Modeling of transport phenomena in a hybrid forward osmosis-directional freeze crystallization process for clean water recovery from hydrometallurgical effluents
Bibliographic record
Abstract
Abstract Sustainable water recovery and reuse are critical yet challenging, especially from industrial effluents in cold regions. This work presents a robust numerical model of the transport phenomena in a hybrid two-step forward osmosis (FO)-directional freeze crystallization (DFC) desalination process, whose application in areas with cold climates is advantageous. Deionized (DI) water and a hydrometallurgical effluent were considered as the feed solution in the FO step, while three aqueous solutions of inorganic salts were considered as the draw solutions (DS): NaCl, CaCl2, and MgCl2. The effects of temperature and initial DS concentration were investigated on water flux, reverse solute flux, and specific water flux using computational fluid dynamics (CFD). Based on the simulation results, the highest water flux (18 L/m2/h for DI water and 5 L/m2/h for the hydrometallurgical effluent) and lowest reverse solute flux (consistently below 0.3 mol/m2/h) were obtained when MgCl2 was used as the DS. The effect of solute type in the DS on both water recovery yield and purity was in turn studied in the subsequent DFC step, allowing to visualize the solute distribution during the freezing process.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".