Potential of in-situ sensors with ion-selective electrodes for aeration control at wastewater treatment plants
Bibliographic record
Abstract
A pilot-scale activated sludge wastewater treatment plant (WWTP) operated with nitrification and pre-denitrification was monitored with a set of on-line sensors for over 3 years. Wet-chemistry ex-situ analyzers, UV and UV-Visible in-situ sensors and in-situ sensors based on ion-selective electrodes (ISE) were used. New ISE sensors for ammonium, nitrate and nitrite, adapted to water and wastewater matrices, have been released in recent years, With adequate quality control they proved to be highly accurate and reliable in WWTP influents and activated sludge (AS) reactors even at the end of the biological treatment zone, working at low ammonium concentrations (1-2 mgN/l). The ammonium measurement was used to test several feed-forward and feed-back aeration control strategies. The first aim was to keep inorganic nitrogen compounds, i.e. ammonium, nitrate and particularly nitrite, as low as possible in the effluent, and within Swiss national standards (<2.0 mgNH(4)-N/l, <0.3 mgNO(2)-N/l, 24 h average). All the strategies were successful at keeping ammonium low and subsequently at gaining denitrification capacity to significantly reduce the total nitrogen discharge. Some control strategies however generated temporary peaks of ammonium or even accumulation of nitrite.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".