Laboratory-scale bioaugmentation relieves acetate accumulation and stimulates methane production in stalled anaerobic digesters
Bibliographic record
Abstract
An imbalance between acidogenic and methanogenic organisms during anaerobic digestion can result in increased accumulation of volatile fatty acids, decreased reactor pH, and inhibition of methane-producing Archaea. Most commonly the result of organic input overload or poor inoculum selection, these microbiological and biochemical changes severely hamper reactor performance, and there are a few tools available to facilitate reactor recovery. A small, stable consortium capable of catabolizing acetate and producing methane was propagated in vitro and evaluated as a potential bioaugmentation tool for stimulating methanogenesis in acidified reactors. Replicate laboratory-scale batch digesters were seeded with a combination of bioethanol stillage waste and a dairy manure inoculum previously observed to result in high volatile fatty acid accumulation and reactor failure. Experimental reactors were then amended with the acetoclastic consortium, and control reactors were amended with sterile culture media. Within 7 days, bioaugmented reactors had significantly reduced acetate accumulation and the proportion of methane in the biogas increased from 0.2 ± 0 to 74.4 ± 9.9 % while control reactors showed no significant reduction in acetate accumulation or increase in methane production. Organisms from the consortium were enumerated using specific quantitative PCR assays to evaluate their growth in the experimental reactors. While the abundance of hydrogenotrophic microorganisms remained stable during the recovery period, an acetoclastic methanogen phylogenetically similar to Methanosarcina sp. increased more than 100-fold and is hypothesized to be the primary contributor to reactor recovery. Genomic sequencing of this organism revealed genes related to the production of methane from acetate, hydrogen, and methanol.
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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 | 0.000 |
| 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 | 0.001 | 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".