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Record W2336726912 · doi:10.14288/1.0081114

Scale-up and modeling of flotation columns using surface areas

2009· article· en· W2336726912 on OpenAlexaff
Ian Matthew Flint

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScale (ratio)Environmental scienceGeologyGeographyCartography

Abstract

fetched live from OpenAlex

This research presents three models of froth flotation that recognize flotation as an interfacial phenomenon wherein the rate of solids-surface-area removal is related to the bubble-surfacearea flux. These models use two "streams", air and liquid, within the vessel. The solids within the column are associated with one, or both, of these streams. The procedures and benefits of batch tests for flotation columns are also outlined. The first model uses this batch data and the simplex method of non-linear regression to determine four model parameters: a kinetic rate constant, maximum recovery, entrainment and carrying capacity. These parameters are then used in the second model in order to estimate continuous column performance. Within the continuous model, carrying capacity is determined by overflow bubble size; recovery within the froth zone is determined by loss of bubble surface area; and solids transfer from liquid- to gas-phase is estimated by kinetic relationships and the axial dispersion model. The prediction performance of the model is verified using both batch mechanical cell and column flotation cell batch data. The final model "framework" characterizes the effect of bubble residence time and bubble loading rates within a flotation cell using mechanistic bubble - particle collision and attachment relationships.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.951

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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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 designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

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