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Molecular Dynamics Investigation on Coke Ash Behavior in the High-Temperature Zones of a Blast Furnace: Influence of Alkalis

2017· article· en· W2770497890 on OpenAlexaff
Kejiang Li, Rita Khanna, Jianliang Zhang, Mohammed Bouhadja, Minmin Sun, Mansoor Barati, Zhengjian Liu, Chandra Veer Singh

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsCokeBlast furnaceChemistryViscosityOxygenThermodynamicsDiffusionBasic oxygen steelmakingChemical engineeringOrganic chemistrySteelmaking

Abstract

fetched live from OpenAlex

With specific focus on local structural order, bonding networks, transport properties, and viscosity of the molten ash oxides, we report molecular dynamics simulations on the influence of alkalis (Na 2 O and K 2 O) on coke behavior within a blast furnace. Atomistic simulations were carried out on the Al 2 O 3 –SiO 2 –CaO–K 2 O–Na 2 O system at 2223 K for a range and relative proportions of Na 2 O and K 2 O. Alkalis were seen to have a strong effect on the oxygen bonding networks; the relative proportions of bridging and nonbridging oxygen showed a sharp increase, while significant reductions were observed for tricluster oxygens. Total diffusion coefficients and viscosity showed a highly nonlinear dependence on the relative proportions of two alkalis with large changes observed in the simultaneous presence of alkalis as compared to their individual presence. Our studies have shown that the combined influence of alkalis on the viscosity of molten ash, and associated coke degradation within a blast furnace, is likely to be much smaller than previously perceived and could even be negligible for some alkali concentrations.

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)0.000
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 score0.235
Threshold uncertainty score0.414

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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