Shear‐Dependent Yield Stress of Iron Ore Fine Tailings in Two‐Step Flocculation Process
Bibliographic record
Abstract
Both shear and flocculation have a significant influence on the rheological behavior of tailings, especially the yield stress. In the two‐step flocculation process, the above two kinds of actions exist at the same time, and they influence each other. In order to explore the change rule of the yield stress and its internal causes, a two‐step flocculation process of the iron ore fine tailings with different shear conditions in the four different phases was designed. In the primary flocculation phase, tailing particles combined with the primary flocculant and formed a primary floc network structure. In the primary broken phase, shear destroyed the primary floc network structure and decreased the average floc size, so the shear‐dependent yield stress, the floc strength factor, and the fractal dimension decreased. In the secondary flocculation phase, broken floc combined with the secondary flocculant and produced a more compact floc network structure which had a better shear resistance. Therefore, in the secondary broken phase, with the increase of shear, the decrease of yield stress, the floc strength factor, and fractal dimension were less obvious than that in the primary broken phase. In both two broken phases, the yield stress of the secondary flocculating slurry was always higher than that of the primary flocculating slurry, but with the increase of shear, the difference became smaller. The floc strength and fractal dimension also showed the same rule. The internal reasons for the stronger shear resistance of the secondary flocculating slurry were the increase of the number of binding sites, the electric neutralization between the two flocculants, and the steric hindrance effect of the flocculants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".