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Record W2773083675 · doi:10.2166/wpt.2017.096

Demonstration of innovative MABR low-energy nutrient removal technology at Chicago MWRD

2017· article· en· W2773083675 on OpenAlex
Jeff Peeters, Nick Adams, Zebo Long, Pierre Côté, Thomas E. Kunetz

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Practice & Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsCentre For Cold Ocean Resources EngineeringSuez (Canada)
Fundersnot available
KeywordsNitrificationEnvironmental engineeringEffluentEnvironmental scienceAmmoniaAerationWaste managementPulp and paper industryChemistryNitrogenEngineering

Abstract

fetched live from OpenAlex

Abstract An innovative MABR (membrane-aerated biofilm reactor) membrane technology was demonstrated at the O'Brien Water Reclamation Plant (OWRP) of the Metropolitan Water Reclamation District of Greater Chicago (Chicago MWRD). The demonstration unit was equipped with one full-scale membrane cassette. The technology was evaluated for its potential to improve performance for total suspended solids (TSS) and ammonia removal during stressed conditions (specifically cold temperature peak flow periods) and to meet future effluent phosphorous limits. Over a period of 9 months, the MABR oxygen transfer rate was stable and ranged between 8 and 12 g-O2/d/m2 of membrane surface area. The nitrification rate varied between 0.5 and 2.5 g-N/d/m2 and was affected primarily by the ammonia loading rate and the feed carbon to nitrogen ratio. Most of the oxygen transferred was accounted for by nitrification. The MABR hybrid process enables important process improvements while reducing plant energy consumption.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.009
GPT teacher head0.240
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it