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Record W7162037212 · doi:10.82308/28533

Investigation of critical coalescence concentration in flotation machines

2022· dissertation· en· W7162037212 on OpenAlexaboutno aff
Jeffrey Opoku

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleCoalescence (physics)Froth flotationReagentMineral processingSauter mean diameterVolumetric flow rate

Abstract

fetched live from OpenAlex

Flotation is a widely used mineral separation technique that relies on several components functioning at the optimum conditions in order to achieve a well-tuned integrated system. The recovery of mineral particles is mainly attributed to the bubbles generated and by rule the smaller the bubbles the more surface area available for the transport of mineral particles to the froth zone. The use of surfactants such as frothers aid in the preservation of small bubbles that are generated in a flotation machine. This function is accredited to their coalescence prevention characteristics based on their chemical and structural compositions. Additionally, research shows that some collectors also possess this coalescence prevention and foaming capabilities. Several methods have been introduced to characterize and measure this frother function and one such method is the critical coalescence concentration (CCC). It is determined from the plot of Sauter mean bubble diameter (D32) and reagent addition (concentration). It has been demonstrated that operational parameters influence the CCC but there is less data to show the effect, different flotation machines have on CCC.In this study, a test program was designed to compare CCC values between same type flotation machines in two-phase (water-air). Two Denver cell stations were used to measure bubble size using the photographic technique developed at McGill. The stations were designed to control gas flow rate and included the McGill bubble viewer for bubble size measurements. The reagents used for this study were analytical grade glycol (PPG 425), alcohol (MIBC) and thionocarbamate collector (Flottec 1234). Bubble size measurement was repeated a minimum of 3 times for each of the reagent concentrations (0, 1, 2, 5, 10, 20, 40) at superficial gas velocity of 0.5cm/s and 1.5 cm/s and the margin of error was less than 13% for a 95% confidence level. The results demonstrated that CCC values were similar for frothers for the same-type flotation machines.To validate the work done in two-phase, a design of experiment was also conducted to investigate the effects of solids on bubble size. The ore of choice for this experimental design was a copper sulphide one. The response of the experiment was bubble size (D32), the three independent variables were solid percent (S) measured in %, reagent concentration (C) measured in ppm, superficial gas velocity (Jg) measured in cm/s and their five corresponding levels (±β, ±1, 0). The results indicated that the presence of solids did not significantly influence bubble size

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
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)0.0010.000

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 head0.016
GPT teacher head0.306
Teacher spread0.290 · 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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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