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Record W4224218508 · doi:10.3390/min12040454

Using Top-of-Froth Conductivity to Infer Water Overflow Rate in a Two-Phase Lab-Scale Flotation Column

2022· article· en· W4224218508 on OpenAlexafffund
Mark Lepage, C.O. Gómez, Kristian E. Waters

Bibliographic record

VenueMinerals · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsEnvironmental scienceEntrainment (biomusicology)Volumetric flow rateGangueConductivityPetroleum engineeringMaterials scienceChemistryGeologyMechanicsMetallurgy

Abstract

fetched live from OpenAlex

The metallurgical performance of a flotation machine is largely defined by phenomena occurring in the froth zone. The water content in the froth affects recovery by influencing froth stability and mobility and, at the same time, reduces grade by mechanical entrainment of gangue particles in the overflow water. Efficient operation requires a compromise between the water carried by bubbles from the collection zone and that which overflows. It is believed that the most suitable operating strategy could be based on the measurement of froth water content, as a strong correlation with water overflow is anticipated. This work reports the testing results of an in situ electrical conductivity sensor continuously measuring the froth zone water content in a laboratory-scale flotation column. The test program included simultaneous measurement of froth conductivity and water overflow rates for changes in gas flow rate and frother concentration. The results show a stronger dependence of the measured top-of-froth water content on frother concentration than on the gas flow rate. A relatively linear trend was shown between top-of-froth water content and water overflow rate for a given air rate and frother.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0080.000

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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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.

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

Citations5
Published2022
Admission routes2
Has abstractyes

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