An experimental study on the flocculating settling of unclassified tailings
Bibliographic record
Abstract
Unclassified tailings are the main backfilling aggregates in mines and their settling is the first step in the utilization of tailings; thus, it is very important to determine their settling behavior. The aim of this study was to understand the flocculating settling behavior of unclassified tailings with different factors. The combination of property detection, laboratory experiments and industrial tests were used to assess the flocculating settling behavior of unclassified tailings; the orthogonal experimental design and the control variate method were used for an experimental design. The results show that the flocculating settling velocity of unclassified tailings decreases with the increase of slurry concentration and that this settling velocity increases first and then decreases with the increase of flocculant unit consumption. The underflow concentration is positively correlated with the slurry concentration and negatively correlated with the flocculant unit consumption and flocculant concentration. Slower feed velocity could produce higher concentration underflow but lower clarity overflow water. The greater the mud height, the higher the underflow concentration and the suspended solids concentration in the overflow water. The underflow concentration has a maximum at the rake speed of 0.3 r/min, and the rake speed has little effect on the suspended solids concentration in the overflow water. By analyzing the settling velocity, the underflow concentration, the suspended solids concentration in the overflow water and the solid flux, the following parameters of the flocculating settling experiments were determined: the flocculant type is APAM with a molecular weight of 12 million, the flocculant unit consumption is 30 g/t, the slurry concentration is 6 vol.%, the flocculant concentration is 0.1 wt.%, the rake speed is 0.3 r/min, and the feed velocity is 0.4 L/min (its solid flux is 0.523 t/(m2·h)). The industrial tests were carried out based on the laboratory settling data, and the appropriate selection parameters of the industrial tests were estimated.
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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 | 0.000 |
| 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".