A technique to determine nitrogen removal rates in systems performing simultaneous nitrification and denitrification
Bibliographic record
Abstract
This paper reports on a possible technique to determine specific nitrification and denitrification rates (SNR and SDNR) in an oxidation-reduction potential (ORP) controlled, intermittent aeration (IA) tank, in which simultaneous nitrification and denitrification (SND) occurred. In addition, SNRs in a three-stage Bardenpho aerobic zone and SDNRs in its anoxic zone were determined. This research was done at bench scale. The technique involves a steady-state run and two additional transient-state tests (created by either ammonia or nitrate shock loading). The rates obtained, using this technique, are the maximum rates possible in a continuous process under certain, improvised conditions. The technique is extremely flexible and generates data relating the rate to substrate concentration in one steady-state run. Data analysis was performed using the integral method; an excellent agreement between predicted and experimental data was found. Zero-order kinetics could describe nitrification in an ammonia concentration range of 1–30 mg/L and denitrification in a nitrate concentration range of 10–30 mg/L. The SNRs in the intermittently aerated, complete-mix (IACM) tank (0.39–1.69 mg g–1 h–1) were considerably lower than those in the 3-stage Bardenpho aerobic zone (3.4–3.81 mg g–1 h–1) due mainly to imposed dissolved oxygen limitations. The SDNRs in the IACM tank were in a range of 0.16–1.26 mg g–1 h–1, which were also considerably lower than that in the 3-stage Bardenpho anoxic zone (2.0–2.5 mg g–1 h–1). Key words: acetate, denitrification, intermittent aeration, kinetics, methanol, nitrification, ORP control, simultaneous nitrification and denitrification.
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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 | 0.001 | 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 | 0.001 |
| 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".