Decentralized wastewater treatment in an urban setting: a pilot study of the circulating fluidized bed bioreactor treating septic tank effluent
Bibliographic record
Abstract
To meet the increasing wastewater treatment demand while minimizing the land footprint of the treatment systems and plants, more efficient and compact processes are needed. The circulating fluidized bed bioreactor (CFBBR) has been proven to achieve high levels of biological nutrient removal. Past studies at the lab and pilot scale achieved 94% COD removal and 80% nitrogen removal at HRT's of 2–4 h. A collaborative project between Western University and the Guangzhou Institute of Energy Conversion (GIEC), in Guangzhou, China, further explored the treatment of municipal wastewater with the CFBBR. A pilot CFBBR, with aerobic and anoxic columns for nitrification and denitrification, was constructed at the GIEC for in-situ treatment of septic tank effluent from a residential building. Due to high concentrations of ammonia (NH4-N), the wastewater had a COD/N ratio of 2–3. Thus, operating at a longer HRT and supplementing COD, in the form of glucose, was necessary to achieve a high nitrogen removal efficiency. The system was run both with and without supplemental COD at HRT's between 16 and 21 h, treating approximately 1000–1270 L/d. Overall, a COD removal efficiency of at least 92%, ammonia removal of 97%, and nitrogen removal of 82% was achieved. The CFBBR system achieved an effluent with BOD and NH4-N concentrations both below 5 mg/L, a NO3-N concentration below 15 mg/L, and a total nitrogen concentration below 25 mg/L. The compact design of this pilot-CFBBR, coupled with its high BNR performance make it an excellent option for decentralized treatment of urban wastewaters.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".