Biological methane potentials of food waste of different components: Methane yields, production kinetics, and element balance
Bibliographic record
Abstract
• FW components exert significant impact on FW methane yield. • HPLFW feeds showed significantly higher CH 4 yield than HCFW feeds. • FW CH 4 production can be described by a modified two-component kinetic model. • Element formula of organic content of FW were determined. This study assessed the methane production from food waste (FW) with dominant components of Meat (MFW), Fruit &Veg (VFW), Grain (GFW), Dairy (DFW), and the mixed feed of these components (MixFW). The high protein and lipid content FW (HPLFW) of MFW, DFW, and MixFW showed the methane yields of 337.0 ± 3.0, 307.4 ± 0.8, and 297.1 ± 1.2 ml-CH 4 /gCOD, respectively, while those for the high carbohydrate content FW (HCFW) of VFW and GFW were 238.3 ± 1.2 and 171.2 ± 0.3 ml-CH 4 /gCOD, respectively. A modified two-component kinetic (MTK) model was demonstrated to be the best to describe the methane production kinetics of both HPLFW and HCFW types of feeds. The element balance analysis revealed the element formula of the FW feeds and the methane-conversion organic content. The results obtained from this study showed that the high lipid and animal protein content increased the methane yield and biogas methane composition.
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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 | 0.000 |
| 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".