Deposition, consolidation, and strength of a non-plastic tailings paste for surface disposal
Bibliographic record
Abstract
Various thickened tailings schemes have been implemented as environmentally attractive alternatives to conventional tailings slurry deposition in surface impoundments. The first site to dispose of a non-plastic tailings dewatered to a paste, which is pumped and deposited out the end of a pipe to form a stack, is at Bulyanhulu, Tanzania. Since design precedents did riot exist for such a facility, a field study was undertaken during start up to establish the framework for follow up laboratory investigations. Key areas were identified for research including deposition characteristics, consolidation behaviour, and strength. The paste behaviour was characterized through correlated results from laboratory experiments using a "50Ø Rheometer", a modified geotechnical laboratory vane, and flume tests. Test results of the Bulyanhulu paste identified a clear linear trend between the angle of repose and the vane shear strength. The use of these characterization tools helped to quantify the effects of changing the flocculating agents in, and temperature of, the paste. Traditional consolidation test results from three different tailings pastes were normalized onto one distinct curve using the void index concept. The normalized results for the non-plastic fine grained materials are different from those reported for clays. A normalized plot could reduce the amount of preliminary laboratory testing by providing better initial estimates for in situ void ratios and settlement for a given paste project. Dynamic compaction of paste, which is one method of strength gain in the field, was successfully modelled and monitored in the laboratory. Future investigations could examine the field scale implications of these findings. A standardized sample preparation procedure has been developed, with a high success rate, for both static and cyclic triaxial testing of non-plastic tailings pastes. Static and cyclic triaxial testing parameters have been established for the Bulyanhulu tailings paste, which are consistent with published data. The results are believed to be the first reported for non-plastic tailings paste in the context of a surface stack. The tests and procedures outlined have established the framework and starting points for further research, some of which has begun within the Lassonde Institute at the University of Toronto.
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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 | 0.001 | 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) | 0.001 | 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".