A simple method to estimate the contribution of biological floc and reactor‐solution to mass transfer of oxygen in activated sludge processes
Bibliographic record
Abstract
In this study, the mass transfer coefficient of biological floc (K(L)a(bf)) was estimated from the mass transfer coefficient of the mixed-liquor (K(L)a(f)) and the reactor-solution (K(L)a(e)). The biological floc resistance (BFR) and reactor-solution resistance (SR) were defined as the reciprocal of K(L)a(bf) and K(L)a(e), respectively, by applying the concept of serial-resistance originally presented in two-film theory (Lewis and Whitman (1924) Ind Eng Chem 16:1215-1220). The specific biological floc resistance (SBFR) was defined as biological floc resistance per unit biomass concentration. The data indicated that an activated sludge process yielding low BFR/MLR and BFR/SR tended to produce higher oxygen transfer efficiency. Surprisingly, the reactor-solution posed the same level of resistance as clean water in all experiments, except in a 5-day SRT, non-nitrifying, completely mixed activated sludge (CMAS) process run. Furthermore, SBFR successfully represented biological floc and showed a positive correlation to sludge volume index (SVI). In addition, SBFR/SR and oxygen transfer efficiency (OTE(f)) followed an exponential relationship for the complete data set. The method of separating the mixed-liquor into biological floc and reactor-solution improved the understanding of oxygen transfer under process conditions, without resorting to intrusive techniques or direct handling of fragile biological floc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".