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Record W2899172054 · doi:10.1515/htmp-2017-0106

A Thermodynamic Study of the Reduction of a Limonitic Laterite Ore by Methane

2018· article· en· W2899172054 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHigh Temperature Materials and Processes · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLateriteMethaneNickelMaterials sciencePyrometallurgyMagnetiteMetallurgyOxideCarbon fibersIron oxideFerroalloyChemistryComposite numberComposite materialSmelting

Abstract

fetched live from OpenAlex

Abstract The recovery of nickel from the oxidic nickeliferous laterite ores is receiving increasing attention due to the difficulty of recovering this metal from the sulphide ore deposits. One possible solution is to selectively reduce the nickel oxide in the ore, which could then be upgraded by, for example, magnetic concentration. In this article, a thermodynamic study was performed on the reduction of a limonitic laterite ore by methane. Methane was selected as the reducing agent as it has a lower environmental impact than carbon due to the reduced carbon dioxide emissions. The effects of temperatures and methane additions on the nickel recovery and nickel grade were investigated. High recoveries of over 95 % were predicted, but the grades were limited to about 2.5 % due to the formation of magnetite. The thermodynamic simulations for reduction by methane were in agreement with the experimental results in the literature for other reducing agents, reflecting the fact that the nickel oxide in the limonitic ore is relatively unstable. Thus, high recoveries could be achieved irrespective of the reducing agent involved.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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