From Seasonal to Year-round Nitrification: Performance Evaluation of North America's Largest MABR Installation
Bibliographic record
Abstract
From Seasonal to Year-round Nitrification: Performance Evaluation of North America's Largest MABR InstallationAbstractThis study evaluated the performance of North America’s largest MABR installation under different operational and seasonal conditions. Total nitrogen (TN) removal improved from 40- 50% when the plant was in a conventional activated sludge configuration to 75-85% after the MABR upgrade. The MABR reduced effluent ammonia during winters and nitrate concentrations during summer to below 10 mg-N/L in both the cases. The nitrification rate in the MABR varied between 0.7-2.2 gNm-2 d -1 with lower nitrification rates observed during reduced airflow operation and winter conditions. The development of thicker biofilms limited ammonia diffusion into the biofilm that resulted in reduced nitrification rate. Increasing the flow of sparging air was able to control the biofilm thickness to improve nitrification. The denitrification rate in the MABR process was between 100-200 gNm-3 d -1 which followed a close trend with nitrification rate. The nitrification rate in the MABR was found to be the rate limiting step for TN removal in the process.Addition of an MABR to an existing conventional activated sludge process substantially improved TN removal and reduces seasonal spikes in effluent ammonia. The improved TN removal in the plant was due to elevated levels of simultaneous nitrification and denitrification in the hybrid MABR process.SpeakerLakshminarasimman, NarasimmanPresentation time15:30:0015:50:00Session time15:30:0017:00:00SessionAlternative Approaches to Intensify Secondary TreatmentSession locationRoom S404a - Level 4TopicIntermediate Level, Municipal Wastewater Treatment Design, NutrientsTopicIntermediate Level, Municipal Wastewater Treatment Design, NutrientsAuthor(s)Lakshminarasimman, NarasimmanAuthor(s)N. Lakshminarasimman 1; D. Celmer-Repin 2 ; J. Mank 3; J. Gagnon 4; M. Reeve 5; J. Peeters 6; W.J. Parker 1; N. Lakshminarasimman 1;Author affiliation(s)Department of Civil and Environmental Engineering, University of Waterloo, ON, Canada 1; Region of Waterloo, ON, Canada 2 ; Ontario Clean Water Agency, ON, Canada. 3; Veolia Water Technologies 4; Veolia Water Technologies 5; Veolia Water Technologies 6; Department of Civil and Environmental Engineering, University of Waterloo, ON, Canada 1; Department of Civil and Environmental Engineering, University of Waterloo, ON, Canada 1;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159017Volume / Issue Content sourceWEFTECCopyright2023Word count14
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".