Experimental Research of Effect of Iron Ore Powders Ratio on Ferro-Coke Quality
Bibliographic record
Abstract
High reactivity ferro-coke is a novel blast furnace burden material which could improve blast furnace efficiency and reduce CO2emissions greatly,and reaches the goal of reasonable utilization of resources of low-grade coal and iron ore.Through a series of coke-making experiments,the relationship between the ratio of iron ore powders and the ferro-coke properties such as ash content,sulfur content,relative density,mechanical strength and thermal property were studied under condition of various iron ore powders and blend ratios.The results show that,with increasing of ratios of iron ore powders,for the ferro-coke,sulfur content increases slightly,ash content increases linearly,both true and apparent relative densities increase,both total and apparent porosity decrease,both crushing strength and abrasive strength decrease,reactivity increases and strength after reaction decreases,metallic iron content increases and reduction degree of iron oxides increases for the Canadian and E-xi iron ore powders and reduction degree of iron oxides decreases for the Australian iron ore powder.On the ratio of iron ore powder less than 15%, the mass percent of metallic iron is 6%-9%,and reduction degree of iron oxides is 55%-70%for the ferro-coke from Canadian and E-xi iron ore powder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".