Biological Nutrient Removal Using a Novel Laboratory‐Scale Twin Fluidized‐Bed Bioreactor
Bibliographic record
Abstract
Abstract The capability of biological nutrient removal from wastewater of a novel laboratory‐scale twin fluidized‐bed bioreactor (TFBBR) was studied. The work showed approximately 96 % organic matter, 84 % nitrogen, and 12 % phosphorus removal efficiencies in the first three phases of the study at influent synthetic municipal wastewater (SMW) flow rates of 150, 190, and 240 L/d, with corresponding organic loading rates of 1.3, 1.7, and 2.3 kg COD m–3 d–1 and nitrogen loading rates of 0.14, 0.18 and 0.25 kg N m–3 d–1. The TFBBR effluent was characterized by <1.0 mg NH4‐N/L, <4.3 mg NO3‐N/L, <6 mg TN/L, <6 mg SBOD/L, and 6–10 mg VSS/L. For the three phases, biomass yields of 0.06, 0.066, and 0.071 g VSS/g COD were observed, respectively, which was a significant further reduction in yield compared to the liquid‐solid circulating fluidized‐bed bioreactor technology developed and patented by this research group, of 0.12–0.16 g VSS/g COD. The very low yield was due to a longer solid retention time of 72–108 d.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".