Anaerobic digestion of corn ethanol thin stillage in batch and by high-rate down-flow fixed film reactors
Bibliographic record
Abstract
Thin stillage (CTS) from a dry-grind corn ethanol plant was evaluated as a carbon source for anaerobic digestion (AD) by batch and high rate semi-continuous down-flow stationary fixed film (DSFF) reactors. Biochemical methane potential (BMP) assays were carried out with CTS concentrations ranging from approximately 2,460-27,172 mg total chemical oxygen demand (TCOD) per litre, achieved by diluting CTS with clean water or a combination of clean water and treated effluent. High TCOD, SCOD and volatile solids (VS) removal efficiencies of 85 ± 2, 94 ± 0 and 82 ± 1% were achieved for CTS diluted with only clean water at an organic concentration of 21,177 mg TCOD per litre, with a methane yield of 0.30 L methane per gram TCOD(removed) at standard temperature and pressure (STP, 0 °C and 1 atmosphere). Batch studies investigating the use of treated effluent for dilution showed promising results. Continuous studies employed two mesophilic DSFF anaerobic digesters treating thin stillage, operated at hydraulic retention times (HRT) of 20, 14.3, 8.7, 6.3, 5 and 4.2 d. Successful digestion was achieved up to an organic loading rate (OLR) of approximately 7.4 g TCOD L(-1)d(-1) at a 5 d HRT with a yield of 2.05 LCH(4) L(-1)d(-1) (at STP) and TCOD and VS removal efficiencies of 89 ± 3 and 85 ± 3%, respectively.
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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 | 0.001 |
| 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".