Ultraviolet disinfection of sequencing batch reactor effluent: A study of physicochemical properties of microbial floc and disinfection performance
Bibliographic record
Abstract
The influence of physicochemical parameters of microbial floc on the ultraviolet disinfection of effluent from sequencing batch reactors (SBRs) was studied. Sludge retention time (SRT) and synthetic feed were adjusted in the SBRs to vary the physiochemical properties of microbial floc. Escherichia coli was added daily as an indicator organism for assaying UV disinfection. The composition and concentration of extracellular polymeric substances (EPS) did not change with SRT, but changed with the carbon source that the SBRs were fed. The estimated UV absorbance at a wavelength of 253.7 nm due to EPS was approximately 250 for a solid layer of floc compressed into 1 cm, which is insignificant compared to the total UV absorbance of microbial floc; in comparison to the cellular constituents of microbial floc, EPS does not appear to be a limiting factor in the UV disinfection performance of microbial floc. The total number of enumerable E. coli in the effluent from an SBR showed a negative correlation with SRT and hydrophobicity of microbial floc. Effluent with a high SRT may have increased the ability of E. coli to floc and resulted in less freely suspended E. coli. Key words: UV disinfection, microbial floc, sludge retention time, EPS, hydrophobicity, Escherichia coli.
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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".