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Record W4402238664 · doi:10.3390/min14090906

Investigation of the Flotation of an Ore Containing Bastnaesite and Monazite: Kinetic Study and Process Flowsheet Simulation

2024· article· en· W4402238664 on OpenAlexaff
Claude Bazin, Jean‐François Boulanger

Bibliographic record

VenueMinerals · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité Laval
Fundersnot available
KeywordsMonaziteHydrometallurgyRare earthMineralMineral processingChemistryMetallurgyMineralogyGeologyGeochemistryMaterials scienceZirconSulfuric acid

Abstract

fetched live from OpenAlex

Laboratory flotation tests carried out using an ore sample containing Rare Earth Elements (REEs) present as monazite and bastnaesite show that the flotation of monazite is slower and yielded lower recovery than that of bastnaesite. Results show that when studying the performances of a concentration process for an REE ore, it is essential to not look only at the behavior of the individual REEs but to convert elemental assays into mineral assays to obtain the mineral’s actual response to the concentration process. The results of the laboratory flotation tests are used to calibrate a flotation simulator applied to study different circuit configurations for the concentration of the REE minerals. Indeed, it is shown that for the studied ore, two cleaning stages of a rougher concentrate are sufficient to produce a concentrate with a Total Rare Earth Oxide (TREO) grade above 40%, which is acceptable for the subsequent hydrometallurgical process. The simulation also shows that it may be feasible, if required for the hydrometallurgy step, to separate bastnaesite and monazite by taking advantage of the different flotation kinetics of the two minerals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.217

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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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