Influence of Co-substrates for the Methane Production Rate Kinetics in Anaerobic Fermentation of Pig Slurry
Bibliographic record
Abstract
The influence of co-substrates on kinetics of methane production in anaerobic fermentation of pig slurry was studied by performing a series of laboratory experiments using food waste and biowaste as co-substrates. Experiments were performed in the laboratory reactors of «Hohenheim» biogas testing system in batch mode at the temperature of 37ºC. Given 40 g of fresh pig slurry was fed to each digester and mixed with co-substrates in the ration of 55:45% by organic dry matter content. Methane production rate was used to evaluate co-digestion of pig slurry. Research results showed that co-substrates gave significant effect to methane production kinetics. The kinetic parameters of methane production i.e. potential yield of methane (P), maximum methane yield rate (Rm) and duration of lag-phase (λ) were analyzed using modified Gompertz equation. Technical time to produce 95% of potential methane yield was investigated and efficient period of anaerobic digestion was calculated. The potential production of methane was reached in lag-phase duration (λ) of 10-13 days. Maximal methane production rate was reduced 2-3 times in co-digestion than in mono-digestion of pig slurry. The potential yield of methane (P) was 0.420 Nm3(kg оDM)-1, 0.246 Nm3(kg оDM)-1 and 0.198 Nm3(kg оDM)-1 in mono-digestion and co-digestion of pig slurry with food waste and biowaste, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".