Optimized continuous hydrogen production by «Enterobacter aerogens» from glycerol-containing waste
Bibliographic record
Abstract
ABSTRACT Glycerol is the main by-product of biodiesel production. Enterobacter aerogenes has a known ability to convert glycerol (GL) in a fermentative process to yield hydrogen and ethanol. To demonstrate the potential of a continuous fermentative process to valorize crude-glycerol, hydrogen yield was optimized by determining the optimal cultivation conditions in serum bottles, which were then applied to the optimization of the operation of a 3.6-L continuous bioreactor for maximum hydrogen yield. Conditions optimized in bottles were grouped and tested using a Box-Behnken response surface methodology to determine the optimal concentration of inoculum volume (18%), O2 in transfer step (7.5% O2), Na2HPO4 (12 g/L), NH4NO3 (1.5 g/L) and FeSO4.7H2O (6.25 mg/L). Two levels of full factorial design with a middle point were used to optimize the concentration of trace salts including Na2EDTA (3.5 mg/L), CaCl2.2H2O (0 mg/L) and MgSO4.7H2O (200 mg/L) while a parametric study was used to determine the optimal amounts of two phosphate salts (Na2HPO4, KH2PO4). After a scale-up of 30x in batch mode, the optimal operating conditions of the 3.6-L bioreactor (50% working volume) were determined to be: fresh feed rate (0.44 mL/min), liquid recycle ratio (33%), pH (6.4), glycerol concentration (15 g/L), mixing speed (500 rpm), and waste reuse (0%). Using the optimized conditions we demonstrated the stability of the system over time and obtained the highest yields ever reported in CSTR, 0.86 mole hydrogen/mole GL and 0.74 mole ethanol/mole GL, and this at a significantly reduced media cost of $ 0.91 CAD/L (77% lower than previous studies).
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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) | 0.001 | 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 | 0.000 |
| 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".