Impacts of variable pH on stability and nutrient removal efficiency of aerobic granular sludge
Bibliographic record
Abstract
The impact of pH variation on aerobic granular sludge stability and performance was investigated. A 9-day alkaline (pH=9) and acidic (pH=6) pH shocks were imposed on mature granules with simultaneous chemical oxygen demand (COD), nitrogen and phosphorus removal. The imposed alkaline pH shock (pH 9) reduced nitrogen and phosphorus removal efficiency from 88% and 98% to 66% and 50%, respectively, with no further recovery. However, acidic pH shock (pH 6) did not have a major impact on nutrient removal and the removal efficiencies recovered to their initial values after 3 days of operation under the new pH condition. Operating the reactors under alkaline pH induced granules breakage and resulted in an increased solids concentration in the effluent and a significant decrease in the size of the bio-particles, while acidic pH did not have significant impacts on granules stability. Changes in chemical structure and composition of extracellular polymeric substances (EPS) matrix were suggested as the main factors inducing granules instability under high pH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".