Effect of pre-treatments on biological methane potential of dewatered sewage sludge under dry anaerobic digestion
Bibliographic record
Abstract
The aim of the study is to enhance hydrolysis of dewatered sewage sludge to tackle the problem of low biological methane potential (BMP) and low efficiency of dry anaerobic digestion. Different pre-treatment i.e. physical (ultrasonication), chemical (acid, ozone) and combined (ultrasonication-ozone) methods were investigated and evaluated in terms of BMP and biodegradation. Ultrasonic pre-treatment had the best result among the single technologies, the BMP increased by 104.7%, while total solid (TS), volatile solid (VS) and chemical oxygen demand (COD) reduction were improved by 30.1%, 36.9% and 33.9%, respectively, over control. Combined pre-treatment (ultrasonication-ozone) showed more significant enhancement than single methods as evidenced by 138.2% higher BMP and 53.7%, 63.7% and 57.3% more reduction in TS, VS, COD, respectively, over control. The BMP increment positively correlated either with energy input, concentration or dose of pre-treatment applied. Among the tested methods, the physical pre-treatments out-compete chemical ones. Ultrasonic combined with ozone pre-treatment technology has good energy and economic feasibility.
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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 | 0.001 |
| 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".