Modeling the metabolic adaptations of the biomass under rapid growth and starvation conditions in the activated sludge process
Bibliographic record
Abstract
For wastewater treatment, the activated sludge models (ASMs) 1, 2, and 3 of the International Water Association (IWA) are accepted as industrial standard. However, many authors have observed that the kinetic parameters of these models depend on the type of substrate, process configuration, and sludge age. Some publications showed that the kinetic parameters of ASMs could be influenced by regulation of enzyme production. Therefore, an engineer aiming to make some modifications to a specific system is not able to predict the response of the real system after the modifications and choose the right configuration or modifications with the same set of parameters. On the other hand, cybernetic models are proposed for modeling cell growth and focus, among other things, on regulation of enzyme production, that is to say on induction. Thus, the objective of this paper is to present an activated sludge model that mimics the enzymatic induction of active biomass within the framework of ASMs. In the proposed model, process rates are modulated according to the environmental conditions and cell history. The model is fitted on the basis of data found in the literature. All data collected from short and long transient experiments were fitted with the same set of parameters, which was not possible with other models. The proposed model gives a more realistic picture of active biomass and of its specific activity under highly varying process conditions, but further research is required to support the model with experimental data. Key words: activated sludge models (ASMs), activity, biomass, enzymes, induction, model, parameter, rate, regulation, sludge, transient.
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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 | 0.001 |
| 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) | 0.001 | 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".