Biogas production performance of mesophilic and thermophilic anaerobic co-digestion with fat, oil, and grease in semi-continuous flow digesters: effects of temperature, hydraulic retention time, and organic loading rate
Bibliographic record
Abstract
Anaerobic co-digestions with fat, oil, and grease (FOG) were investigated in semi-continuous flow digesters under various operating conditions. The effects of hydraulic retention times (HRTs) of 12 and 24 days, organic loading rates (OLRs) between 1.19 and 8.97 gTVS/Ld, and digestion temperatures of 37 degrees C and 55 degrees C on biogas production were evaluated. It was proposed that, compared to anaerobic digestion with wastewater treatment plant sludge (primary raw sludge), semi-continuous flow anaerobic co-digestion with FOG could effectively enhance biogas and methane production. Thermophilic (55 degrees C) co-digestions exhibited higher biogas production and degradation of organics than mesophilic co-digestions. The best biogas production rate of 17.4 +/- 0.86 L/d and methane content 67.9 +/- 1.46% was obtained with a thermophilic co-digestion at HRT = 24 days and OLR = 2.43 +/- 0.15 g TVS/Ld. These were 32.8% and 7.10% higher than the respective values from the mesophilic co-digestion under similar operating conditions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".