Addition of Different Biochars as Catalysts during the Mesophilic Anaerobic Digestion of Mixed Wastewater Sludge
Bibliographic record
Abstract
Biochar (BC) recently gained attention as an additive for anaerobic digestion (AD). This work aims at a critical analysis of the effect of six BCs, with different physical and chemical properties, on the AD of mixed wastewater sludge at 37 °C, comparing their influence on methane production and AD kinetics. AD batch tests were performed at the laboratory scale operating 48 reactors (0.25 L working volume) for 28 days with the addition of 10 g L−1 of BC. Most reactors supplemented with BCs exhibited higher (up to 22%) methane yields than the control reactors (0.15 Nm3 kgVS−1). The modified Gompertz model provided maximum methane production rate values, and in all reactors the lag-phase was equal to zero days, indicating a good adaptation of the inoculum to the substrate. The potential correlations between BCs’ properties and AD performance were assessed using principal component analysis (PCA). The PCA results showed a reasonable correlation between methane production and the BCs’ O–C and H–C molar ratios, and volatile matter, and between biogas production and BCs’ pore volume, specific surface area, and fixed and total carbon. In conclusion, the physic-chemical properties of BC (specifically, hydrophobicity and morphology) showed a key role in improving the AD of mixed wastewater sludge.
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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 | 0.001 | 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".