering

Abstract

fetched live from OpenAlex

Developing dry separation methods to replace the commonly used water-based separation has become crucial due to increasing water shortages. One of the candidates for dry processing is gas–solid fluidized beds. The bed behavior and solid motion in fluidized beds have been investigated using various visual and numerical methods for decades. However, there are not enough studies focused on the separation behavior of the fine particles. This work details the investigation of particle motion through a fluidized bed using the positron emission particle tracking (PEPT) technique. Single-particle tracking is a powerful mechanism providing knowledge about separation mechanisms through direct visualization of the particle trajectory determined from recording the particle position over time. In this study, the movements of different-sized beds were characterized by tracking an activated single quartz particle and then by tracking an activated single hematite particle. The separation behavior of a heavy particle was determined for different-sized fractions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.266
Teacher spread0.227 · 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 designBench or experimental
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

Citations7
Published2023
Admission routes2
Has abstractyes

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