Monitoring anaerobic digestion: a 2-year brewery case study
Bibliographic record
Abstract
Operational data from an anaerobic wastewater treatment plant (expanded granular sludge bed (EGSB) reactor) were analysed before and after a defect with the solids separator. The results presented suggest that a newly available method for the analysis of total volatile fatty acids (VFAs) was ideal as a rapid, onsite, operational indicator of reactor stability. These total VFAs were shown to provide an earlier warning of the separator problem than the other rapid routine methods of monitoring digesters such as alkalinity and suspended solids. Chemical oxygen demand (COD) removal, pH and gas yield were not as useful for monitoring because of their slow response. The results are from a high rate reactor; the loads were 18 kg COD/m3/d in the first year and 26 in the second with 4·4 d hydraulic retention time. The results for both years of operation demonstrate a 95% conversion of COD into gas with an additional contribution from solids digestion (specific gas yield of 0·4 l methane (CH4)/g CODrem). This high performance was attributed to the solubility of the COD and the efficient EGSB mixing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".