Anaerobic Digestion Performance and Microbial Community Structures in a Pilot‐Scale Up‐Flow Anaerobic Sludge Blanket (UASB) Treating Distillery Wastewater
Bibliographic record
Abstract
ABSTRACT Anaerobic digestion (AD) is a promising technology for treating high‐strength industrial wastewater while recovering biogas as a renewable energy source. In this study, a pilot‐scale up‐flow anaerobic sludge blanket (UASB) reactor, with a working volume of 225 L, was operated for 130 days to treat raw distillery wastewater. Three different types of distillery wastewater, including centrate, spent caustic, and other low‐strength process wastewater generated during operations (e.g., condensation water), were mixed to balance extreme pH levels and create conditions more suitable for AD. The UASB reactor demonstrated stable performance at an organic loading rate (OLR) of 10.0 kg COD/m3/day, achieving a COD removal efficiency of 86%. Average methane yield was more than 71% throughout the operation. Microbial community analysis revealed a significant increase in key syntrophic bacteria and methanogens, with Methanobacterium accounting for 55.8% of the archaeal population. Network and Mantel analyses indicated that syntrophic partnerships play a crucial role in enhancing AD of distillery wastewater under high OLR conditions. Overall, the UASB reactor exhibited high process stability, highlighting its potential for large‐scale application in distillery wastewater treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".