Use of anaerobic baffled reactors (ABR) operated with and without recycle for treatment of aircraft deicing fluid (ADF)
Bibliographic record
Abstract
Deicing fluid, which is used to prevent ice formation and to remove ice from aircraft, is used in large quantities in Canada every winter. It has been reported that the application of aircraft deicing fluid (ADF) to planes can result in as much as 96% of the total glycol used being lost in the runoff. Since glycol has a very high chemical oxygen demand (COD), the resulting runoff will exert a high COD regardless of dilution. For this reason, it is desirable to treat the runoff before discharging it to a body of water. Successful treatment of ADF has already been achieved using Upflow Anaerobic Sludge Blanket (UASB) reactors, yet the treatment has been limited by the maximum flowrate attainable before substantial washout of biomass occurs. The particular flow characteristics within Anaerobic Baffled Reactor (ABR) which lead to long solids retention times (SRT) have been found, in the current study, to overcome the SRT limitations and have resulted in biomass accumulation which would require biomass wastage to maintain constant biomass concentration within ABR operated without recycle or with a 6:1 recycle ratio. (Abstract shortened by UMI.)
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".