Optimisation of wastewater biological denitrification based on mathematical simulation
Bibliographic record
Abstract
With increasing urbanisation and industrial activity, municipal wastewater treatment faces growing challenges, particularly in nitrogen removal. This study developed a bioelectrochemical system to enhance microbial activity and nitrogen removal pathways through electrochemical properties. A mathematical evaluation model was constructed, and experiments were conducted to analyse the effects of electrochemical characteristics, dissolved oxygen levels, and carbon/nitrogen ratios on nitrogen migration, conversion, and removal. Results showed that applying a power supply shortened the domestication time of nitrifying bacteria and improved nitrogen removal efficiency. Under high dissolved oxygen conditions (4.5–5.5 mg/l) with applied voltage, NO3−-N removal reached 73.72%. At a high carbon/nitrogen ratio of 8, removal increased to 89.63%. With 0.2 V constant power, high dissolved oxygen, and a high carbon/nitrogen ratio, NO3−-N levels dropped to nearly 0 mg/l, achieving a total nitrogen removal rate of ≈99%. These findings demonstrate the effectiveness of the proposed bioelectrochemical denitrification method, offering a new technological approach for municipal wastewater treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".