Simultaneous Carbon and Nitrogen Removal in Anoxic-Aerobic Circulating Fluidized Bed Biological Reactor (CFBBR)
Bibliographic record
Abstract
Biological nutrient removal (BNR) in municipal wastewater treatment to remove carbonaceous substrates and nutrients, has recently become increasingly popular worldwide due to increasingly stringent regulations. Biological fluidized bed (BFB) technology, which could be potentially used for BNR, can provide some advantages such as high efficiency and a compact structure. This work shows the results of simultaneous elimination of organic carbon and nitrogen using a circulating fluidized bed biological reactor (CFBBR, which has been developed recently for chemical engineering processes. The CFBBR has two fluidized beds, running as anoxic and aerobic processes to accomplish simultaneous nitrification and denitrification, with continuous liquid recirculation through the anoxic bed and the aerobic bed. Soluble COD concentrations in the effluent ranging from 4 to 20 mg l(-1) were obtained at varying COD loading rates; ammonia nitrogen removal efficiencies averaged in excess of 99% at a minimum total hydraulic retention time (HRT) of 2.0 hours over a temperature range of 25 degrees C to 28 degrees C. Effluent nitrate nitrogen concentration of less than 5 mg l(-1) was achieved by increasing effluent recycle rate. No nitrite accumulation was observed either in the anoxic bed or in the aerobic bed. The system was able to treat grit chamber effluent wastewater at a HRT of 2.0 hours while achieving average effluent BOD, COD, NH3-N, TKN, nitrates, total phosphate, TSS and VSS concentrations of 10 mg l(-1), 18 mg l(-1), 1.3 mg l(-1), 1.5 mg l(-1), 7 mg l(-1), 2.0 mg l(-1), 10 mg l(-1) and 8 mg l(-1) respectively. The CFBBR appears to be not only an excellent alternative for conventional activated sludge type BNR technologies but also capable of processing much higher loadings that are suitable for industrial applications.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".