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Feasibility of concentrating potassium minerals from Verdete ore by cell flotation

2021· article· en· W3162132719 on OpenAlexaff
Laryssa Batista Christina Cordeiro, Michelly dos Santos Oliveira, Kátia D. Oliveira, Marcos A.S. Barrozo, C.N. Ávila-Neto

Bibliographic record

VenueThe Journal of Engineering and Exact Sciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsCarleton University
FundersFundação de Amparo à Pesquisa do Estado de Minas Gerais
KeywordsCalcinationSodium silicateFroth flotationChemistryDepressantPotassiumOleic acidMass fractionFraction (chemistry)SodiumMineralogyNuclear chemistryChromatographyMetallurgyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

A silicate ore with K2O content above 10%, found in the central region of Minas Gerais (Brazil), called Verdete, was floated in flotation cell. The goal was to evaluate the flotation behaviour of the ore constituents (glauconite, muscovite, K-feldspar and quartz) relative to the use of different collectors (fatty acid soap obtained from rice oil, amine and oleic acid) and depressors (gelatinized cornstarch and sodium silicate). Flotation of the calcination products of Verdete with MgCl2 and CaCl2 were also evaluated. Mass recovery of flotation carried out with Verdete reached a maximum of 53% when amine and sodium silicate were used as collector and depressant, respectively. Mass recovery was directly proportional to collector dosage, and was also influenced by the type of depressor. Calcination of Verdete with MgCl2?xH2O partially consumed the micas and generated MgO. Flotation of this calcination product concentrated MgO in the floated fraction, reaching 93% when oleic acid was used as collector.

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)0.001
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 score0.049
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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

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