MétaCan
Menu
Back to cohort
Record W2626981768

A history of gravity separation at Richards Bay Minerals

2006· article· en· W2626981768 on OpenAlexaff
P.J. Walklate, J.R. Fourie

Bibliographic record

VenueJournal of the Southern African Institute of Mining and Metallurgy · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsGravity separationSeparator (oil production)ConcentratorSpecific gravityHeavy mineralSluiceSeparation (statistics)BayGeologyMineral processingEnvironmental scienceMineralogyEngineeringEnvironmental engineeringMathematicsElectrical engineeringMetallurgyGeochemistryMaterials scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Richards Bay Minerals has been operating gravity separation circuits since 1977, when the first mining concentrator and mineral separation plants were commissioned. Reichert cones were employed to produce a heavy mineral concentrate at the mine and wash-water spirals and shaking tables were used at the mineral separation plant to generate a suitable rutile and zircon-rich concentrate for further processing on dry electrostatic separation machines. Helical sluices, developed by Wright, provided an alternative to the Reichert cone and all future mining concentrator plants were equipped with these types of units. Roche (MDL) and Multotec developed improved designs of helical sluice and these eventually replaced the wash-water spirals and shaking tables in the mineral separation plant, reducing water requirements and improving plant availability. This paper descriptionbes the gravity separation circuits used at Richards Bay Minerals throughout its history and discusses the advantages and disadvantages of the separation equipment employed. i?½Newi?½ developments in gravity separation are also discussed, namely the Floatex density separator and the Kelsey centrifugal jig.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.276

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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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 designNot applicable
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

Citations6
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Southern African Institute of Mining and MetallurgySame topicMinerals Flotation and Separation TechniquesFrench-language works237,207