Biomethane Recovery From Industrial Organic Waste: Effect Of Substrate Type And Food-To-Microorganism (F/M) Ratio
Bibliographic record
Abstract
Food processing and packaging industries witnessed significant increase during recent decades to keep pace with population growth. Municipalities around the world are therefore adopting circular economy approach to resolve growing industrial waste generation and achieve zero-emission by 2050. Several studies indicated that anaerobic digestion (AD) process is a promising technique for waste treatment due to its low carbon footprint and potential for recovering several value-added products including biomethane. In this study, AD processing was investigated using five industrial wastes including bakery processing and kitchen waste (BP+KW) mixture, fat, oil, and grease (FOG), powder whey, ultrafiltered (UF) milk permeates, and pulp and paper (P&P) compost. Biochemical methane potential (BMP) was conducted under mesophilic conditions for 40 days to investigate biomethane generation at different food-to-microorganism (F/M) ratios of 1, 2, 4, and 6. Experimental results showed that methane yield values decreased with increasing F/M ratios for all feedstocks. The most optimum ratio for methane yield in all feedstocks was F/M=1 g TCOD/g VSS except for powder whey at which F/M=2 was the optimum ratio. The maximum methane yield values were 454 mL CH4/ g VS added (336 mL CH4/ gTCOD added) for FOG and the lowest value of 190 mL CH4/ g VS added (135 mL CH4/ gTCOD added) for P&P at F/M=1. Pearson’s coefficient values ranged from -0.85 to -0.97 for all feedstocks: thus, indicating a strong inverse correlation between methane yield and increasing F/M ratios. Process kinetics was also investigated using first-order model where the highest reaction rate coefficient (k) values were observed for FOG
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".