The impact of reduced phosphorus levels on microbial floc properties during biological treatment of pulp and paper wastewaters
Bibliographic record
Abstract
The effect of limiting P in activated sludge was investigated in laboratory scale sequencing batch reactors (SBRs) fed effluent from a container board mill. Floc characterization included measurement of hydrophobicity, surface charge, and analysis of extracellular polymeric substances (EPS). Reactor performance was assessed by monitoring COD and inorganic P removal, MLSS, and sludge settleability (SVI and batch settling flux) over a period of eight months. Control reactors (BOD:N:P of 100:5:1) were compared to reactors run under P-limited conditions (100:5:0.3; 100:5:0.1). Reactor performance at lower temperatures (14 degrees C; control = 26 degrees C) was also studied to assess the impact of P-limitation. Changes in floc structure and the composition of EPS occurred within 1 to 3 days following a reduction in P levels. There was an insignificant increase in SVI; however, gravitational settling velocity and batch settling flux values for low P floc were consistently higher than for floc generated under control conditions. Lower temperatures (14 degrees C) resulted in a deterioration in floc settling properties at a BOD:N:P of 100:5:1. This impact on settling was significantly reduced at a BOD:P of 100:0.1. Reducing P concentrations in the wastewater treatment system has the potential to improve sludge settleability and reduce final P discharges.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".