Modeling biological nutrient removal in a liquid–solid circulating fluidized bed bioreactor
Bibliographic record
Abstract
Abstract BACKGROUND: Both laboratory‐scale and pilot‐scale liquid–solid circulating fluidized bed (LSCFB) bioreactors have demonstrated excellent biological nutrient removal (BNR) from municipal wastewater. In this study, a model for the LSCFB for biological nutrient removal has been developed, calibrated, and validated using pilot‐scale experimental results. RESULTS: An efficient reactor arrangement predicted anoxic–anaerobic and aerobic biofilm thicknesses of 150–400 and 70–175 µm in the riser and downer, respectively. Furthermore, distribution of chemical oxygen demand (COD), NH4‐N, NOX‐N, and dissolved oxygen in the biofilm, as well as nutrients removed in the aerobic and anoxic zones, reflect nitrification, denitrification and enhanced biological phosphorus removal in the LSCFB. The model predicted both anoxic effluent and final effluent COD, SCOD, SBOD, NH4‐N, NO3‐N, TKN, TN, PO4‐P, and TP were within the 95% confidence intervals of the experimental data. Model‐predicted simultaneous nitrification/denitrification occurring in the aerobic downer. CONCLUSION: This model developed for LSCFB using the AQUIFAS biofilm diffusion model successfully evaluated the process performance. It is an efficient tool for further research, design, and optimization of the fixed film bioreactor. Copyright © 2010 Society of Chemical Industry
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".