Forward Osmosis for Metal Processing Effluents under Similar Osmotic Pressure Gradients
Bibliographic record
Abstract
Water recovery from aqueous effluents in the mining and metals processing industry poses a unique challenge due to the high concentration of dissolved salts typically requiring energy intensive methods of treatment. Forward osmosis (FO) is a lower energy technology which employs a draw solution to osmotically extract water through a semi-permeable membrane further concentrating any feed. Successful FO operation relies on using a draw solution of higher osmotic pressure than the feed to extract water while minimizing concentration polarization to maximize the water flux. Previous studies employing FO on industrial feed samples commonly used concentration instead of osmotic pressures for feed and draw characterization; this led to misleading conclusions on the impact of design variables on water flux performance. By employing a factorial design of experiments methodology, this study examined the independent and interactive effects on water flux by: osmotic pressure gradient, crossflow velocity, draw salt type, and membrane orientation. With a commercial FO membrane, this work tested a solvent extraction raffinate and a mine water effluent sample to demonstrate application significance. By optimizing with osmotic gradient independent variables, water flux can be improved by over 30% without increasing energy costs or compromising the 95-99% salt rejection of the membrane.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".