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Record W4400900222 · doi:10.1177/03019233241259298

Solid carbonaceous materials transformation during pulverised coal and natural gas co-injection

2024· article· en· W4400900222 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.

Bibliographic record

VenueIronmaking & Steelmaking Processes Products and Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsArcelorMittal (Canada)Natural Resources Canada
Fundersnot available
KeywordsCoalNatural gasWaste managementNatural (archaeology)Environmental scienceMaterials scienceGeologyEngineering

Abstract

fetched live from OpenAlex

Co-injection of coal and gaseous fuel, such as natural gas (NG), is common practice promoting pulverised coal gasification in blast furnace (BF) ironmaking. The high hydrogen content in NG makes it possible to act as cleaner reductant to reduce iron ore to produce hot metal, which will result in reduced CO 2 emissions during the ironmaking process. In this work, selected parameters affecting the overall performance of the pulverised coal and natural gas co-injection system were studied using the CanmetENERGY injection test rig, including coal injection rate, natural gas rate and blast oxygen enrichment. Increase in combustion intensity promotes conversion of injected coal into solid carbonaceous material with relatively low reactivity with CO 2 . Hence, it reduces the competitiveness of combustion residues for oxygen in the raceway to continue the gasification process. NG co-injection reduces the coal combustion intensity but enhances the reactivity of combustion residues by competing the oxygen in the raceway.

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 score0.245
Threshold uncertainty score0.815

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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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