Optimizing the Mixing Ratios of Source-Separated Organic Waste and Thickened Waste Activated Sludge in Anaerobic Co-Digestion: A New Approach
Bibliographic record
Abstract
Anaerobic co-digestion (AnCoD) presents several advantages over conventional mono-digestion. Various factors can impact the efficiency of the co-digestion process, including the mixing ratio of the feedstocks. This study primarily investigates the effects of different mixing ratios on methane production during the co-digestion of source-separated municipal organic waste (SSO) with thickened waste activated sludge (TWAS). While the C/N or COD/N ratio has generally been used for optimizing the mixing ratios of co-digested feedstocks, a new approach is introduced in this study to evaluate the effects of the lipid, protein, and carbohydrate (L:P:C) ratios on the efficiency of AnCoD with respect to methane production, kinetics, and synergism at mixing ratios of TWAS:SSO of 10:90, 30:70, 50:50, 70:30, and 10:90. AnCoD improved methane production and kinetics relative to TWAS at all mixing ratios, the highest of which was at the 10:90 ratio, corresponding to a methane yield, maximum methane production rate, and an L:P:C ratio of 353 mL CH4/g COD, 25 mL CH4/g COD/d, and 8:1:18, respectively. Improvements in methane yields and kinetics due to synergy were evident at all mixing ratios, with improvements in methane yields ranging from 11 to 23% and improvements in kinetics ranging from 18 to 58%. Improvements in methane yields and kinetics were insensitive to the feedstock composition beyond the 50:50 mixing ratio.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".