Anaerobic Digestion of Food Waste with the Addition of Biochar Derived from Microwave Catalytic Pyrolysis of Solid Digestate
Bibliographic record
Abstract
This study explores the potential of biochar derived from microwave-assisted catalytic pyrolysis of solid digestate as an additive to enhance the stability and performance of the anaerobic digestion process. The focus was placed on the effects of biochar dosage, pyrolysis temperature, and pyrolysis catalyst on methane production. Biochemical methane potential (BMP) tests using synthetic food waste as the substrate revealed a dosage-dependent relationship with specific methane yield (SMY). At a low biochar dosage of 0.1 g/g total solids (TS), improvement in methane (CH4) production was marginal, whereas a high dosage of 0.6 g/g TS increased CH4 content by at least 10% and improved yield by 35–52%. ANOVA analysis indicated that biochar dosage level significantly influenced CH4 yield, while pyrolysis temperature (400 °C vs. 500 °C) and catalyst (20 wt% K3PO4 vs. 10 wt% K3PO4/10 wt% clinoptilolite) did not lead to significant differences in CH4 yield between the treatments. Correlation analysis results suggested that biochar’s most impactful properties on methane yield would be dosage-adjusted specific surface area (or total surface area per unit volume of substrate) and aromaticity index. The findings underscore the potential of solid-digestate-derived biochar as a beneficial additive for anaerobic digestion and hence the sustainability of food waste management system.
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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".