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Record W4309928723 · doi:10.1080/00084433.2022.2146954

An investigation into processing fine magnetite using a magnetic hydrocyclone

2022· article· en· W4309928723 on OpenAlexaff
Meng Zhou, Lilla A. Farkas, Ozan Kökkılıç, Raymond Langlois, N.A. Rowson, Kristian E. Waters

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsHydrocycloneMagnetiteArithmetic underflowMineral processingSlurryGrindingHematiteMaterials scienceUltrafine particleMagnetic separationMetallurgyAdsorptionWastewaterWaste managementEnvironmental scienceMineralogyChemistryComposite materialEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

As a consequence of fine grinding in the mineral processing industry, fine material containing quantities of valuable metal is being lost to tailing dams, wastewater and mine drainage. The processing of ultrafine particles has always been a challenge in the mining industry, thus typically, fine components are often considered waste during mineral processing. In addition, magnetic adsorbents are also being investigated as a method of processing wastewater, leading to the need for methods of recovering fine material post adsorption. A method of fines recovery, using a magnetic hydrocyclone to increase ultrafine and fine (−38 µm) material recovery from slurry was investigated in this paper. The attached permanent (Nd-Fe-B) magnet concentrated magnetite particles to the underflow and increased recovery by 15.4%, 5.6% and 2.0% on average for ultrafine (<5 µm) magnetite and two size distributions of fine magnetite (<38 µm), respectively, when compared to a conventional hydrocyclone.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.980

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.000

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 head0.012
GPT teacher head0.239
Teacher spread0.228 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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