Biological Hydrogen Production from Corn-Syrup Waste Using a Novel System
Bibliographic record
Abstract
The reported patent-pending system comprises a novel biohydrogen reactor with a gravity settler for decoupling of SRT from HRT. The biohydrogenator was operated for 100 days at 37 °C, hydraulic retention time 8 h and solids retention time ranging from 2.2–2.5 days. The feed was a corn-syrup waste generated as a byproduct from an industrial facility for bioethanol production located in southwestern Ontario, Canada. The system was initially started up with a synthetic feed containing glucose at concentration of 8 g/L and other essential inorganics. Anaerobicaly-digested sludge from the St. Mary’s wastewater treatment plant (St. Mary, Ontario, Canada) was used as the seed, and was heat treated at 70 °C for 30 min to inhibit methanogens. After 10 days, when the hydrogen production was steady, the corn-syrup waste was introduced to the system. Glucose was the main constituent in the corn-syrup; its concentration was varied over a period of 90 days from 8 to 25 g/L. The change in glucose concentration was used to study the impact of variable organic loading on the stability of hydrogen production in the biohydrogenator. Hydrogen production rate increased from 10 L H2/L·d to 34 L H2/L·d with the increase of organic loading rate (OLR) from 26 to 81 gCOD/L·d, while a maximum hydrogen yield of 430 mL H2/gCOD was achieved in the system with an overall average of 385 mL H2/gCOD.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".