Viscous and filamentous bulking in activated sludge: Rheological and hydrodynamic modelling based on experimental data
Bibliographic record
Abstract
Although achieving good activated sludge settleability is a key requirement for meeting effluent quality criteria, wastewater treatment plants often face undesired floc structure changes. Filamentous bulking has widely been studied, however, viscous sludge formation much less investigated so far. Our main goal was to find relationship between sludge floc structure and related rheological properties, moreover, to estimate pressure loss in pipe networks through hydrodynamic modelling of the non-Newtonian flows in case of well settling (ideal-like), viscous and filamentous sludge. Severe viscous and filamentous kinds of bulking were generated separately in continuous-flow lab-scale systems initially seeded with the same reference (ideal-like) biomass and the entire evolution of viscous and filamentous bulking was monitored. The results suggested correlation between the rheological properties and the floc structure transformations, and showed the most appropriate fit for the Herschel-Bulkley model (vs. Power-law and Bingham). Validated computational fluid dynamics studies estimated the pipe pressure loss in a wide Reynolds number range for the initial well settling (reference) and the final viscous and filamentous sludge as well. A practical standard modelling protocol was developed for improving energy efficiency of sludge pumping in different floc structure scenarios.
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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) | 0.000 | 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) | 0.000 | 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".