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Pilot Scale Testing of A New Configuration of The Membrane Aerated Biofilm Reactor (MABR) to Treat High‐Strength Industrial Sewage

2010· article· en· W2442589812 on OpenAlexafffund
Anne‐Emmanuelle Stricker, Heather Lossing, John Gibson, Youngseck Hong, Jane Challen Urbanic

Bibliographic record

VenueWater Environment Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnvironment and Climate Change Canada
FundersSustainable Development Technology Canada
KeywordsAerationWastewaterNitrificationBiofilmChemical engineeringChemistrySpargingSewage treatmentAir strippingEnvironmental engineeringMaterials sciencePulp and paper industryEnvironmental scienceNitrogenOrganic chemistry

Abstract

fetched live from OpenAlex

An alternative design of the membrane aerated biofilm reactor (MABR) was developed to overcome some of the current technical and economical limitations preventing full scale applications of the process. The ZeeLung system uses a new dense hollow-fibre membrane with an unprecedented thin diameter. Two pilot units treating a synthetic high-strength industrial wastewater (4700 mgCOD/L, 145 mgTKN/L) operated successfully for 16 months. They performed simultaneous COD removal, nitrification and denitrification. The very high specific surface area (810 m2/m3) allowed the surface loading rate to be kept low enough (3.6 gCOD/(m2.d)) to maintain a relatively thin biofilm (200 to 350 microm) and use low-pressure air (41 kPa) instead of high-pressure pure oxygen. Intermittent air and liquid mixing at high frequency and low shear were compared: they were equally effective in enhancing substrate transfer, but failed to stabilize biofilm accumulation. Air sparging additionally prevented the acidification of the bulk by stripping CO2.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.068
GPT teacher head0.271
Teacher spread0.203 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2010
Admission routes2
Has abstractyes

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