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Record W4200085940 · doi:10.1016/j.clet.2021.100386

Composition and Cr- and Fe-speciation of dust generated during ferrochrome production in a DC arc furnace

2021· article· en· W4200085940 on OpenAlexafffund
Eleanor J. Berryman, Doğan Paktunç, David Kingston, Johan P. Beukes

Bibliographic record

VenueCleaner Engineering and Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsNational Research Council CanadaNatural Resources Canada
FundersBrookhaven National LaboratoryOffice of ScienceGovernment of CanadaNatural Resources CanadaU.S. Department of Energy
KeywordsFerrochromeChromiteSmeltingSlag (welding)Electric arc furnaceMetallurgyFlux (metallurgy)Environmental chemistryChemistryEnvironmental scienceMaterials science

Abstract

fetched live from OpenAlex

A potential hazard associated with ferrochrome production is the unintentional generation of Cr(VI) in the off-gas dusts produced during smelting. Cr(VI) is a well-known environmental toxin and genotoxic carcinogen. Although Cr(VI) has been identified in baghouse dusts associated with submerged-arc furnaces, its occurrence in DC-arc-furnace dusts has not been previously investigated. In this study, we report the bulk composition and phase make-up, as well as the surface and bulk speciation of Cr and Fe of dusts generated during smelting of chromite ore under basic slag conditions in a pilot-scale DC arc smelter. Dusts are captured from both within the DC furnace as well as along the off-gas handling stream, after passing through the afterburner. The dusts primarily comprise feed material (chromite, flux, reductant), aerosolized slag, and glassy spherules, interpreted as off-gas condensates. Dusts collected from within the furnace, known as freeboard dusts, are dominated by feed materials, with elevated amounts of flux (lime) and lesser amounts of slag and spherules. In contrast, spherules dominate dusts collected along the off-gas handling stream by dust separators that capture the increasingly fine-grained fraction of dust, with the highest proportion of spherules occurring in baghouse dusts. Chromite is the primary host of Cr in all dust samples, whereas the spherules have low Cr contents. Cr(VI) was identified by X-ray photoelectron spectroscopy in the surfaces of dust collected from all parts of the smelter system considered, including from the furnace freeboard, despite the overall reducing conditions present therein. Cr(VI) concentrations are sufficiently high in dusts collected from the off-gas handling stream (post-afterburner, cyclone, and baghouse) to be detected in the bulk dust samples by X-ray absorption spectroscopy. The higher concentration of Cr(VI) in the finer-grained off-gas dusts, especially the baghouse dusts, is consistent with the increased abundance of spherules, the major Cr(VI)-hosting phase, in these samples. Fe is similarly more oxidized in dusts collected down the off-gas handling stream compared to dusts collected from within the furnace, which are dominated by Fe(II). Fe(III) hosted by FeOOH is predominate in dusts collected from all locations along the off-gas handling stream. These results demonstrate that there is sufficient oxygen ingress and sufficiently high temperatures in the closed DC furnace for Cr(VI) to be generated in the freeboard off-gas dusts. Cr(VI) is associated with the finest grained fraction of dusts, underlining the importance of ensuring the continual improvement of dust-capture systems, especially in terms of their ability to target increasingly finer fractions of dust.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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 designObservational
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

Citations10
Published2021
Admission routes2
Has abstractyes

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