Iron, aluminum, and thorium impurity removal from a rare earth element pregnant leach solution using magnesium carbonate
Bibliographic record
Abstract
This study investigates the selective precipitation of aluminum, iron, and thorium from a rare earth element (REE)-containing pregnant leach solution (PLS) using magnesium carbonate (MgCO3) as a precipitant. The goal is to efficiently remove impurities while minimizing valuable REE losses. A combination of experimental methods and aqueous thermodynamic modeling (OLI software) was used to understand the precipitation behavior of these elements under varying pH, temperature, and hydrogen peroxide (H2O2) conditions. Kinetic experiments confirmed equilibrium is reached within 30 min. A central composite design (CCD) and response surface methodology (RSM) revealed that iron is nearly completely removed at pH 3.5, with thorium and aluminum precipitation occurring at higher pH values. Optimal conditions, 81 °C, pH 3.6, and 0.52 mL H2O2, enabled complete removal of iron, ~ 95% removal of thorium, and ~ 65% of aluminum, with TREE losses under 3%. Solid precipitates were characterized via X-ray diffraction, Raman spectroscopy, inductively coupled plasma mass spectroscopy, and scanning electron microscopy energy dispersive spectroscopy, identifying ferrihydrite, aluminum sulfate, and magnesium carbonate phases. Thermodynamic models supported experimental findings, qualitatively predicting solubility trends. A technoeconomic analysis for a 1000 m3/day PLS treatment plant in Ontario, Canada, estimated monthly operational costs at ~$2.65 million and capital costs at ~$7.85 million. This work advances impurity removal strategies in REE processing, offering scalable, cost-effective, and environmentally responsible solutions for enhancing REE recovery efficiency.
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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) | 0.000 | 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 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".