Doing More with Less: Refining the Westbank Biological Nutrient Removal Process to Achieve Aerobic Granular Sludge While Enhancing Performance
Bibliographic record
Abstract
Doing More with Less: Refining the Westbank Biological Nutrient Removal Process to Achieve Aerobic Granular Sludge While Enhancing PerformanceAbstractAECOM partnered with the City of Penticton (Canada) to investigate various process modifications to its existing biological nutrient removal (BNR) process to promote the growth and retention of granular sludge. The continuous flow AGS demonstration pilot utilizes two completely independent trains, operated side-by-side. The pilot train configuration was modified to include an inDENSE hydrocyclone skid to selectively waste floccular biomass. The pilot train transitioned to a hybrid-floccular sludge, achieving an average SVI-30 of 70 -75 mL/g. Based on the particle size distribution analysis the average particle size in the pilot train was 210 µm vs 86 µm in the control train. Assuming the threshold size for granules is 200 µm, the granules composed approximately 12% in the control train vs 44% in the pilot train. The pilot also indicated that operating the last aerobic cells at low DO to maximize SND significantly reduced effluent TN by reducing nitrate concentration.This paper was presented at WEFTEC 2023 in Chicago, IL.SpeakerGalvagno, GiampieroPresentation time13:30:0013:50:00Session time13:30:0015:00:00SessionApplying Hydrocyclones for DensificationSession locationRoom S501a - Level 5TopicFacility Operations and Maintenance, Intermediate Level, Municipal Wastewater Treatment Design, Nutrients, Research and InnovationTopicFacility Operations and Maintenance, Intermediate Level, Municipal Wastewater Treatment Design, Nutrients, Research and InnovationAuthor(s)S. MurthyAuthor(s)S. Murthy <sup>1</sup>; S. Murthy <sup>1 </sup>; G. Galvagno <sup>2</sup>; J. Acio <sup>2</sup>; M. Kowalski <sup>3</sup>; S. Henderson <sup>4</sup>; G. Marsden <sup>5</sup>; J. Mertz <sup>5</sup>; P. Sampara <sup>6</sup>; K. Sears <sup>7</sup>; B.M. Stinson <sup>8</sup>; R.M. Ziels <sup>9</sup>; M. Kowalski <sup>3</sup>;Author affiliation(s)NEWhub Water Corporation <sup>1</sup>; NEWhub Water Corporation <sup>1 </sup>; AECOM Canada, Kelowna, BC <sup>2</sup>; AECOM <sup>2</sup>; AECOM <sup>3</sup>; City of Penticton <sup>4</sup>; City of Penticton <sup>5</sup>; City of Penticton <sup>5</sup>; University of British Columbia <sup>6</sup>; AECOM <sup>7</sup>; AECOM <sup>8</sup>; University of British Columbia <sup>9</sup>; AECOM <sup>3</sup>;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159207Volume / Issue Content sourceWEFTECCopyright2023Word count20
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".