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Record W3193689565 · doi:10.11159/mmme21.116

Upgrading the Fe Grade of Magnetite Concentrate Using a MagneticHydro-Sizer

2021· article· en· W3193689565 on OpenAlexvenueno aff
Evans Vincent Rissenga, Willie Nheta

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
FundersUniversity of Johannesburg
KeywordsMagnetiteMagnetic separationMaterials scienceMetallurgyEnvironmental science

Abstract

fetched live from OpenAlex

In this study, a magnetic hydro-sizer was used to upgrade the final Fe concentration grade of a magnetite ore. The impact of the magnetic field intensity and water flow rate on the concentration grade were investigated. The chemical composition, mineralogical phases and surface topography of the feed to the hydro-sizer were analysed using XRF, XRD and SEM respectively. The results revealed that the feed to the magnetic hydro-sizer contained 61.25% Fe. Major phases in the feed are magnetite, hongquiite, calcium magnesium catena-silicate and quartz. SEM results showed that the sample was well liberated. A concentrate containing 63.9% Fe was produced whilst operating at particle size distribution of 80% passing 180 m, water flow rate of 290m 3 /hr and a magnetic intensity of 1350gauss. The higher the magnetic intensity, the lower the Fe grade of the concentrate. The higher the water flow rate, the higher the Fe grade of the concentrate. For a magnetic hydro-sizer, operating field should be between 1200 and 1350 gauss and a water flow rate above 260m3/hr for the final concentration of magnetite.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.398

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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