Mathematical modeling of rare earth element separation in electrodialysis with adjacent anion exchange membranes and ethylenediaminetetraacetic acid as chelating agent
Bibliographic record
Abstract
Abstract This research delves into the effective use of electrodialysis for the separation of rare earth elements (REEs), specifically separating dysprosium (Dy) from praseodymium (Pr) and neodymium (Nd). A robust mathematical model based on the extended Nernst-Planck equation is introduced, simulating the process within a configuration that includes two adjacent anion exchange membranes. The model integrates aspects such as feed equilibrium, ion exchange within the membrane, and overall ion flux. Validation of the model's predictability was conducted through Chi-squared tests and root mean square error (RMSE) calculations, affirming its capability to accurately predict ion concentrations across different compartments. The study examines essential parameters such as applied voltage, rinse solution concentration, and feed concentration, assessing their impacts on separation performance and energy efficiency. Results indicate that higher voltages above 8 V, while speeding up separation, detrimentally impact energy use. It also highlights a critical balance in rinse solution concentration; lower concentrations below 0.05 mol/L enhance energy efficiency but may undercut separation efficacy due to early depletion. A linear correlation between the necessary rinse concentration and feed concentration was established, with higher feed concentrations demonstrating reduced specific energy consumption, thus enhancing overall efficiency. However, challenges remain in current efficiency due to the independent migration of SO42− ions in this specific setup. The findings advocate exploring alternative configurations, like alternating cation and anion exchange membranes, to optimize both environmental and economic aspects of REE separation. This study provides valuable insights and recommendations for refining electrodialysis systems in REE processing, contributing to sustainable and cost-effective electrodialysis systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".