Optimized biological nitrogen removal of high-strength ammonium wastewater by activated sludge modeling
Bibliographic record
Abstract
Abstract Wastewater containing high ammonium concentrations is produced from various industrial activities. In this study, the author used a complex activated sludge model, improved by utilizing BioWin© (EnviroSim, Hamilton, Canada) simulation software, to gain understanding of the problem of instability in biological nitrogen removal (BNR). Specifically, the study focused on BNR in an industrial wastewater treatment plant that receives high-strength ammonium wastewater. Using the data obtained from a nine-day sampling campaign and routinely measured data, the model was successfully calibrated and validated, with modifications to the sensitive stoichiometric and kinetic parameters. Subsequently, the calibrated model was employed to study various operating conditions in order to optimize the BNR. These operating conditions include alkalinity addition, sludge retention time, and the COD/N ratio. The addition of a stripping step and modifications to the configuration of the aerators are suggested by the author to increase the COD/N ratio and therefore enhance denitrification. It was found that the calibrated model could successfully represent and optimize the treatment of the high-strength ammonium wastewater.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".