Characteristics of Biohydrogen Production and Performance of Hydrogen-Producing Acetogen by Increasing Normal Molasses Wastewater Proportion in Anaerobic Baffled Reactor
Bibliographic record
Abstract
The biohydrogen production efficiency and performance of hydrogen-producing acetogen in a four-compartment anaerobic baffled reactor (ABR) were studied by gradually increasing the influent normal molasses wastewater (NMWW) proportion. When the influent NMWW proportion increased to 55%, ABR could develop microbial community with methanogenic function in 63 days and reach a stable operation. When the influent NMWW proportion increased to 80% and reached a stable state, ethanol fermentation was established from butyric acid fermentation in the first three compartments, whereas butyric acid fermentation in the fourth compartment was strengthened. The average biohydrogen production yield and biohydrogen production capacity by COD removal increased to as high as 12.85 L/day and 360.22 L/kg COD when the influent NMWW proportion increased from 55% to 80%, respectively. Although the biogas yield and the specific biogas production rate reached 61.54 L/day and 232 L/kg MLVSS·day, the biohydrogen production yield and specific biohydrogen production rate were only 12.85 L/day and 48 L/kg MLVSS·day, which results in hydrogen consumption by homoacetogenesis and methanogenesis.
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 | 0.001 | 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".