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Record W4401456120 · doi:10.3390/met14080906

Analysis of the Feeding Behavior in a Bottom-Blown Lead-Smelting Furnace

2024· article· en· W4401456120 on OpenAlexaff
Kena Sun, Xiaowu Jie, Yonglu Zhang, Wei Gao, Derek O. Northwood, Kristian E. Waters, Hao Ma

Bibliographic record

VenueMetals · 2024
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill UniversityUniversity of Windsor
FundersNational Key Research and Development Program of China
KeywordsSmeltingMaterials scienceParticle sizeMetallurgyParticle (ecology)Molten metalRange (aeronautics)Slag (welding)Composite materialGeologyEngineeringChemical engineering

Abstract

fetched live from OpenAlex

Computational fluid dynamics (CFD) software was used to simulate the feeding behavior in a bottom-blown lead-smelting furnace. The results show that when the particle size is less than 30 μm, 20% of the particles are suspended in the gas phase and do not enter the melt pool for smelting, thus resulting in material loss. When the particle size exceeds 75 μm, the particles settle in the metal layer. When the particle size is 40–60 μm, the particles are distributed in the slag and metal phases, and the material is uniformly distributed in the molten pool; additionally, the average velocity of the particles exceeds 1.4 m/s, the average temperature exceeds 960 K, and the particles exhibit better behavior within this range, thus rendering it the optimal range of particle sizes for feeding.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations2
Published2024
Admission routes1
Has abstractyes

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