MétaCan
Menu
Back to cohort

Analysis of the possibility of replacing carbon with hydrogen in the conditions of smelting cast iron from vanadium-containing titanomagnetites

2023· article· en· W4389143932 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFerrous Metallurgy Bulletin of Scientific Technical and Economic Information · 2023
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsBlast furnaceHydrogenCokeVanadiumMetallurgyMaterials scienceCarbon fibersSmeltingBlast furnace gasDirect reduced ironFerrous metallurgyWaste managementChemistryComposite material

Abstract

fetched live from OpenAlex

75 % of the total carbon dioxide emissions by ferrous metallurgy enterprises is generated in the blast furnace process. One of the directions of CO2 emission reduction in pig iron production is partial replacement of carbon monoxide with hydrogen as a reducing agent. It is shown that such a replacement can lead to a decrease in the total carbon consumption due to reduction of heat consumption for the direct reduction of iron oxides. Using a mathematical model of the blast furnace process, the efficiency of partial replacement of process fuel (coke, natural gas, pulverized coal) with a hydrogen additive was evaluated. Calculations were performed for the operating conditions of blast furnaces of EVRAZ NTMK JSC, which melt vanadium-containing titanomagnetites. The coefficients of process fuel replacement with hydrogen and the coefficients of the influence of the replacement of process fuel with hydrogen on the change in CO2 emissions are calculated. The dependence of the change in the productivity of the furnace on the consumption of hydrogen at a constant minute flow rate of the blast and its adjustment to maintain the pressure drop has been established. It is shown that in the absence of gas dynamics reserves, the replacement of process fuel with hydrogen will be accompanied by a decrease in furnace productivity. The smelting of pig iron from titanomagnetites with the replacement of technological fuel with hydrogen will complicate the refining of melting products due to an increase in the formation of titanium carbides and carbonitrides. The reduction in CO2 formation at hydrogen entering the blast furnace with natural gas is 0.35 kg/m3 compared to pure hydrogen replacement of 0.73 kg/m3, not taking into account CO2 emission during hydrogen production

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.194
Teacher spread0.186 · 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