Rare-earth metal ore processing technologies when developing new deposits
Bibliographic record
Abstract
The article provides a brief overview of processing technologies for rare-earth raw materials used under greenfield development projects in different countries of the world (Africa, Greenland, Australia, Canada). The projects feature deposits with different mineral compositions, mass fractions of rare-earth metals (REM) in ores of 0.2 to 15 %, and the presence of niobium, zirconium, tantalum, phosphorus, uranium, and thorium. The resulting production facilities will extract 180 kt to 7.2 Mt rare-earth ore annually to generate 1.5 to 20 kt oxides of heavy and light groups of rare-earth metals along with the rare metals. The analysis of technologies for the projects considered demonstrates that magnetic and radiometric separation, dense-medium concentration and flotation with hydrometallurgical processing in the form of leaching with sulfuric or hydrochloric acid, followed by extraction of the target products, will be used for the processing of rare-earth raw materials. A characteristic feature of a number of projects is, first of all, the direct hydrometallurgical processing of the feed. The concentration technologies for ores containing rare-earth metals also indicate a clear trend towards a more active use of high-intensity magnetic separation. The main products to be obtained with these technologies will include composite concentrates of oxides or carbonates of rare-earth metals. At the same time, the commissioning dates for the projects are being repeatedly postponed; the implementation of many projects remains uncertain, which is largely due to the stagnant dynamics of global prices for rare-earth metals.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".