Impact of Hydrothermal Pretreatment Parameters on Mesophilic and Thermophilic Fermentation and Anaerobic Digestion of Municipal Sludge
Bibliographic record
Abstract
Four parameters affecting hydrothermal pretreatment (HTP) of municipal sludge prior to anaerobic digestion and fermentation were investigated. Partial factorial design including several key HTP parameters at two distinct levels, including temperature (170 and 190 °C), retention time (RT) (10 and 30 min), pH (4 and 10), and solid content (SC) (4% and 16%), were studied. Further, the impact of HTP parameters on mesophilic and thermophilic fermentation was explored and compared. Results revealed a significant effect of all HTP parameters on COD solubilization, VFA, and methane yield. There were correlations between HTP parameters and process responses such as VFA yield and methane yield. HTP was found to increase COD solubilization and VFA production between 15 and 20% during thermophilic fermentation in relation to mesophilic treatment. All parameters, including SC, temperature, pH, and RT, were important contributing factors affecting methane production during anaerobic digestion. The highest methane production yield of 269 mL CH4/g TCOD added was observed at the highest SC (16%) and pH (10) and at the lower temperature (170 °C) and RT (10). HTP is expected to be combined with other intensification routes to treat waste with high solid contents improving the fermentation and anaerobic digestion 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".