Process and model of a chemically buffered supported liquid membrane system for cobalt extraction
Bibliographic record
Abstract
Abstract BACKGROUND Cobalt is a valuable metal whose total annual supply from recycling is projected to be 34 000 t by 2030, primarily from batteries. Supported liquid membrane (SLM) technology is emerging as a promising technology alternative to conventional hydrometallurgy for cobalt recovery. RESULTS This work featured the development of a novel physics‐based computational fluid dynamics simulation for cobalt extraction from acetate‐buffered synthetic Co–Ni solution in a hollow‐fiber SLM system. The system involved cobalt and nickel, both at 0.167 mol L −1 initial concentration, sodium acetate buffer at 0.5 mol L −1 and pH adjusted to 5.85 with KOH or H 2 SO 4 . The process module had 57 m 2 active surface area with a single fiber modeled as a cylindrical tube of 200 μm in diameter and 0.85 m in length in the Poiseuille flow regime with a total flow rate of 5.5 L min −1 . At the fiber wall, an acidic organophosphorus SLM was present, where ion exchange between Co 2+ and H + occurred. Using experimental cobalt mass transfer rates dependent on Co 2+ concentration and the acetate p K a , simulation results within 5% of operational data were obtained for outlet Co 2+ concentration and pH. Parametric effects of feed flow rate and buffer concentration were explored to enhance system design and performance. CONCLUSIONS The simulation was validated with good fidelity against measurements from an industrial‐scale module. The results show near‐optimal cobalt recovery possible at the test flow rate, even with sodium acetate buffer at 100 mol m −3 , about 20% of the trial‐run level. The model is sufficiently general and fundamental, thus readily adaptable to other SLM systems. © 2025 His Majesty the King in Right of Canada. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI). Reproduced with the permission of the Minister of Innovation, Science, and Economic Development.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".