Potential of Wastewater-Treating Anaerobic Granules for Biomethanation of Synthesis Gas
Bibliographic record
Abstract
Gasification of biomass produces a mixture of gas (mainly carbon monoxide (CO), carbon dioxide (CO(2)), and hydrogen (H(2))) called synthesis gas, or syngas, by thermal degradation without combustion. Syngas can be used for heat or electricity production by thermochemical processes. This project aims at developing an alternative way to bioupgrade syngas into biogas (mainly methane), via anaerobic fermentation. Nonacclimated industrial granular sludge to be used as reactor inoculum was initially evaluated for mesophilic carboxydotrophic methanogenesis potential in batch tests at 4 and 8 mmol CO/g VSS.d, in the absence and presence of H(2) and CO(2), respectively. Granular sludge was then introduced into a 30 L gas-lift reactor and supplied with CO, to study the production of methane and other metabolites, at different gas dilutions as well as feeding and recirculation rates. A maximal CO conversion efficiency of 75%, which was gas-liquid mass transfer limited, occurred at a CO partial pressure of 0.6 atm combined with a gas recirculation ratio of 20:1. The anaerobic granule potential for methanogenesis from CO was likely hydrogenotrophic, combined with CO-dependent H(2) formation, either under mesophilic or thermophilic conditions. Thermophilic conditions provide the anaerobic granules with a CO-bioconversion potential significantly larger (5-fold) than under mesophilic conditions, so long as the gas-liquid transfer is alleviated.
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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) | 0.001 | 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 | 0.000 |
| 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 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".