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Record W2756963198 · doi:10.5151/2594-357x-27542

ADVANCED TECHNOLOGY DEVELOPMENTS IN REMEDIAL STAVE COOLING

2016· article· en· W2756963198 on OpenAlexaff
Dustin Vickress, Darryl Metcalfe, David A. Rudge, Maciej Jastrzebski, Ian Cameron, Hyde Barry, Andrew Shaw, Ponomar Andriy

Bibliographic record

VenueABM Proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsRemedial educationComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The premature failure of copper staves in the lower stack and bosh is frequently the cause for an interim blast furnace repair at great cost to the blast furnace owner.Protective accretions may form on the stave hot face, but when these accretions are lost the staves can begin to deteriorate both due to abrasive wear from exposure to the descending burden materials and due to thermal cracking from exposure to hightemperature process excursions.These mechanisms can lead to failure of cooling passages, which forces blast furnace operators to shut off these channels, accelerating the rate of copper stave wear.Finger cooler technology was developed to extend blast furnace life by restoring cooling to damaged staves.Evidence for their effectiveness is presented herein in the form of experimental test work and conjugate heat transfer (CHT) analysis carried out using computational fluid dynamics (CFD) simulations.These tests demonstrate that under typical blast furnace excursion conditions, a stave fitted with finger coolers will undergo half the temperature rise of a stave fitted with conventional cigar coolers.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.202
Teacher spread0.195 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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