The Recovery and Concentration of Spodumene Using Dense Media Separation
Bibliographic record
Abstract
In coming years, global lithium production is expected to increase as the result of widespread electric vehicle adoption. To meet the expected increase in demand, lithium must be sourced from both brine and hard-rock deposits. Heavy liquid separation (HLS) and dense media separation (DMS) tests were conducted on the pegmatites from Hidden Lake, NWT, Canada to demonstrate the potential role of this technology in the concentration of spodumene (LiAlSi2O6) from hard-rock sources. A continuously operated DMS circuit test, conducted on +840 µm material, produced a concentrate grading 6.11% Li2O with ~50% lithium recovery. The circuit rejected 50% of the original mass to tailings, with only 8% lithium losses. Sensitivity analysis showed that minor changes (+/−0.05) in the DMS-specific gravity cut point resulted in significant changes to the mass rejected and to the concentrate grade produced; this may limit the feasibility and operability of the downstream grinding and flotation circuits. The results demonstrate the potential for DMS in the concentration of spodumene from the Hidden Lake pegmatites, and by extension, the potential for DMS in the concentration of spodumene from other hard-rock occurrences.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".