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Record W2911816406 · doi:10.1002/cjce.23476

Slurry rheology in mineral processing unit operations: A critical review

2019· review· en· W2911816406 on OpenAlexafffundvenue
Nestor Cruz, J. Forster, Erin R. Bobicki

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRheologySlurryDewateringTailingsMineral processingGrindingProcess engineeringUnit operationMaterials scienceParticle (ecology)Environmental scienceGeologyMetallurgyChemical engineeringEngineeringGeotechnical engineeringComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Knowledge of slurry rheology in mineral processing is essential for the optimization of wet‐grinding, dewatering, transport, and tailings management and it is becoming important in flotation due to the increasing need to process complex ores with a high clay mineral content. Slurries of these ores often display complex rheological behaviour even at low solids concentration, and traditional rheology measurements in mineral processing, such as yield stress and apparent viscosities, may not provide the critical information needed for process optimization. The objective of this paper is to provide a comprehensive review of some of the previous rheology studies performed at the bench, pilot plant, and industrial scale for different unit operations and to discuss the potential for alternative rheology measurements to provide a better insight into colloidal particle interactions in slurries with clay minerals. Firstly, the fundamentals of slurry rheology and the influence of colloidal particles are presented. Secondly, rheology measurements for suspensions are described briefly. Thirdly, a review of rheology studies in grinding, classification, flotation, dewatering, and tailings management is done, outlining possible ways of determining types of particle associations in slurries. Finally, recommendations for future research directions are provided.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.319
Teacher spread0.269 · 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
GenreReview

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

Citations52
Published2019
Admission routes3
Has abstractyes

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