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EVOLUTION OF SCHEMES OF HEAT EXCHANGE IN A BLAST FURNACE

2016· article· sh· W2595410443 on OpenAlexaff
Yu. G. Yaroshenko, Н. А. Спирин, В. С. Швыдкий, Y. Gordon, В. В. Лавров

Bibliographic record

VenueIzvestiya Ferrous Metallurgy · 2016
Typearticle
Languagesh
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsBlast furnaceSmeltingCokeMetallurgyThermalHeat exchangerWork (physics)Materials scienceEnvironmental scienceWaste managementEngineeringMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

The development of schemes of heat exchange in a blast furnace was considered with the improvement of the blast furnace smelting technology. It was noted that for the estimation of the thermal state of blast furnace as a control object it is expedient to divide it into two thermal zones, upper and bottom. The interface between them is in the top of the mixed recovery between the level of the beginning of the carbon gasifi cation of coke and the horizon, below which iron oxides are directly reduced. Upper slow heat exchange section in terms of heat exchange is reserve height providing better thermal and regenerative operation of the furnace. The bottom section of slow heat exchange should not be used as a reserve for improvement of thermal and reduction work of the blast furnace. It is shown that the presence of two zones of intense heat exchange in the present conditions of smelting of various types of iron ore using the combined blowing of high parameters is a prerequisite for the stability of the course of the blast furnace process and effi ciency of smelting.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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 designNot applicable
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

Citations6
Published2016
Admission routes1
Has abstractyes

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Same venueIzvestiya Ferrous MetallurgySame topicIron and Steelmaking ProcessesFrench-language works237,207