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Record W2735632553 · doi:10.3390/min7070118

Concentration at the Minimum Bubble Velocity (CMV) for Various Types of Flotation Frothers

2017· article· en· W2735632553 on OpenAlexaboutno aff
Przemyslaw B. Kowalczuk, Jan Zawała, Jan Drzymała

Bibliographic record

VenueMinerals · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleCoalescence (physics)AlkylTerminal velocityEthylene glycolChemistryPulmonary surfactantChromatographyThermodynamicsMechanicsChemical engineeringPhysicsOrganic chemistryBiochemistryEngineering

Abstract

fetched live from OpenAlex

This paper presents the determination of a concentration at the minimum bubble velocity (CMV) for different types of frothers, such as straight and branched alkyl chain aliphatic alcohols, 1,ω-diols, poly(propylene glycol) and poly(ethylene glycol) alkyl ethers, n-alkyltrimethylammonium bromides, commercial frothers and others. The values of terminal rise bubble velocity were reviewed from the experimental data published in the literature for two different types of columns, i.e., a short PAS (used in Polish Academy of Sciences) of height (35 cm) and a long McGill of height (350 cm). The obtained empirical equation is universal and allows one to rapidly and accurately determine the CMV for all surfactants. The proposed empirical model can also be used to predict the terminal bubble velocity–frother concentration curve by knowing the maximum and minimum terminal velocities, as well as the values of CMV. Assessment and usefulness of frother characterization parameters (i.e., concentration at the minimum bubble velocity (CMV), dynamic frothability index (DFI) and critical coalescence concentration (CCC)) were shown in the flotation of coal.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.025
GPT teacher head0.294
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2017
Admission routes1
Has abstractyes

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