The effect of composite additives on the rheology of concentrated iron ore tailings and their components
Bibliographic record
Abstract
The major components of iron ore tailings produced in the Pilbara region are hematite, goethite and kaolinite. At times, these tailings have developed a viscosity or yield stress too high for the pump to handle. This rheological problem urgentlyrequires a cost-effective and simple solution. To address this issue, this study evaluates the yield stress-solids concentration relationship of iron ore tailings, ochreous goethite sourced from a Pilbara mine, and kaolin suspensions with and without the composite additive NaOH-Na2SiO3-Na polyphosphate. Our results reveal that the yield stress-concentration curve shifts to a higher concentration for all three materials when the additive is above a critical level. At 0.5 dwb% (g/100g solids) of the composite additive, the yield stress was close to zero at 65 wt% solids for all three suspensions. This indicates that iron ore tailings can be transported at a concentration in excess of 65 wt% solids by using the composite additive. The cost required to process tailings of 55 to 65% solids was between USD 2 to USD 4 per ton of solids, although the additive dosage’s optimisation was outside this study’s purview. The tailing viscosity and yield stress can be converted back to paste consistency with a neutralising additive for safer storage in the dam or as a feedstock for dry stacking, i.e., drying, harvesting and stacking.
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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 | 0.001 |
| 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 | 0.001 |
| 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".