Compared performance of trickle-bed and fluidized bed bioreactors for syngas bio-upgrading into RNG
Bibliographic record
Abstract
The present study investigates, optimizes and compares the conversion of carbon monoxide (CO) and syngas (CO/Hz/C02, 40/40/20, and 20/20/10, v/v) to renewable natural gas (RNG) in two types of reactors, trickle-bed reactor (TBR) and fluidized bed reactor (FBR), highlighting their respective advantages and disadvantages. The comparison considered various aspects of reactor operation efficiency with regards to the specific roles of the different microbial trophic groups for RNG production. Overall, TBR results indicate good conversion efficiencies (up to 97%) and a relatively constant stoichiometry-based CH4 yield (88-100%), for CO partial pressures lower than 0. 5atm. regardless of the operational condition tested. Once the biofilm was sufficiently developed, a maximum CO conversion activity of 37 mmolCO.g- 1 volatile suspended solid (VSS). d-1 was achieved. In FBR, restricted mass transfer and absence of attached biomass growth limited the overall reactor efficiency. Only 10% of initial biomass concentration was recovered at the end of the test. The reactor was operative at higher CO partial pressure with non-diluted syngas but the maximum efficiency obtained under stable operating conditions was barely 82-85%. During the reactor operation, methanogenic, hydrogenophilic, acetoclastic and carboxydotrophic specific activities varied in function of substrate composition, biofilm type and structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".