Optimizing the recovery of rare earth elements from acid mine water: A sustainable approach using selective precipitation
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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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".