Process control and design considerations for methanol‐induced denitrification in a sequencing batch reactor
Bibliographic record
Abstract
The primary goal of this research was to determine the effect of methanol-induced denitrification on volatile suspended solids production, settleability, and oxidation-reduction potential in a full-scale sequencing batch reactor. Batch tests were also conducted to determine the influence of mixing and acclimatization on the denitrification of wastewater with methanol. The observed sludge production in the full-scale sequencing batch reactor with methanol addition was 0.21 kg volatile suspended solids l(-1) methanol, versus the calculated stoichiometric sludge production of 0.17 kg volatile suspended solids l(-1) methanol. The settleability in the full-scale sequencing batch reactor, measured by the sludge volume index, increases linearly with increasing denitrification rate. The total change in the oxidation-reduction potential magnitude during a sequencing batch reactor cycle increased linearly with increasing denitrification rate. A minimum of 55% increase in the denitrification rate was observed in a batch reactor with methanol addition and a sludge acclimatized to methanol addition, compared to a batch reactor with methanol addition and a non-acclimatized sludge. The non-acclimatized batch reactor had a negligible denitrification rate without methanol addition. However, significant denitrification rates were observed in the acclimatized batch reactors without methanol addition, potentially caused by microbial storage or an increased population of denitrifiers that scavenge any available carbon. A completely mixed batch reactor, with sludge acclimatized to methanol addition during the anoxic cycle, had an increase in the denitrification rate ranging from 660%, without methanol addition, to 200%, with a methanol dosage of 12.7 mg l(-1), compared to the unmixed batch reactor with an acclimatized sludge. Therefore, mixing appears to be critical to the denitrification process, to realize the best kinetic performance.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".