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Record W2022862294 · doi:10.1002/ep.10547

Combined MBBR‐MF for industrial wastewater treatment

2011· article· en· W2022862294 on OpenAlexaff
Atehna Pervissian, Wayne J. Parker, Raymond L. Legge

Bibliographic record

VenueEnvironmental Progress & Sustainable Energy · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAlumChemistryFerricChlorideEffluentMoving bed biofilm reactorCoagulationFoulingMembrane foulingWastewaterPulp and paper industryFiltration (mathematics)ChromatographyUltrafiltration (renal)MembraneNuclear chemistryEnvironmental engineeringInorganic chemistryBiofilmEnvironmental scienceOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract An assessment of the performance of a combined moving bed biofilm reactor and a membrane filtration (MBBR‐MF) system for treatment of a wastewater from a potato chip factory was performed. Pretreatment of membrane feed by coagulation with alum, ferric chloride, and a blend of polyaluminum chloride and polyamine was investigated for improving membrane performance. The effect of coagulation on membrane fouling was found to strongly depend on the type and dosage of the coagulant and the MBBR effluent characteristics. Ferric chloride performed the best as a pretreatment coagulant compared with alum and the coagulant blend. It reduced total fouling by 79% and increased consistency as compared with the other coagulants. Alum and the blend were, on average, 30% and 8%, less effective than ferric chloride in reducing total fouling. © 2011 American Institute of Chemical Engineers Environ Prog, 2011

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.218
Teacher spread0.194 · 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 teacher head, not a consensus.

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

Citations12
Published2011
Admission routes1
Has abstractyes

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