Accelerating the Consolidation of Thickened Tailings Using Sand Co-Disposal
Bibliographic record
Abstract
Only grams or kilograms of valuable resources are extracted for each tonne of ore processed, generating immense amounts of tailings. Returning tailings-containment dams to their equivalent pre-disturbance landscape state is many stakeholders’ acceptable management strategy. The depositing of thickened fluid fine tailings (FFT), which the industry has relied upon to develop geotechnically stable dry landscapes, has not materialised due to the low hydraulic conductivity of such placements. The addition of sand to thickened FFT is proposed to accelerate the consolidation of thickened FFT in a co-disposal scheme, and this study evaluates its impact. In the continuum of solid wastes from mining operations, sand is the next-larger-sized grade of solids after “fine” that is produced in high proportions. Blends of highly-plastic FFT and fine-grade sand tailings at varying sand-to-fine ratio (SFR) values were tested. Sand lowered the liquid limit but did not significantly affect the plastic limit of FFT. The changes in liquid and plastic limits for SFR ≥ 1 blends were under 5% of water content. The maximum unit weight for the co-disposal mixes was obtained at SFR 3. Despite increases in unit weights, hydraulic conductivity increased with increasing SFR, while the trend for compression rates was in the reverse order. Void ratio—effective pressure—hydraulic conductivity power law relationships at varying SFR were obtained from the consolidation measurements. The constitutive constants of these relationships were used to model settlement of 30 m deep pits over a 25-year period. For all the deposits studied, the dewatering converged to a maximum solids content characteristic of each SFR in the order 74, 85, 88, and 94% (w/w) for thickened FFT and SFRs 1, 2, and 3, respectively. The numerically calculated average solid contents of the deep-pit placements grew closer with increasing SFR becoming 59, 77, 82, and 86% (w/w), respectively. Balancing the consolidation-rate gains and the land footprint required for storage with SFR increase, the optimal composition for co-disposal for this material and similar tailings is SFR 1.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".