Flotation of lithium ores to obtain high-grade Li<sub>2</sub>O concentrates. Are there any mineralogical imitations?
Bibliographic record
Abstract
The current lithium demand for batteries in general and namely for the electrical vehicle, awakened the attention for mineral processing of lithium ores. The largest lithium reserves are in brines from western South America and in pegmatites. Throughout Europe it is possible to identify several lithium deposits, namely in granitic pegmatites. An efficient mineral processing approach could be the key for an economically viable mining project. This work addresses a mineral processing study by froth flotation of samples collected in two European lithium ore pegmatites deposits -Lntt (Finland) and Gonalo (Portugal) and aims at paying attention to some mineralogical features that can decrease the mineral processing efficiency and consequently the upgrading of the Li2O concentrates. In the case of Lntt, spodumene is the main lithium mineral and a grade of 5.20 % Li2O is the maximum obtained in the concentrates, whilst lepidolite is the lithium-bearing mineral in Gonalo and that can be concentrated by froth flotation up to 4.50 % Li2O. Taking into consideration the Li2O content of both Lntt spodumene and Gonalo lepidolite, respectively 7.0 and 5.58 % Li2O, higher concentrate grades would be expected. In both studied cases, very fine quartz and albite inclusions locked in lithium silicates were identified justifying the existence of a limitation for the processing technology. The mineral processing of the two pegmatites revealed the difficulty of producing Li2O close to the stoichiometry of the spodumene and lepidolite in either of these two ores.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".