Treatment of sulfur and nitrogen by microoxygenated microbial sludge: stoichiometry and model
Bibliographic record
Abstract
A microoxygenated biological reactor (MOBR) was used to treat high concentrations of sulfur, nitrogen and organic pollutants by integrating the processes of sulfate reduction, sulfide oxidation, nitrification, denitrification and organic matter removal into a single reactor under 0·10–0·15 mg/l of dissolved oxygen (DO). Stoichiometric equations and kinetic models describing biochemical reactions catalysed by MOBR sludge were developed. Microcosm experiments were carried out using MOBR sludge to validate the stoichiometric equations and develop a model of microbiological processes in the MOBR. The theoretical predictions from stoichiometric equations agreed well with the experiments, and the kinetic model fit well the experimental data. In sulfate reduction, 95·7% of sulfate was reduced to sulfide and 76·7% of sulfide was oxidised to S0 in the sulfide oxidation process. In the nitrification process, 76·5% of ammonium was oxidised to nitrate and 98·2% of nitrate was denitrified to nitrogen gas (N2). In methanogenesis, methanogens consumed less organic carbon (C) than sulfate-reducing bacteria and denitrifying bacteria. It was found that the maximum specific growth rate of sulfate-reducing bacteria and methanogens under microoxygenation was lower than that under anaerobic conditions. In addition, there was a higher inhibitory effect of sulfide on methanogens in micro-DO than in the anaerobic system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".