Tailings filtration: integrating process design with geotechnical outcomes
Bibliographic record
Abstract
With the number of high-profile tailings storage facility incidents over recent years, filtration and dry stacking has received unprecedented attention as a viable option for the management of tailings. Due to regulatory pressures, both new and existing operations are at the very least evaluating or re-evaluating the cost-benefit balance offered by tailings filtration. Consequently, there has been a significant increase in the amount of evaluative test and design programmes in the area. However, the quality of this work may not always meet a level of technical rigour commensurate with the higher cost and associated risk of the filtration approach. One key risk in the filtered tailings design process lies in the interface between the tailings processing and materials handling/geotechnical disciplines, which typically operate in isolation of each other. This risk is in part due to different terminologies and technical bases used for describing similar properties, but more so is due to this divide preventing development of a holistic picture of the behaviour of the material (e.g. dewatering rates, achievable versus target moistures, rheological behaviour, material variability) across thickening, pumping, filtration, cake handling and ultimate placement. If available as early as possible in the design process, this information can save money and reduce risk by allowing well-informed decisions around design criteria, equipment choices (type, size, dewatering pressure, operational targets) and ongoing detailed test programmes. Using indicative data (Compressibility/permeability, rheology, filter sizing, Atterberg limits, unconfined compressive strength, flow moisture point) this paper illustrates how a testing and evaluative approach integrating both process and geotechnical considerations can assist in clearly and quickly identifying critical flow sheet design information, operational windows, design and operational risks and highlight avenues for optimisation. Importantly much of this information can be obtained with relatively small sample sizes, early in the evaluation process and at lower cost. The strong interlinkage between material properties is also discussed. Whilst the focus of this paper is on filtration, this approach is equally valuable across all methods of tailings dewatering and disposal, and in particular assists with the early identification of the most suitable approach for an operation.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".