Coagulation optimization using ferric and aluminum salts for treating high algae and high alkalinity source water in a typical North-China plant
Bibliographic record
Abstract
Coagulation optimization using coagulants of ferric chloride (FeCl3), polyaluminum chloride (PACl), and their combinations (FeCl3/PACl) were evaluated through jar tests, by treating source water with high algal content (10–40 million cells/L) and high alkalinity (80–110 mg/L). The results indicated that when compared to single coagulants, the combined coagulants showed a superior coagulation performance in terms of turbidity, UV254, and algal removal. The optimal dosage was determined as 30–35 mg/L by using the combined PACl/FeCl3 (1:2 by mass) and dosing PACl followed by FeCl3. By adding the coagulant aids of polymerized diallyl dimethyl ammonium chloride (HCA) and polyacrylamide (FO4190), the floc sizes may enlarge up to 1.75–2.0 mm. Scanning electron micrographs showed that the coagulant combination can form a more compact reticular aluminum-ferric structure, and thus increased the settleability of the flocs. The combined coagulation was further evaluated in full-scale water treatment plants, confirming the improvement of the removal of algae, turbidity, and residual iron in the treated water.
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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 | 0.001 | 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 | 0.000 |
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".