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Record W2044201206 · doi:10.1002/jctb.1096

Impact of chemical oxidation on sludge properties and membrane flux in membrane separation bioreactors

2004· article· en· W2044201206 on OpenAlexafffund
BQ Liao, LJJ Catalan, IG Droppo, SN Liss

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsToronto Metropolitan UniversityEnvironment and Climate Change CanadaLakehead University
FundersLakehead University
KeywordsUltrafiltration (renal)ChemistryMembraneHypochloriteSodium hypochloriteChemical oxygen demandMembrane reactorChlorineChemical engineeringBioreactorChromatographyPulp and paper industrySewage treatmentEnvironmental engineeringInorganic chemistryOrganic chemistryBiochemistryEnvironmental science

Abstract

fetched live from OpenAlex

Abstract The effects of sodium hypochlorite (NaOCl) on sludge properties and membrane permeate flux were studied using a pressurized stirred ultrafiltration cell. Oxidation with NaOCl resulted in sludge solubilization and decreased sludge floc sizes. The sludge dissolution constant was estimated at 0.3 ± 0.1 mg sludge mg −1 free chlorine under testing conditions. The increases in soluble chemical oxygen demand and total carbohydrate concentration in centrifuged supernatant were less than proportional to chemical dosage. Membrane permeate fluxes were much lower as a result of oxidation. Soluble biopolymers accounted for more than 76% of the total hydraulic resistance during ultrafiltration of oxidized sludge. By contrast, both the settleable sludge flocs and the soluble biopolymers were important contributors to the hydraulic resistance of sludge before oxidation. Given the benefits of NaOCl solution for membrane cleaning and its negative impact on sludge properties, the quantity of NaOCl solution used for maintenance cleaning of membranes should be optimized. Copyright © 2004 Society of Chemical Industry

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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

Citations7
Published2004
Admission routes2
Has abstractyes

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