Biological Phosphorus Removal and Denitrification of a Fish Farm Effluent in a Sequencing Moving Bed Biofilm Reactor
Bibliographic record
Abstract
Abstract Removal of phosphate and nitrate from the effluent of a fish farm with a recirculation system was tested in a sequencing moving bed biofilm bioreactor (SMBBR) over a 160-day period. This bioprocess made use of a stock tank (ST) that allowed the same volume of anaerobic water to be reused from one batch to another. Water from the ST contained an excess of a carbon source (acetate), which made it possible to alternate between anaerobic (1.5 h) and aerobic/anoxic (4 h) conditions to achieve enhanced biological phosphorus removal (EBPR). The developed biomass (2,072 mg total solids m-2 d-1 and 892 mg total volatile solids m-2 d-1 ) removed 7.5 mg of PO4-P per litre and 8.5 mg of NO3-N per litre from an influent containing 10 mg of PO4-P per litre and 21 mg NO3-N per litre. The dynamic variation of phosphate and chemical oxygen demand (COD) in the influent showed that the biomass was sensitive to the CODavailable/Pinfluent ratio. A ratio of 10 to 15 mg of COD per milligram of P seemed to favour phosphorus accumulating organisms (PAOs). Differences between the nitrate, phosphate, and oxygen reduction kinetics suggested that the denitrification could be attributed to organisms other than PAOs. The SMBBR-ST showed potential for EBPR and for denitrification as well. However, the economic feasibility of implementing such a process in a full-scale operation remains to be demonstrated.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".