Sustainable enhancement of biogas production from a cold-region municipal wastewater anaerobic digestion process using optimized sludge-derived and commercial biochar additives
Bibliographic record
Abstract
Anaerobic digestion (AD) of municipal wastewater sludges produces valuable solid digestate and biogas. Biogas is a source of clean energy and enhancement of its production has been of recent interest for increased electricity generation, among other products. The objective of this study was the development of a novel municipal sludge-derived biochar and its application in a municipal wastewater AD system to increase the biogas production rate. Thickened waste-activated sludge (TWAS) samples were collected from the cold-region municipal wastewater treatment plant and used to synthesize biochar applied in the simulation of AD processes using laboratory-scale reactors. The TWAS-derived biochar was synthesized using the commonly used furnace pyrolysis (sludge-based biochar, SBC), and more novel microwave pyrolysis including phosphoric acid as a microwave activator (activated sludge-based biochar, ASBC). The microwave pyrolysis conditions were optimized using a computational fluid dynamics (CFD) technique. In addition, various commercially available carbon-based additives were assessed for their impacts on the AD process including activated carbon, wood-derived biochar, and forest residue-derived biochar. Results showed that the ASBC increased the cumulative methane production by 50% (333 mL/g VS) versus the control sample (221 mL/g VS) after 30 d. The ASBC showed higher surface area, electrical conductivity, and metal contents versus the other biochars which boosted the AD microbial community growth leading to higher organic matter conversion into biogas. In the ASBC-amended digesters, the bacterial phylum Bacteroidota , which contains a major genus of the dgA-11-gut-group , exhibited a synergy between organic substrate fermentation and volatile fatty acid production, resulting in enhanced biogas production. The TWAS biochar demonstrated promising performance in enhancing the AD process fostering energy and resource self-sufficiency at municipal wastewater treatment plants. This smart sludge management aligns well with sustainable waste management practices and clean energy production strategies, especially considering that the biochar was sourced from a readily available continuous waste-product stream. • Computational fluid dynamics optimized the biochar production process. • Sludge-based biochar improves anaerobic digestion processes. • Sludge-based biochar biogas production compositions were >70% CH 4 and <30% CO 2 . • Anaerobic digestion including sludge-based biochar produced more stable digestate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".