Analysis of Different Types of Carbonaceous Reductants and Methods of Slag and Sludge Recovery at Converter Production
Bibliographic record
Abstract
Recycling of metal-containing wastes with an iron content of more than 15%, such as slag and sludge from the gas cleaning of converter production, can be used to recover the metal. In order to study the reducing properties of coal sludge, coke breeze (coke), Shubarkol coal, a series of experimental melting of coal sludge briquettes with different contents of the listed carbon-containing materials was carried out. Sludge-coal briquettes were made from converter slag and sludge using hydrated lime as a binder. To determine the necessary parameters for the reduction of the iron-containing element, chemical and technical analyzes were carried out for ash content, moisture content and volatile matter. The choice of the optimal component composition of briquetted mixtures has established that the proportion of the reducing agent in the briquette should not exceed 20...25%, in order to avoid excess carbon, which binds into solid carbide compounds. The share of the reducing agent in the briquette was 15...18%. Chemical analysis of the resulting alloy and slag component pointed out the expediency of using Shubarkol coal as a carbonaceous reducing agent with high reactivity and electrical resistivity
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".