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Record W6939588272 · doi:10.6084/m9.figshare.25551598

Assessment of the impact of grinding conditions and water quality on the flotation of rare earth elements bearing minerals using hydroxamic acid

2024· article· en· W6939588272 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsGangueDolomiteGrindingTailingsFroth flotationRare earthBleachPyriteParticle size

Abstract

fetched live from OpenAlex

Froth flotation is commonly used for the separation of rare earth minerals (REMs). A better understanding of flotation is of interest for REMs and non-sulfide minerals that carry other elements such as niobium and lithium. This study explores the effect of grinding conditions and water quality on the flotation performance of REMs at laboratory scale using material from the Ashram carbonatite deposit (Canada). Denver cell testing was used to evaluate how different particle sizes (P<sub>80</sub> between 20 and 34 µm) and rod size distributions in conventional grinding impacted the performance of REM flotation using a hydroxamic acid collector (1120 g/t). The effect of water quality (i.e. presence of rust or other species) was also evaluated. REMs, mostly monazite, represented 3% of the feed material mass with dolomite as the main gangue mineral. A P<sub>80</sub> of 24 µm strikes a compromise between sufficient liberation and limited entrainment. Grinding media corrosion negatively affected separation efficiency, causing recovery of quartz, which was possibly activated. While entrainment is responsible for over 50% of dolomite recovery, aggregation and/or slow gangue flotation account for another 40% and play an important role in achieving selectivity, highlighting the role of pulp chemistry in selective flotation of REMs.

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 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 score0.192
Threshold uncertainty score0.995

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)0.0050.000

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 head0.078
GPT teacher head0.377
Teacher spread0.299 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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