Multi-component kinetics of activated sludge treatment of bleached kraft mill effluent
Bibliographic record
Abstract
This study investigated the discrepancies between the BOD removal rates measured during short term assays and those measured during continuous activated sludge treatment of bleached kraft mill effluent (BKME). A combination of batch tests and fed batch tests with oxygen uptake rate (OUR), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and mixed liquor volatile suspended solids (MLVSS) measurements were used to characterize the degradation rates for the activated sludge treatment of BKME and to divide the soluble readily biodegradable substrate into two to five separate fractions based on biodegradation rates. The removal rates varied by over an order of magnitude between the most readily degradable substrates (1 x 10(-3) mg COD/mg MLVSS minute), and the more slowly degradable substrates (2 x 10(-5) mg COD/mg MLVSS minute). If the readily biodegradable fraction of BKME was modeled as one substrate, initial rate kinetic measurements from batch tests were heavily influenced by the fractions with the greatest degradation rates, while any remaining BOD in the treated effluent was predominantly from the slowly degradable fraction, giving inconsistent results. Taking the multi-component nature of the wastewater into account, batch test results can be used to predict fed-batch and continuous activated sludge reactor performance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".