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Record W4387460183 · doi:10.1080/00084433.2023.2266209

Thermodynamic assessment of tin-smelting from cassiterite concentrates

2023· article· en· W4387460183 on OpenAlexaff
Elmira Moosavi‐Khoonsari, Sina Mostaghel

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2023
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsSNC-Lavalin (Canada)École de Technologie SupérieureUniversity of Toronto
Fundersnot available
KeywordsCassiteriteLiquidusSmeltingMetallurgySlag (welding)Lead smeltingTinMaterials scienceMineralogyChemistryAlloy

Abstract

fetched live from OpenAlex

Tin is a critical and rare metal produced via carbothermic reduction smelting of upgraded concentrates in two steps, primary and secondary reduction. There is limited literature available on cassiterite smelting and the thermodynamic behaviour of Sn during primary and secondary reduction processes. The present work performs a systematic assessment/optimisation of smelting parameters of a simplified cassiterite concentrate for the first time. This assessment was carried out with the aid of thermochemical analysis. The effect of process variables including reduction extent, temperature, and flux addition on the outputs of the primary and secondary reduction steps was studied. The effects of temperature and slag composition on slag liquidus temperature and viscosity were determined. The effect of recycling the process byproduct, the hard head (HH), on operational parameters and outcomes, especially Sn recovery, was also investigated, and a series of optimum process conditions was proposed.

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)0.000
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.229
Teacher spread0.219 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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