Investigation of Denitrification Kinetics Using Various Carbon Sources in Sequencing Batch Reactors at Cold Temperature
Bibliographic record
Abstract
Facilities across North America are designing plants to meet stringent limit of technology (LOT) treatment for nitrogen removal. This is in response to the Chesapeake Bay Agreement, which will limit effluent total nitrogen to 3 mg/L. Of particular interest is the use of an alternate external carbon source to replace the most commonly used carbon, methanol. Replacing methanol with an alternate external carbon source for denitrification in the winter will be particularly important since methanol utilizer growth is stunted during colder temperatures. This study focuses on three external carbon sources: methanol, ethanol and acetate. The aim of this study was to obtain the specific denitrification rate (SDNR) of the substrates in two different contexts. Sequencing batch reactors (SBRs) were set up to acclimate carbon free biomass to the specified substrate while in-situ SDNRs were conducted concurrently. Once the biomass was acclimated to the corresponding substrate, a series of ex-situ SDNRs were performed using various biomass/substrate combinations. All experiments were conducted at 13°C. The results suggest that the SDNRs for acetate (32 mgNO3-N/gVSS/hr) and ethanol (30 mgNO3-N/gVSS/hr) are higher than that for methanol (9 mgNO3-N/gVSS/hr). Ethanol acclimated biomass fed with acetate resulted in the highest SDNR of 26 mgNO3-N/gVSS/hr.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".