Continuous hydrogen production using upflow anaerobic sludge blanket reactors: effect of organic loading rate on microbial dynamics and <scp>H<sub>2</sub></scp> metabolism
Bibliographic record
Abstract
Abstract BACKGROUND Enriching hydrogen (H2)‐producing bacteria by suppressing methane‐ producing microorganisms is a critical step in continuous biological H2 production. Selective inhibition of methanogens can be achieved by varying the organic loading rate (OLR) in continuous reactors. In this study, continuous H2 production was examined using mixed anaerobic cultures fed glucose in upflow anaerobic sludge blanket reactors (UASBRs) operating at 37°C and at pH 5.0 by varying the OLR at a constant hydraulic retention time. RESULTS A stable H2 yield of 1.64 ± 0.04 mol mol−1 glucose was obtained at OLRs from 8.6 to 12.8 g COD L−1 d−1. Increasing the OLR increased the hydrogenase flux and suppressed methanogenesis. At high OLRs, high acetate and solvent production were associated with Clostridiaceae and Ruminococcaceae while at low OLRs, the presence of Synergistaceae and Propionibacteriaceae were related to the production of propionate and other reduced byproducts. CONCLUSION This study successfully demonstrated continuous H2 production using bench‐scale UASBRs fed glucose. Understanding the OLR effect on H2 production and microbial interaction is important in the full‐scale operation of H2 production facilities using low value feedstocks such as switch grass, corn stover and shorghum. © 2016 Society of Chemical Industry
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".