Characterizing denitrification kinetics at cold temperature using various carbon sources in lab-scale sequencing batch reactors
Bibliographic record
Abstract
Wastewater treatment plants in the Chesapeake Bay region are becoming more interested in external carbon sources for denitrification. This is in response to the recent regulations to remediate the Chesapeake Bay, which will limit effluent total nitrogen to near 3 mg/L for plants, thus requiring near complete elimination of inorganic nitrogen species. Since sufficient internal carbon is usually not available for complete denitrification, external carbon is needed to supplement internal sources. Of particular interest is the use of an alternate external carbon source to replace the least expensive source methanol. This study focuses on three commonly available external carbon sources: methanol, ethanol and acetate. The aim of this study was to obtain the specific denitrification rate (SDNR) of the substrates under several conditions. Sequencing batch reactors (SBRs) were set up to first grow biomass to the specified substrate while in situ SDNRs were conducted concurrently. Once the biomass was grown with the corresponding substrate, a series of ex situ SDNRs were performed using various biomass/substrate combinations to evaluate response to substrate combinations at 13 degrees C. Results from this study indicate that the SDNRs for biomass grown on methanol, ethanol and acetate were 9.2 mg NO(3)-N/g VSS/hr, 30.4 mg NO(3)-N/gVSS/hr and 31.7 mg NO(3)-N/g VSS/hr, respectively, suggesting that acetate and ethanol were equally effective external carbon sources followed by much lower SDNR using methanol. Ethanol could be used with methanol biomass with similar rates as that of methanol. Additionally, methanol was rapidly acclimated to ethanol grown biomass suggesting that the two substrates could be interchanged to grow respective populations with a minimum lag period.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".