Energy recovery wastewater treatment plants through anaerobic digestion
Bibliographic record
Abstract
Anaerobic co-digestion (AcoD) helps improve the treatment of organic waste and the recovery of energy in wastewater treatment plants. The current work describes a complete assessment of the various kinetic modeling techniques and the effects of different feedstock compositions on the performance of AcoD based on extensive datasets and sophisticated computational modeling. Eighteen different biomethane potential (BMP) datasets were used to determine several key kinetic parameters, including first-order hydrolysis coefficients (khyd, d-1; 0.08–0.70). The first-order kinetic model was shown to have overwhelmingly better predictive ability (R² > 0.95) and parameter identifiability with respect to the Monod-type models. The incorporation of the modified GISCOD framework with the inhibition function for long-chain fatty acids (LCFA) provided tools for highly accurate simulation of co-digestion dynamics and operational cost reduction of 10.2%. However, feedstock with protein content over 2.5 wt% resulted in significant ammonia inhibition (p-value<0.01) and a reduction of 18–22% of methanogenic activity. Multivariate sensitivity analysis showed protein and lipid fractions to be the predominant controls for process stability and methane yield. Quantitative descriptions were able to clarify the results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".