Meta‐Analysis of Production of Volatile Fatty Acids From Waste Streams: Towards Creating Decision Support Tools for Process Optimization
Bibliographic record
Abstract
After anaerobic digestion, the sludge in wastewater treatment plants encompasses biosolids and food waste entering the sewer systems through food waste grinders in the kitchen sinks, especially in North America. These digested biosolids and food waste are typically discarded in landfills or incinerated. However, producing volatile fatty acids (VFAs) through fermentation of this waste stream of sludge and food waste is a lucrative value chain to biosolids and food waste management. The co-fermentation of sludge and food waste enhances microbial diversity and provides optimal carbon:nitrogen ratio for VFA generation. However, variation in the source and composition of the food waste significantly impacts the fermentation efficiency. In this study, a meta-analysis of 107 studies from North America was performed to understand the correlation between operational parameters and their effects on VFA production to use it as a tool for process optimization. The 107 studies were selected out of 303 from the database of Scopus and Web of Science from the year 2000 to 2024. The included studies were original research articles with inclusive data on VFA production using food waste as a substrate. Initial substrate concentration was found to be a reliable predictor for VFA production, followed by temperature and pH. Substrate concentrations between 9 and 20 gCOD/L, coupled with temperatures around 25°C or lower and neutral to slightly acidic pH, were observed to create favorable conditions for microbial activity and VFA generation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".