Recovery of particulate matter from a high-rate moving bed biofilm reactor by high-rate dissolved air flotation
Bibliographic record
Abstract
Abstract High-rate biological wastewater treatment processes for carbon recovery are able to improve the energy balance and carbon footprint of water resource recovery facilities. Combination of a high-rate moving bed biofilm reactor (HR-MBBR) with a rapid flotation (HR-DAF), as a replacement for the ‘A stage’ of the A-B process, can achieve this objective. The main goal of this study was to maximize the capture of biodegradable particulate matter from an HR-MBBR effluent by an HR-DAF. A pilot-scale HR-DAF process was operated downstream of an HR-MBBR treating screened municipal wastewater. The particulate biodegradable matter recovery was evaluated by determining the total suspended solids (TSS) removal efficiency. TSS recovery in experiments without chemicals at low surface loading rates (<15 m/h) and high recycle ratio (>25%) was 94 ± 1%. By using a tannin-based polymer, the solids capture efficiency of the HR-DAF was slightly improved with TSS recovery reaching 96 ± 1% at a high SLR (at least 22 m/h) and low recycle ratio (14%). The anaerobic biodegradability of the tannin tested was determined to be 17%. The HR-DAF process downstream of an HR-MBBR gave a very good particulate matter recovery that offers a promising alternative to the A-B process for carbon recovery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".