A cost-effective tailings solution to rheology issues while meeting the environmental constraints using inexpensive additives
Bibliographic record
Abstract
A significant quantity of tailings is produced in the wet beneficiation of iron ore. These tailings are flocculated and thickened in a thickener before being pumped to a tailings storage facility TSF, i.e., a pond, which can be several km away. Occasionally, the thickened tailings acquired a higher yield stress than can be pumped. This material then becomes a bottleneck reducing the plant output. This study demonstrates how to reduce these problematic tailings’ yield stress or viscosity with a cheap additive. NaOH costing ~ US 400 per tonne resulted in a 50-60% reduction in the yield stress at pH 10 and ~ 90% reduction in the viscosity at 100s-1. The legislated environmental constraints are i) the pH of the disposed of tailings must be less than 10 and ii) any chemical leachate of heavy metal ions must be less than stock drinking water guidelines or water table water. Leaching results showed this was the case for all leachate chemicals evaluated up to pH 11.2. Some leachate chemicals evaluated were As, B, Cr, Cu, Mo, Pb, Se, Th, Ti, U and V. However, not all tailings (from different mines) respond to NaOH treatment.
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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 | 0.001 |
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".