Differential impact of acidic and alkaline conditions on hydrothermal pretreatment, fermentation and anaerobic digestion of sludge
Bibliographic record
Abstract
Abstract Anaerobic digestion and fermentation processes in wastewater sludge treatment are limited by several factors, including the slow breakdown of complex organic matter and solubilization of solids. In this study, thermochemical pretreatment of thickened waste activated sludge using high temperature (>170 °C) was investigated to understand the impact of the pretreatment on the volatile fatty acids (VFA) production and its fractions during the fermentation process. Furthermore, the influence the thermochemical pretreatment on sludge disintegration and methane recovery was investigated. A range of acidic and alkaline conditions over the pH range of 4.5–10 was examined. Sludge (pH adjusted) was exposed to hydrothermal pretreatment (HTP) at a temperature of 170 °C for 30 min. Pretreated samples were then subjected to batch fermentation and methane potential tests which revealed that acidic and alkaline conditions resulted in increased sludge solubilization during HTP. Acidic conditions were associated with a higher VFA production yield of up to 185 mg chemical oxygen demand/g total chemical oxygen demand. Alkaline conditions led to a higher methane production yield where the maximum yield (276 mL CH4/g total chemical oxygen demandadded) was found to occur at pH 10. Therefore, alkaline sludge used for fermentation has shown technical and economic feasibility for sludge carbon recovery.
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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 | 0.000 | 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".