Feasible Power Production from Municipal Sludge Using an Improved Anaerobic Digestion System
Bibliographic record
Abstract
Anaerobic digestion with ozonation is a promising process to enhance the digestion efficiency and reduce the sludge quantity for disposal. In this study, new process schemes by incorporating thermophilic digestion were studied for further improvements. Pilot tests were performed with three schemes having mesophilic, thermophilic or mesophilic-thermophilic hybrid reactors. In the process scheme with thermophilic digestion, the degradation ratio of VSS components was observed to improve by 5.5% over mesophilic digestion. The amount of ozone consumption could also be reduced by 18%. However, biogas conversion ratio was not improved due to considerable non-degradable organic fraction remaining in soluble form. In batch tests, this soluble fraction was found to readily degrade by mesophilic microorganism. Based on this observation, a mesophilic-thermophilic hybrid flow scheme was developed. In this flow scheme, thermophilic microbes rapidly degraded ozonated sludge and remaining soluble organic components were converted to biogas by mesophilic microbes. This flow scheme reduced ozone consumption as well as improved the biogas conversion of municipal sludge to 78.6%. The cost performance analysis of a municipal WWTP (population equivalent 150,000) considering electricity production resulted in electricity production cost of 5.0 JPY/kWh, lower than the current market price of 9.3 JPY/kWh.
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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) | 0.000 | 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".