A distillery by-product as an external carbon source for enhancing denitrification in mainstream and sidestream treatment processes
Bibliographic record
Abstract
The use of fusel oil as an 'alternative' carbon source for denitrification in the mainstream and sidestream treatment processes was studied. Research comprised two kinds of batch experiments as well as acclimation of process biomass to external carbon sources. In the conventional nitrate utilization rate (NUR) measurements (one-phase experiments with non-acclimated biomass), the NUR with fusel oil was 1.4-1.7 g N/(kg VSS·h which was comparable to NUR with ethanol and with slowly biodegradable fraction of the settled wastewater. When fusel oil was added at the beginning of the anoxic phase, preceded by an anaerobic phase (in two-phase experiments with non-acclimated biomass), the NURs of 2.5-2.9 g N/(kg VSS·h) were comparable to the tests without the addition of any external carbon sources. The addition of fusel oil and ethanol resulted in a significant enhancement of the denitrification efficiency in lab-scale sequencing batch reactors treating sludge reject water. The NURs continuously increased from below 1 g N/(kg VSS·h) to over 10 g N/(kg VSS·h) over the entire 4-week operational period, indicating gradual acclimation to the substrate. The overall total N removal efficiency reached ∼90%.
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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 | 0.000 | 0.001 |
| 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".