Dewatering of mine tailings using electrokinetic geosynthetics
Bibliographic record
Abstract
Many mining operations produce tailings that dewater very slowly under self-weight consolidation. One way of reducing the water content of such tailings is by electroosmotic dewatering. Although the technique has been used with some success in civil engineering applications, it is still largely seen as a solution of last resort. This is probably due to the high energy costs reported in the literature, as well as problems of very rapid corrosion of metal electrodes. This paper describes a study using newly developed electrokinetic geosynthetics (EKGs) as electrodes for the in situ dewatering of mine tailings. Laboratory tests were undertaken on mineral sands tailings in both a purpose-built testing cell and a laboratory testing tank using EKGs, followed by an outdoor experiment in a tank containing approximately 9 m3of the tailings. This test was run for over 2 months. Energy consumption in the outdoor test was less than 1 kWh per dry tonne of material dewatered and there was no sign of electrode deterioration even after 2 months of usage. The results point to a potentially powerful technique for reducing the water content of tailings ponds in situ, thus increasing storage space, improving stability, and facilitating closure of these facilities.Key words: tailings, dewatering, electroosmosis, electrokinetic geotextiles, consolidation.
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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 | 0.000 | 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 | 0.000 |
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".