Evaluation of several respirometry-based activated sludge toxicity control strategies
Bibliographic record
Abstract
Four different strategies including influent storage and reintroduction, step-feeding, rapid sludge recycle and waste sludge storage were evaluated using the denitrification layout of the IWA simulation benchmark. The control objective was to minimise deterioration in effluent quality caused by a certain toxic input event. In these strategies the maximum specific respiration rate (Rmax) was selected as a measured and controlled variable. To simplify the analysis, the toxicant was assumed to be a soluble and nonbiodegradable substance. Two toxic influent files were developed with square-wave input lasting 3 hours. To detect the influent toxicity, a pseudo-online flow-through respirometer was applied. A number of simulations were performed and the results suggested that the influent storage and reintroduction strategy provided the most optimistic results and other strategies could not mitigate the toxic effect. The influent storage and reintroduction strategy strongly depended on reintroduction flow rate from the storage tank. The simulation according to reintroduction flow could estimate the time required for completely treating toxic wastewater stored in the storage tank. Also the IWA simulation benchmark was enhanced to evaluate toxicity effect on the activated sludge process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".