Improved of Biogas Production by Anaerobic Co-digestion of Ziziphus Leaves and Cow Manure Wastes
Bibliographic record
Abstract
This study aimed to use low cost materials and environmentally friendly approach to product biogas by anaerobic co-digestion from agriculture waste and animal waste and evaluate the cumulative biogas and methane yield at optimal operating condition at different substrates mono and co-digestion.For this purpose, biogas production from anaerobic co-digestion of locally organic wastes such as Ziziphus leaves waste (ZLW) and Cow manure waste (CMW) in laboratory scale batch reactor, the organic wastes (ZL), and (CM) were characterized by Kjeldahl analysis system.The effects of Mass ratio, Dilution water and pH solution treatment value for difference type of agriculture waste on the biogas production were taken into full consideration.The results showed the ultimate accumulative of biogas yield from co-digesting at optimum condition was estimated to be 4090 and 2380 mL/g VS, for Co-digestion (ZL:CM) and Mono-digestion (CM), respectively.A higher rate of methane concentration was observed at mesophilic condition, which was estimated to be 67.64 and 52.60%, for Co-digestion (ZL:CM) and mono-digestion (CM), respectively.It can be concluded that the addition of ZL:CM in Co-digester is more significant in increasing the methane concentration and biogas production compared to a mono-digester (CM).This study was analyzed by using a kinetic modified Gompertz model for entire digestion process to get the best fits the experimental data.
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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".