Applicability of industrial wastewater as carbon source for denitrification of a sludge dewatering liquor
Bibliographic record
Abstract
The applicability of four industrial waste streams from potato processing, canola processing and oil refining, biodiesel production (glycerol), and glycol as substitutes to methanol and ethanol in denitrification of anaerobically digested sludge dewatering liquor (centrate) was evaluated in bench-scale sequencing batch reactors. It was found that glycerol was the best substitute with the specific denitrification rate (SDNR) of 13 mg NO3-N/(g VSS x h) followed by potato processing wastewater at 12mg NO3-N/(g VSS x h). Both substrates produced faster SDNR than methanol's 10mg NO3-N/(g VSS x h); however, they were inferior to ethanol's 17 mg NO3-N/(g VSS x h). Glycol had SDNR of 8 mg NO3-N/(g VSS x h) and demonstrated a very fast acclimation rate, i.e. the response in increased denitrification rate was visible in three days following glycol addition. Canola processing and oil refining wastewater was considered an inappropriate carbon source due to a low SDNR of 5 mg NO3-N/(g VSS x h) and apparent inhibitory effect on nitrification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".