Effect of recycle on treatment of aircraft de-icing fluid in an anaerobic baffled reactors
Bibliographic record
Abstract
Aircraft de-icing fluid at 7 000 mg COD/ℓ was successfully treated in an anaerobic baffled reactor operated with and without recycle at volumetric organic loading rate of between 4 and 11 g COD/ℓreactor·d. Reactor recycle was found to improve reactor performance. The anaerobic baffled reactor operated with a 6:1 recycle ratio achieved a minimum hydraulic retention time of 17 h with an acceptable COD removal efficiency of 93% at a volumetric organic loading rate of 9.9 g COD/ℓreactor·d. This corresponded to a specific organic loading rate of 0.35 g COD/g VSS·d and specific organic removal rate of 0.32 g CODrem/g VSS·d. Without recycle similar removal efficiency was achieved; however, the loading rates were about 40% less. Due to biomass growth specific organic loading rate was not found to vary significantly through most of the experimental period despite loading rate increases. Hydrodynamically, an anaerobic baffled reactor may be characterised as an in-series continuously stirred tank reactor where the number of continuously stirred tank reactors corresponded to the number of actual compartments. Volatile fatty acid profiles tend to indicate that anaerobic baffled reactor compartmentalisation served to separate acidogenic and methanogenic activities longitudinally through the reactor, with the highest proportion of acidogenic activity in the first compartments. The net accumulated yield within the anaerobic baffled reactor was found to be of 0.007 g VSS/g CODrem when the ABR was operated without recycle and of 0.016 g VSS/g CODrem for the ABR operated with recycle. Water SA Vol.31 (3) 2005: pp.377-384
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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".