Impacts of Temperature and Solids Retention Time, and Possible Mechanisms of Biological Hydrolysis Pretreatment on Anaerobic Digestion
Bibliographic record
Abstract
Anaerobic digestion (AD) has benefits in sludge management, energy recovery, and pathogen reduction. In order to better understand the mechanisms of biological hydrolysis (BH) pretreatment on AD, biochemical methane potential (BMP) and continuous stirred-tank reactor (CSTR) tests were utilized to compare untreated municipal combined sludge with pilot-scale BH pretreated sludge. During the BH process, there was 15%, 30%, and 33% (w/w) volatile solids (VS) reduction after BH at 42 °C (BH42) for 24, 48, and 72 h, respectively; under BH61 (42 °C for 36 h and 61 °C for 6 h), and there was 10% and 30% (w/w) overall VS reduction after 36-h and 42-h hydrolysis, respectively. BMP results showed that BH42-pretreated sludge had 22.6% enhancement of methane yield compared to untreated sludge, and BH61 pretreated sludge had 29.4% enhancement of methane yield. Both temperature and solids’ retention time (SRT) contributed to the enhanced AD performance within 36 h, while temperature played more important roles after 36-h BH pretreatment. CSTR tests confirmed the acceleration of anaerobic digestion by BH pretreatment, and higher enhancement was observed when SRT of anaerobic digestion was shorter than 16 days. Through a literature review of BH-related studies, the possible mechanisms were highlighted for further optimization on the scale-up systems in order to reduce carbon footprint and operating expenditure for wastewater treatment plants.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".