Thermal-assisted aerobic composting: a sustainable approach to sludge dewatering and process optimization
Bibliographic record
Abstract
• Thermal assistance reduced aerobic composting initiation time by about 85.71%. • Thermal assistance reduced complete aerobic composting cycle by about 68.18%. • Time periods of moisture removal and organic matter degradation are identified. • The substance-energy changes of thermal-assisted aerobic composting is revealed. Thermal-assisted aerobic composting is a novel process for treating municipal dewatered sludge (MDS), the core of which is effective moisture removal from MDS through the synergistic action of thermal treatment and microorganisms. In this study, the operational condition combination of the thermal-assisted aerobic composting process was effectively optimized through the implementation of an orthogonal experiment. On the basis of these findings, the technical superiority of this process was confirmed by comparing it with traditional drying processes and by exploring the material and energy changes during the thermal-assisted aerobic composting process. The results revealed that the optimal process conditions for thermal-assisted aerobic composting were as follows: a heating temperature of 50 °C, a ratio of MDS to auxiliary materials (AM) of 4:1, and continuous turning. Thermal assistance significantly shortened the initiation time of aerobic composting, reducing the entire composting cycle to 7 days. The mixture of MDS and AM after completing the process presented a low moisture content (21.8 ± 2.2%) and high calorific value (19583.1 ± 42.4 kJ/kg), indicating that the mixture has excellent performance in subsequent thermal treatment. Process analysis revealed that moisture removal primarily occurred during the 0 st-10 th days of operation, whereas organic matter degradation mainly took place between the 10 th -20 th hours. The process heat changes revealed that the heat input exceeded the heat output, demonstrating that rapid moisture content reduction could spontaneously proceed in a positive direction. This study provides a theoretical basis and foundation for an inventive path for the drying MDS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".