Investigations on Strategic Element Recovery by an Underground Membrane Pilot Plant from In-Situ Extracted Bioleaching Solutions
Bibliographic record
Abstract
Focusing on the selective extraction of the critical raw materials indium and germanium from real bioleaching solutions, extended studies have been carried out using Europe’s first underground hybrid membrane pilot plant (TRL6). In order to transfer former laboratory experiments to pilot scale, NF99 (Alfa Laval) was used for the evaluation of membrane permeance and ion retention. A performance test of microfiltration (MF) and nanofiltration (NF) showed high permeances with low root-mean-square deviation under feed variation (5.2% for MF, 4.7% for NF). Depending on the feed load, a significant permeance drop of up to 57% for MF (3 bar) and 26% for NF (10 bar, 1.1 m s−1) was observed. The NF retention performance showed that, without regular chemical cleaning, the selectivity between the target elements degraded. By introducing acidic-basic cleaning steps, it was possible to keep the retention behavior at an approximately constant level (In 91.0 ± 1.3%; Ge 18.2 ± 5.5%). In relation to the specified target, the best results could be achieved at low pressure (7.5 bar) and a maximum overflow velocity of 1.1 m s−1, with a retention of 88.4% for indium and 8.8% for germanium. Moreover, the investigations proved the functionality and long-term stability of the underground membrane device.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".