Optimizing the recovery of rare earth elements from acid mine water: A sustainable approach using selective precipitation
Why this work is in the frame
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Bibliographic record
Abstract
This study focuses on the recovery of rare earth elements (REEs) from acid mine water (AMW) through a two-step selective process, which consists of a selective extraction with ion exchange followed by a precipitation stage using oxalic acid. Optimization of the effective REE recovery from sulphuric ion-exchange concentrates results in sustainable AMW management, providing a secondary resource for critical metals towards green transition. Experimental results indicate that (1) the use of oxalic acid facilitates the formation of REE-oxalate crystals, yielding recovery efficiencies in light rare earth elements (LREEs) much higher than for heavy rare earth elements (HREEs) at specific excess doses, and that (2) LREEs act as precursors for HREE precipitation. Moreover, REE-oxalate crystallization depends on the oxalic acid dose, pH, and precipitation time (PT). The longer the PT, the larger the crystals, which are economically advantageous. The study highlights that AMW is a potential secondary source for the REE recovery, which contributes to sustainable mining practices and provides confidence for further optimization of REE recovery processes. • Oxalic acid used for selective REE precipitation from AMW. • Study supports sustainable practices in REE recovery. • Experimental results indicate a potential industrial-scale REE recovery. • Optimal REE recovery: 300 rpm stirring, 0.15–0.275 M H 2 SO 4 for high rates and selectivity. • LREEs precipitated more efficiently than HREEs under specific conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it