Black-box modelling of non-stationary N2O dynamics in a full-scale wastewater treatment plant
Bibliographic record
Abstract
Using a data-driven approach, we present and compare linear and nonlinear methods for system identification of the potent greenhouse gas, nitrous oxide (N2O), which is produced during the biological treatment of wastewater. N2O is challenging to estimate, as the full understanding of its production process is yet to be determined. Therefore, data-driven approaches hold promise in advancing our understanding and offering solutions for model-based control, fault detection, and analysis. We present two methods for modelling the N2O in a full-scale wastewater treatment plant; the long short-term memory (LSTM) and a linear ARX model and discuss the performance of these models on real-world implementations. Results indicate that the nonlinear LSTM model has enhanced performance when compared to the linear ARX. While single-step predictions exhibit minimal mean squared error (MSE), the time-invariant models struggle to capture the production mechanisms over multi-step predictions due to the excessive need of multi-year data and non-stationarity and non-normality of the predicted variable.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".