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Record W2328526366 · doi:10.1021/ie501745h

Evolution of Limestone Particle Size Distribution in an Air-Jet Attrition Apparatus

2014· article· en· W2328526366 on OpenAlexaff
Gang Xiao, John R. Grace, C. Jim Lim

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsUniversity of British Columbia
FundersScience and Technology Department of Zhejiang ProvinceNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsAttritionParticle-size distributionParticle sizeJet (fluid)Fluidized bedMineralogyMaterials scienceParticle (ecology)MechanicsThermodynamicsChemistryGeologyPhysics

Abstract

fetched live from OpenAlex

Attrition is very important in fluidized-bed reactors, especially for limestone-based sorbents used to capture SO 2 and CO 2 . The initial size distribution of the sorbent particles is commonly used to predict the rate of attrition, but the distribution changes during the process. Limestone samples of narrow particle size distributions (PSDs) were tested in an ASTM air-jet attrition apparatus, and the evolution of the PSDs was investigated. The entropies of information on log-normal functions, a measure of the disorder of particles being entrained into the jets, remained almost unchanged during the attrition process. This indicates that particles of greater entropy generally experience attrition at a greater rate in an air-jet apparatus, except for particles which are too large to be mobilized or too small to stay in the bed.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.312
Teacher spread0.253 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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