Smart municipal wastewater treatment sludge management: Enhancement of biogas production from anaerobic digestion amended by optimized sludge-derived biochar
Bibliographic record
Abstract
Improving anaerobic digestion (AD) efficiency at municipal wastewater treatment plants (MWTPs) is essential for enhancing renewable energy recovery and achieving sustainable sludge management, especially in cold climates where AD performance is often limited. This study introduces an integrated approach using phosphoric acid-activated sludge-derived biochar (ASBC), produced from thickened waste-activated sludge (TWAS), to enhance biogas production. The effects of ASBC particle size and concentration were evaluated using response surface methodology (RSM), while computational fluid dynamics (CFD) simulations were applied to optimize reactor mixing and minimize dead zones. The optimized condition (15 g/L ASBC, 500 μm particle size; R-500-15) resulted in the highest biogas yield of 285 mL/g volatile solids (VS), a 48 % increase compared to the control. Additionally, the methane content in biogas increased to 68 %, which was 9.6 % higher than the control sample (62 %). Microbial community analysis showed that the bacteria family Peptostreptococcaceae (known for anaerobic fermentation) and archaea family Methanomicrobiales (known for methane production) had increased relative abundances of 0.760 and 30.2 % relative to the control in this optimized ASBC treatment. CFD modelling confirmed that tailored intermittent mixing (60 rpm for 55 s every 5 min) effectively reduced reactor dead zones to 13 %, contributing to improved substrate distribution and microbial interaction. This work demonstrates a cost-effective and sustainable strategy to optimize AD performance using waste-derived biochar and hydrodynamic enhancement. The approach supports energy-positive wastewater treatment and aligns circular economy and climate action goals, offering valuable guidance for MWTPs seeking to improve operational efficiency and environmental performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".