Recovery of Valuable Metals from Polymetallic Refractory Concentrate by a Sulfuric Acid Curing and Leaching Method
Bibliographic record
Abstract
Sulfuric acid curing and leaching is a promising technology for treating refractory ores. In this work, a refractory concentrate containing 3191 ppm uranium (U), 2135 ppm niobium (Nb), and 0.7% rare earth minerals (REMs) went through two stages: curing by high-concentration H2SO4 and leaching by low-concentration H2SO4. We investigated the behavior of those valuable metals during the two stages. For both curing and leaching, the operating parameters include the acid-to-solid ratio, time, temperature, and H2SO4 concentration. The recovery for U, Nb, and REMs was as high as 95%, 86%, and 73.5% using a curing acid-to-solid ratio of 1:1, curing temperature of 200 °C, curing time of 1 h, H2SO4 concentration of 98%, leaching liquid-to-solid ratio of 4:1, leaching time of 2 h, leaching temperature of 60 °C, and leaching H2SO4 concentration of 5 g/L. A “sulfuric acid curing–leaching-U extraction by N235–Nb recovery by resin adsorption–REMs’ recovery by resin adsorption” method was implemented, where the overall U, Nb, and REMs’ recovery reached 93.1%, 84.5%, and 69.6%, respectively.
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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".