Preparation of Mine Filling Material from Steel Slag Mud
Bibliographic record
Abstract
With the proliferation of wet milling and magnetic separation, a huge amount of steel slag mud has been discharged, waiting to be recycled.To recycle steel slag mud on a largescale, a possible solution is to prepare steel slag and other industrial solid wastes into mine filling materials.In this paper, the steel slag mud is activated through mechanochemical activation, and the mechanical activation laws are explored through X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FT-IR).The results show that mechanochemical activation can partially excite the activity of steel slag mud, and that the fineness of steel slag mud is positively correlated with the mechanical performance of the filling material.In addition, the low field nuclear magnetic resonance (NMR) was adopted to analyze how the fineness of steel slag mud affects the porosity distribution of the filling material, and disclose the relationship between fineness and the material strength.The analysis shows that the mine filling materials prepared from steel slag mud, slag and flue gas desulfurization gypsum (FGDG) fully satisfy the required working performance.The research findings lay a theoretical basis for recycling steel slag mud, solves the environmental pollution of this type of industrial solid waste and reduces the filling cost of mine enterprises.
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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".