Centrate treatment to produce a nitrifying biomass for bioaugmentation
Bibliographic record
Abstract
The City of Winnipeg is currently conducting studies to minimize expansion costs for wastewater treatment when upgrading to include nitrification.One of the methods considered is centrate freatment.This study examined treatment of centrate by nitrification in a dedicated reactor.The biomass produced was used as seed for bioaugmentation of cold reactors (10'C) treating synthetic wastewater without nitrification.As a result of seeding, nitrification was initiated in the seeded reactors.The degree to which effluent ammonia nitrogen (NFL-N) was reduced depended on the seed dose and the temperature to which the seed was acclimated.Seed acclimated to warmer temperafures experienced decreases in nitrification rates after suddenJy cooling to 10oC.Based on the results of the seeding, simulation modeling was conducted using BioWin to predict the benefits of seeding nitrifiers into treatment systems with different hydraulic and soiids retention times.It was found that, when compared with conventional nitrification systems, producing seed by centrate nitrification could decrease the volume requirements by up to20%.Microbial analysis using fluorescence in situ hybridization (FISH) of ammonia oxidizing bacteria showed that the seed was being washed out of the seeded systems i4advertently with the effluent.This observation explained why poor Nru-N removal was achieved when seed was added to SBRs with short hydraulic retention times.The FISH signal associated with ammonia oxidizers correlated well with effluent NHs-N and nitrate-nitrogen (NOa-N) concentrations and the nifrification rate.Cenfrate was found to be a suitable substrate for the production and harvest of nitrifying seed.Seed produced at the same temperature as the reactor into which it is to be added provided the greatest benefit.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".