Fiber-based super-bridging agents improve flotation and settling during water treatment
Bibliographic record
Abstract
Increasing demand for water poses a major challenge to the water treatment industry. Consequently, water treatment plants are expected to require larger separation units and use more chemicals. Physicochemical treatments produce flocs that are limited in size, which limits floc removal efficiency during gravitational separation. Introducing fiber-based super-bridging agents has improved the floc size, which is 10–100 times larger than conventional flocs. Such improvements could lead to important gains in floc separation and ultimately increase the capacity of water treatment plants. This study analyzed the behavior and interaction of fibers under various coagulation/flocculation conditions to improve flotation and settling. The results also showed that fibers can allow a ~ 50 % reduction in coagulant. The benefits of fibers on floc size were particularly pronounced during suboptimal coagulation, as the injection of fibers compensated for poor coagulation conditions. Air bubbles during air flotation were also better incorporated into the larger and more porous floc structure obtained with fibers, which drastically improved floc removal during flotation. The best turbidity removal after flotation was obtained when combining 2 mg of cationic polyacrylamide and 200 mg of softwood fibers/L. Fiber-based super-bridging agents can be introduced in existing water treatment plants to increase plant capacity, reduce the demand for coagulants/flocculants, and improve contaminant removal.
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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".