Implication of Using Different Carbon Sources for Denitrification in Wastewater Treatments
Bibliographic record
Abstract
Application of external carbon sources for denitrification becomes necessary for wastewater treatment plants that have to meet very stringent effluent nitrogen limits (e.g., 3 to 5 mgTN/L). In this study, we evaluated and compared three carbon sources--MicroC (Environmental Operating Solutions, Bourne, Massachusetts), methanol, and acetate-in terms of their denitrification rates and kinetics, effect on overall nitrogen removal performance, and microbial community structure of carbon-specific denitrifying enrichments. Denitrification rates and kinetics were determined with both acclimated and non-acclimated biomass, obtained from laboratory-scale sequencing batch reactor systems or full-scale plants. The results demonstrate the feasibility of the use of MicroC for denitrification processes, with maximum denitrification rates (k(dmax)) of 6.4 mgN/gVSSh and an observed yield of 0.36 mgVSS/mgCOD. Comparable maximum nitrate uptake rates were found with methanol, while acetate showed a maximum denitrification rate nearly twice as high as the others. The maximum growth rates measured at 20 degrees C for MicroC and methanol were 3.7 and 1.2 day(-1), respectively. The implications resulting from the differences in the denitrification rates and kinetics of different carbon sources on the full-scale nitrogen removal performance, under various configurations and operational conditions, were assessed using Biowin (EnviroSim Associates, Ltd., Flamborough, Ontario, Canada) simulations for both pre- and post-denitrification systems. Examination of microbial population structures using Automated Ribosomal Intergenic Spacer Analysis (ARISA) throughout the study period showed dynamic temporal changes and distinct microbial community structures of different carbon-specific denitrifying cultures. The ability of a specific carbon-acclimated denitrifying population to instantly use other carbon source also was investigated, and the chemical-structure-associated behavior patterns observed suggested that the complex biochemical pathways/enzymes involved in the denitrification process depended on the carbon sources used.
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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) | 0.000 | 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".