Improving Nutrient Removal While Reducing Energy Use at Three Swiss WWTPs Using Advanced Control
Bibliographic record
Abstract
Aeration consumes about 60% of the total energy use of a wastewater treatment plant (WWTP) and therefore is a major contributor to its carbon footprint. Introducing advanced process control can help plants to reduce their carbon footprint and at the same time improve effluent quality through making available unused capacity for denitrification, if the ammonia concentration is below a certain set-point. Monitoring and control concepts are cost-saving alternatives to the extension of reactor volume. However, they also involve the risk of violation of the effluent limits due to measuring errors, unsuitable control concepts or inadequate implementation of the monitoring and control system. Dynamic simulation is a suitable tool to analyze the plant and to design tailored measuring and control systems. During this work, extensive data collection, modeling and full-scale implementation of aeration control algorithms were carried out at three conventional activated sludge plants with fixed pre-denitrification and nitrification reactor zones. Full-scale energy savings in the range of 16-20% could be achieved together with an increase of total nitrogen removal of 40%.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".