Impact of fiber properties on floc formation and turbidity removal
Bibliographic record
Abstract
• Fiber-based agents show stable suspended matter removal across various temperatures • Longer, thinner fibers outperform shorter, thicker ones in fiber-based treatment • Optimal treatment requires > ∼50% of fibers to be longer than ∼2000 µm The water treatment industry is interested in sustainable approaches to minimize chemical demand in the coagulation and flocculation process. In conventional physicochemical treatment, lower water temperatures act to slow down particle collisions, chemical reactions, floc formation, and floc settling rates. This study explored the use of fiber-based super-bridging agents to compensate for the effect of temperature on the coagulation-flocculation process. The efficacy of recycled cellulose fibers was evaluated at the lab scale (250 mL) at various temperatures, demonstrating high turbidity removal with both settling and screening as floc separation methods. For the fiber-based treatment, turbidity differences at temperatures near 5 °C, 10 °C, and 20 °C were minimal indicating that this technology is more effective than the conventional approach (coagulant and flocculant) which showed significant variations between temperatures. Furthermore, regardless of the fiber source and properties, different cellulose fibers were efficient in turbidity removal acting as a super-bridging agent. Additional experiments were conducted to understand how fiber length and diameter distributions influenced the performance of the fiber-based treatment. Fibers of length > 2000 µm and diameter < 100 µm were more efficient in reducing turbidity and translated to lower chemical demand (i.e., 20% reduction in alum demand for a target water quality of 20 NTU) in the coagulation and flocculation process. This sustainable fiber-based water treatment approach has the potential to lower the operational cost of water treatment plants operating at different water temperatures as a function of the season and geographical location, though techno-economic analysis is required to validate this hypothesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".