Effects of anaerobic digestion on the dewaterability of sonicated biosludge
Bibliographic record
Abstract
Studies on the impact of anaerobic digestion on biosludge dewatering are conflicting and mechanisms are poorly understood, with significant implications to both wastewater costs and environmental impact. To identify the mechanisms that improve dewaterability and to amplify the impacts of anaerobic digestion, biosludge was first sonicated to deteriorate dewaterability. Sonicated biosludge was next anaerobically digested under mesophilic conditions in three experiments, and the roles of extracellular polymeric substances (EPS) and supracolloidal particles on dewaterability were explored. Dewaterability, as measured by capillary suction time, improved with anaerobic digestion from 84.7 ± 11.3 s-L/g to 31.6 ± 3.2 s-L/g. The loosely-bound fraction of EPS saw only a minor decrease from 6.4 % ± 1.1 %-1.2 % ± 0.3 % and total EPS protein did not change significantly. On the other hand, in a separate experiment, significant removal of supracolloidal particles under 10 μm was observed within 6 days of anaerobic digestion, coinciding with an improvement in a capillary suction time from 116.0 ± 6.7 s-L/g to 69.8 ± 1.1 s-L/g and Crown Press filtrate total solids from 13.7 ± 0.2 g/L to 8.4 ± 0.5 g/L. In addition, in a third experiment, raw biosludge as well as sonicated at five different intensities, saw significantly faster capillary suction time and decreased Crown Press filtrate total solids which coincided with a decrease in the volume % of particles under 16 μm. The results suggest the net removal of small supracolloidal particles is a mechanism that occurs during anaerobic digestion to improve biosludge dewaterability.
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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 | 0.001 |
| 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".