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Record W2085968212 · doi:10.4236/jmmce.2014.23025

Electrostatic Separation as a Characterizing Tool for the Insulation of Conductive Mineral Particles

2014· article· en· W2085968212 on OpenAlexaff
Philippe Bébin, Dave Mavrovic, Annie Rochette

Bibliographic record

VenueJournal of Minerals and Materials Characterization and Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsCegep de Thetford
Fundersnot available
KeywordsCoatingMaterials scienceComposite materialHematiteTalcParticle (ecology)Electrical conductorVolume (thermodynamics)Particle sizeMineralogyChemical engineeringMetallurgyChemistryGeology

Abstract

fetched live from OpenAlex

This work deals with a non-conventional use of a drum-type electrostatic separator. Indeed, the electrostatic separation process is used as a tool to evaluate the efficiency of different formulations of insulating coatings surrounding coarse and irregular conducting mineral particles. Our analysis is based on the change of the particle’s distribution in the conductive and the non-conductive pans after the electrostatic separation process. Different coating formulations were tested and we found that only hydrophobic components have to be used and that a composite formulation must be considered to sufficiently increase the coating thickness. Viscous hydrophobic oil combined with talc is a particularly relevant coating formulation for insulating hematite or ilmenite particles. The viscosity of the binder plays a crucial role as it guarantees the necessary cohesion of the coating itself. To evaluate the required thickness to obtain efficient insulating capabilities for the coating surrounding coarse and irregular mineral particles, we linked the experimental volume ratio between the coating and the particles and the theoretical ratio. The experimental volume ratio is calculated using the weights of all the materials used and their respective densities. Whereas, the theoretical one is calculated using the volume the mineral particles would have, considering them all identical, spherical, with a smooth surface and the volume of the coating being uniform with the same thickness on each mineral particle. We found that an efficient insulating coating for hematite particles means a thickness of 9.5% of the average mineral radius, ranging from 125 μm to 1250 μm, resulting in an equivalent insulating thickness of about 48 μm for particles of around 1 mm in diameter. Interestingly, all results originate from the analysis of the change occurring in the particle’s distribution in the different collecting pans of an electrostatic separator.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2014
Admission routes1
Has abstractyes

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