A Novel Liquid‐Solid Circulating Fluidized‐Bed Bioreactor for Biological Nutrient Removal from Municipal Wastewater
Bibliographic record
Abstract
Abstract Biological nutrient removal (BNR) using a novel liquid‐solid circulating fluidized‐bed (LSCFB) bioreactor was assessed with and without particle recirculation. The LSCFB employs attached microbial films for the biodegradation of both organics and nutrients within a single circulating fluidized‐bed unit. This new technology combines the more compact and efficient fixed‐film process with the BNR process that provides the additional removal of nitrogen and phosphorous. A lab‐scale LSCFB was demonstrated to treat degritted municipal wastewater (MWW), operated at an empty‐bed contact time of 0.82 h. The system removed 94, 80 and 65 % of organic (chemical oxygen demand, COD), nitrogen (N), and phosphorous (P), respectively, without particle recirculation, whereas with particle recirculation the system removed excess phosphorus and achieved overall removal efficiencies of 91, 78 and 85 % for C, N, and P, respectively. The system generated effluent characterized by <5 mg biological oxygen demand/L, <5 mg total suspended solids/L, <1 mg NH4‐N/L, <7 mg total nitrogen/L, and <1 mg PO4‐P/L. Combination of nitrification, denitrification and enhanced biological phosphorus removal in one unit saves space, reduces energy consumption, and also produces less sludge at approximately 0.12–0.13 g volatile suspended solids/g COD consumed. Excellent lab‐scale results led to the establishment of a pilot‐scale LSCFB for MWW treatment at a capacity of 5000 L/day. Initial results of the pilot‐study showed a similar trend in BNR as observed in the lab‐study.
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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 | 0.001 | 0.000 |
| 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".