Fractionated volatile solids for understanding thermophilic pretreatment of waste activated sludge at 55, 65, and 75°C
Bibliographic record
Abstract
Abstract Autohydrolysis or enzyme hydrolysis pretreatment under thermophilic conditions significantly accelerates organic solubilization of waste activated sludge (WAS), allowing enhanced methanogenesis in subsequent mesophilic anaerobic digestion. Solubilization mechanisms can hardly be explained and clarified using only conventional analytical measurements, such as soluble chemical oxygen demand (COD) and volatile suspended solids (VSS). Here, we proposed a new but readily available analytical method where volatile solids (VS) are fractionized into high volatile solids (VS205), moderate volatile solids (VS350), and low volatile solids (VS505). In a laboratory‐scale experiment, anaerobic digesters were operated at 55, 65, and 75°C with thickened WAS. The high volatile solids (VS205) sensitively reflected the temperature effect while the low volatility solids (VS505) showed relatively insensitive results to the examined temperature conditions. This finding indicates that hydrolysis of high volatile solids (VS205) was accelerated more effectively with the increased temperature. Also, based on the experimental results with the fractionized volatile solids, we recommend that autohydrolysis pretreatment should be operated at 75°C for 5 hr to achieve both rapid hydrolysis and reduced energy consumption. Practitioner points The volatile solids (VS) were divided into high volatile, moderate volatile, and low volatile fractions. The fractionated VS showed how organic solids were hydrolyzed in thermophilic pretreatment of thickened waste activated sludge. At the higher temperature (75°C), the high volatile fraction increased substantially compared to 55 or 65°C. The fractionated VS responded more sensitively to the thermophilic temperatures compared to common analysis parameters (COD, VSS). We recommend thermophilic pretreatment at 75°C for 5 hr for thickened waste activated sludge.
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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 | 0.001 | 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".