Modeling acidogenic and sulfate‐reducing processes for the determination of fermentable fractions in wastewater
Bibliographic record
Abstract
The biochemical acidogenic potential (BAP) of a wastewater is the maximum concentration of volatile fatty acids (VFAs) that can be measured at the end of an anaerobic fermentation test. A model was constructed to describe the acidogenic reactions occurring during BAP tests and to divide the BAP into organic fractions. The model was calibrated with a set of specific experiments highlighting the role of sulfate-reducing bacteria on acidogenic processes, which description was necessary for correct parameter identification. The model could describe acidogenic fermentation processes, with or without sulfate reduction, at 20 degrees C, for 13 wastewaters of different origin, composition, and settleability using the same optimized parameters. A simplified version of the model, without sulfate reduction, was able to describe VFA production by the adjustment of only three variables: readily fermentable organic matter (Sf), anaerobically hydrolyzable organic matter (Xf), and heterotrophic acidogenic biomass (Xha), which proved to be coherent with the experimental BAP value. The combination of the BAP test and the model developed in this study resulted in a new reliable tool to characterize wastewater under anaerobic conditions. As VFAs are the main substrates for phosphate-accumulating organisms (PAOs), the use of organic fractions VFA, Sf, Xf, and Xha in wastewater treatment plant modeling could improve the predictability and optimization of enhanced biological phosphorus removal (EBPR) processes.
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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 | 0.002 | 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".