Low-temperature thermal hydrolysis for enhancing sludge anaerobic digestion and antibiotic resistance management: Significance of digester solids retention time
Bibliographic record
Abstract
Recently, there has been a growing inclination towards utilizing primary sludge (PS) fermentation prior to anaerobic digestion (AD) in water resource recovery facilities (WRRFs), where sludge liquor containing volatile fatty acids is used for biological nutrient removal. Nevertheless, using a low-temperature thermal hydrolysis process (THP) to improve AD in WRRFs adopting PS fermentation remains an area that has received limited research attention. Here, we studied the impact of THP (90 °C, 90 min) on anaerobic co-digestion of thickened waste activated sludge (TWAS) and fermented primary sludge (FPS) under varying solids retention times (SRTs) in semi-continuous mode. The study involved two THP schemes: scheme 1, where THP was done for both TWAS and FPS, and scheme 2, where THP was applied to TWAS only. The results demonstrated that reducing SRT from 20 to 15 and 10 d leads to decreased methane yield in both schemes. However, THP significantly enhances methane production, showing improvements of up to 37.9 % (scheme 1) and 31.2 % (scheme 2) under a 15-d SRT. Furthermore, while decreasing SRT increased the proliferation of antibiotic resistance genes (ARGs), thermal hydrolysis could effectively reduce most ARGs, indicating its potential to mitigate antibiotic resistance in the AD process. Overall, these results provide useful perceptions regarding the potential adoption of low-temperature THP in WRRFs with PS fermentation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".