Staged magnetic concentration of complex rare earth ores containing niobium and zirconium
Bibliographic record
Abstract
The Balzhe rare earth deposit, located in Inner Mongolia, China, is a complex deposit that also contains niobium and zirconium. However, it remains undeveloped. One notable challenge is the low grades of the main valuable minerals, including xinganite, zircon, niobite, ilmenorutile, pyrochlorite, eschynite, monazite, and bastnaesite. These minerals are either weakly magnetic or associated with ilmenite, a paramagnetic mineral. To overcome this, a high-intensity magnetic method can be employed to separate these valuable minerals from the gangue minerals, thereby generating enriched products to lower the cost of the following flotation. In this study, the first step involved utilizing a high-intensity magnetic separation to enrich particles in different size fractions. It was observed that the size fraction of −0.5 + 0.02 mm was particularly suitable for effective separation. Subsequently, staged magnetic pre-concentration was conducted to obtain products with varying specific magnetization coefficients. To optimize the applied magnetic field intensity, a combination of a high-intensity magnetic separator and a superconducting magnetic separator was employed at different magnetic induction intensities. This proposed technique not only reduces reagent costs but also enhances the operational efficiency of subsequent flotation processes. By implementing this approach, it is anticipated that the Balzhe rare earth deposit can be effectively processed, overcoming the challenge of low-grade valuable minerals. This would contribute to improved economic viability and operational efficiency in the extraction of rare earth elements, niobium, and zirconium from the deposit.
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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) | 0.001 | 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".