Valorization of brewery waste slurry with glycerol as co‐substrate for hydrogen and butyrate production using dark fermentation
Bibliographic record
Abstract
Abstract BACKGROUND Among the methods to produce hydrogen biologically, dark fermentation stands out mainly due to its low operating requirements. Organic wastes, such as brewing industry waste slurries and glycerol, can provide a cost‐effective feedstock with the additional potential of generating value‐added byproducts, while addressing a wastewater treatment issue. RESULTS The hydrogen production potential in dark fermentation of a high‐strength brewery waste slurry was optimized with a selected seed sludge, initial COD concentration of 50–60 g L−1, pH 6.4 and fermentation time of 30 h. The main end product was butyric acid, accounting for over 50% of the carboxylic acids. The efficiency of the process on the basis of volume of H2 obtained per gram of COD converted into organic acids was 393 ± 5 and 430 ± 6 mL without and with glycerol, respectively, and the molar ratio of H2 per mole of substrate was 71% of the theoretical molar yield when the fermentation is dominated by butyrate as the end product. CONCLUSIONS A proposed brewery sludge treatment system comprising dark fermentation followed by anaerobic digestion is promising and can be more advantageous than anaerobic digestion alone with an increase of 18.5% in energy potential. Alternatively, with recovery of valuable butyrate, a reduction in 4.5 kg of CO2 emissions per cubic meter of sludge treated can be achieved, with a 27% net loss in energy potential. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
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) | 0.001 | 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".