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Record W2785820657 · doi:10.3390/met8010069

Direct Production of Ferrochrome by Segregation Reduction of Chromite in the Presence of Calcium Chloride

2018· article· en· W2785820657 on OpenAlexafffund
Dawei Yu, Doğan Paktunç

Bibliographic record

VenueMetals · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsNatural Resources Canada
FundersArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaU.S. Department of Energy
KeywordsFerrochromeChromiteReduction (mathematics)CalciumProduction (economics)ChlorideChemistryMetallurgyMaterials scienceInorganic chemistryMathematicsSmeltingEconomics

Abstract

fetched live from OpenAlex

A solid reduction process is described whereby chromite is reduced with the help of calcium chloride to produce ferrochrome alloy powders with high metal recovery. The process involves segregation reduction of chromite using graphite as the reductant and calcium chloride as the segregation catalyst. Experiments were performed in the temperature range of 1200–1400 °C to evaluate the influences of various design parameters using both a thermogravimetric analyzer and an electric tube furnace with continuous off-gas analysis. The reduced products were characterized by scanning electron microscopy, X-ray powder diffraction, synchrotron X-ray absorption spectroscopy, and were subjected to wet chemical analysis. It was concluded that the addition of calcium chloride not only accelerated the carbothermic reduction of chromite but also promoted the formation and growth of individual ferrochrome alloy particles. The alloy formation within chromite particles was minimized, enabling the effective separation of ferrochrome alloy particles from the unwanted gangue without the need for fine grinding. Majority of the calcium chloride remained in a recoverable form, with a small percentage (<10 wt %) consumed by reacting with the siliceous gangue forming wadalite. Pure ferrochrome alloy powders were successfully produced with high metal recovery using elutriating separation.

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 score0.003
Threshold uncertainty score0.201

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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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

Citations11
Published2018
Admission routes2
Has abstractyes

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