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Record W4387119927 · doi:10.1080/00084433.2023.2260664

Leaching kinetics of tungsten in scheelite tailings in sodium carbonate solutions at low temperatures (25–75°C)

2023· article· en· W4387119927 on OpenAlexaff
Terry C. Cheng, Ceferino Soriano, Heather E. Jamieson, Hendrik Falck

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsGovernment of Northwest TerritoriesQueen's UniversityNatural Resources Canada
Fundersnot available
KeywordsScheeliteTailingsLeaching (pedology)TungstenSodium carbonateKineticsChemistryCarbonateMineralogySodiumGeologySoil sciencePhysical chemistryPhysics

Abstract

fetched live from OpenAlex

The leaching kinetics of tungsten in scheelite tailings in sodium carbonate solutions at low temperatures (25–75°C) were found to closely follow the predicted values based on the shrinking core model (SCM), originally developed at elevated temperatures (150–190°C). Temperature demonstrated a profound positive effect on the leaching kinetics of scheelite. The apparent rate constants, k values, determined from leaching the scheelite tailings at low temperatures, were found to be a few orders of magnitude lower than those obtained at elevated temperatures (i.e. ∼ 10−7 vs. 10−4 s−1). The time required to extract over 90% of tungsten was estimated to be 15 days at 75°C, in contrast to 2 h at 200°C. The experimental kinetic data from leaching the scheelite tailings were found to consistently outperform the kinetic model prediction, by as much as 70% and 400% at 50 and 25°C, respectively. As observed at elevated temperatures, an increase in sodium carbonate concentration or agitation speed had little effect on the leaching kinetics at low temperatures. This study demonstrated the potential use of sodium carbonate solutions as a non-aggressive lixiviant in a percolation or dump leach operation, which could be considered as an environmentally viable reprocessing option for scheelite tailings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.211
Teacher spread0.199 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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