Hybrid modelling of nitrogen removal by biofiltration using high-frequent operational data
Bibliographic record
Abstract
ABSTRACT In this research, a parallel hybrid model is presented for the simulation of nitrogen removal by submerged biofiltration of a very large-size wastewater treatment plant. This hybrid model combines a mechanistic and a machine learning model to produce accurate predictions of water quality variables. The models are calibrated and validated using detailed and quality-controlled operational data collected over a period of 3.5 months in 2020. The mechanistic model is a modified activated sludge model that describes the biological, physical and chemical processes taking place in a biofilm reactor based on the domain knowledge of these processes. A three-layer feed-forward artificial neural network with a rectified linear activation function that aims to reduce the mechanistic model's residual error and then correct its output. The results show how the hybrid model outperforms and significantly reduces the size of the mechanistic model's prediction errors of the effluent nitrate concentration from a relative mean error of 12% (mechanistic model) to 2% (hybrid model) during training. The error on nitrate simulations increases to 8% during hybrid model testing, still significantly lower than the error of the mechanistic model. These results support future operational applications of hybrid biofilm models, such as in digital twins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".