Acidogenic Fermentation of Food Waste in a Leachate Bed Reactor at High Organic Loading: Effect of Granular Activated Carbon (GAC) and Inoculum
Bibliographic record
Abstract
Food waste forms a major fraction of municipal solid waste worldwide, constituting 15-30% of municipal solid waste.If the post-harvest losses are included, the global food waste generation exceeds 1.3 billion tons a year, with economic losses exceeding $1 trillion.Landfilling is the primary method of food waste disposal in most countries, resulting in the loss of a valuable resource (food waste) and causing adverse health and environmental effects such as greenhouse gas emissions, pollution of the subsurface environment, and loss of habitat.To ensure environmental and public health, sustainable and cost-effective approaches for food waste stabilization are being intensively researched.Technologies that can stabilize and transform food waste into valuable products present a tangible solution to this challenge.Acidogenic fermentation is emerging biotechnology that transforms food waste to high-value biochemicals such as short-chain fatty acids (SCFAs); thereby combining sustainable management of food waste with resource recovery.Dry fermenters such as leachate bed reactors (LBRs) have received a lot of attention as an economical platform for acidogenic fermentation of food waste.However, LBRs have many operational challenges that need to be resolved including low product yields at high volumetric organic loading, and long fermentation times.This study evaluated the effects of granular activated carbon (GAC) and different inoculum type/enrichment methods to improve the hydrolysis and acidogenesis of food waste in LBRs operated at high volumetric loading (49 g VS/Lreactor).First, the effects of four different GAC loadings of 0, 0.25, 0.38, and 0.51 g GAC/g VSfoodwaste on the performance of LBRs (hydrolysis and acidogenesis) was evaluated.High GAC loading of 0.51 g GAC/g VSfoodwaste achieved hydrolysis yield of 620 g SCOD/kg VSadded and acidification yield of 507 g CODSCFA/kg VSadded, which were the highestx
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".