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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".