Freeze Concentration to Regenerate Concentrated Draw Solution (CDS) and Recover Water from Industrial Effluents
Bibliographic record
Abstract
This work focuses on industrial water recovery using freeze concentration (FC) as a stand-alone water recovery process (part 1), and investigates the possibility of an integrated forward osmosis-freeze concentration (FO-FC) process (part 2). In part 1, A 3-L bench-scale layer freeze crystallizer was designed, and 0.5-1.5 m NaCl and MgCl2 solutions were used to mimic effluents from the mining and extractive metallurgy industries. A maximum impurity reduction of 82 % and 68 % can be achieved from 0.5 m NaCl and MgCl2 solutions, respectively. Agitation at 50 rpm significantly improved ice purity even at a high concentration of 1.5 m MgCl2. In part 2, the results showed that the regeneration of the 2-m MgCl2 concentrated draw solution can be achieved by adjusting the jacket temperature. The multi-step operation further improved ice purity by 14.6 % without sacrificing the CDS regeneration objectives.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".