Influence Mechanisms of Additives on Coal-based Reduction of Complex Refractory Iron Ore
Bibliographic record
Abstract
Additives play an important role in the recycling of complex refractory iron ore by coal-based reduction to improve the self-sufficiency rate of the ore. In this work, the influence mechanisms of CaF2, Na2CO3, and MgCO3 additives on the coal-based reduction of complex refractory iron ore were studied by x-ray diffraction, thermogravimetry and differential scanning calorimetry, scanning electron microscopy, and energy dispersive spectrometry. The results indicate the three additives could greatly improve the quality of reduction and magnetic separation products. A reduction product metallization degree 93.42%, iron grade 91.23%, and iron recovery 95.48% were obtained under conditions of CaF2 dosage 6%, reduction temperature 1225 °C, and reduction time 50 min. These results were 3.38%, 8.09%, and 0.39% higher, respectively, than could be obtained without additives. The CaF2, and the Na2O and MgO produced by the decomposition of Na2CO3 and MgCO3, could not only replace the FeO from (Mg,Fe)SiO4 and FeAl2O4, but also react with SiO2 and Al2O3 in ore to form their corresponding complex compounds. Moreover, all compounds were benefitted by the increase of the FeO content. The F− ion could replace the O2- ion in the silicon oxygen tetrahedron to reduce the viscosity and surface tension of reduced ore, thus reducing the resistance of iron particle aggregation and growth. In addition, the formation of a local micro melting phase under the action of additives could promote the diffusion of crystalline particles, which is more conducive to the reduction of iron minerals and the formation and growth of iron particles.
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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 | 0.000 | 0.000 |
| 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".