Effect of Dynamic Loading on Biological Nutrient Removal in a Pilot-Scale Liquid-Solid Circulating Fluidized Bed Bioreactor
Bibliographic record
Abstract
A pilot-scale liquid-solid circulating fluidized bed (LSCFB) bioreactor was employed for biological nutrient removal from municipal wastewater at the Adelaide Pollution Control Plant, London, Ontario, Canada. Lava rock particles of 600 μm were used as a biomass carrier media. The system generated effluent characterized by <1.0 mg NH4N/L , <6.0 mg NO3N/L , <1.0 mg PO4P/L , <10 mg TN/L, and <10 mg SBOD/L at an influent flow of 5 m3/d , without adding any chemicals for phosphorus removal and secondary clarification for suspended solids removal. The impact of the dynamic loading on the LSCFB effluent quality and its nutrient removal efficiencies were monitored by simulating wet weather condition at a maximum peaking factor of 3 for 4 h. The achievability of effluent characteristics of 1.1 mg NH4N/L , 4.6 mg NO3N/L , 37 mg COD/L, and 0.5 mg PO4P/L after 24 h of the dynamic loading emphasize the favorable response of the LSCFB to the dynamic loadings and the sustainability of performance without loss of nutrient removal capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".