Improvements on activated sludge settling and flocculation using biomass-based fly ash as activator
Bibliographic record
Abstract
Abstract Biomass-based fly ash and wastewater are undesired products of the pulping industry. Recently, the use of biomass-based fly ash as an adsorbent (i.e., a valued material) for constituents of wastewater effluents was reported. In this work, the settling performance and properties of activated sludge were studied in the presence of fly ash. Upon mixing, fly ash increased the zeta potential of the sludge from −31 mV to −28 mV, which was due to the release of cationic ions from fly ash in the sludge suspension. The sludge settling and its flocculation affinity were improved through the complexation of flocs and released cation ions from fly ash. The relationships between the protein/polysaccharide (PN/PS) ratio and the content of extracellular polymeric substances (EPS) as well as the ratio and the properties of the sludge flocs were determined. A correlation between the total loosely bound-EPS (LB-EPS) content and the effluent suspended solids (ESS) (Pearson’s coefficient, rp = 0.83) was observed. The performance of sludge flocculation and settling were much more closely correlated with LB-EPS than with tightly bound EPS (TB-EPS). Scanning electron microscopy (SEM) analysis of sludge flocs before and after EPS extraction showed that the sludge flocs contained a large number of microorganisms, mainly Bacillus and Cocci. The amount of LB-EPS had an adverse influence on bioflocculation, effluent clarification and sludge settling affinity. The sludge properties had a moderate relationship with the PN/PS ratio of LB-EPS. Also, no correlation could be established between the ratio and the TB-EPS content.
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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) | 0.001 | 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 | 0.000 |
| 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".