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Record W2802678016 · doi:10.2166/wst.2018.175

Dynamic characterization of a FeCl3-dosed anaerobic membrane bioreactor (AnMBR) treating municipal wastewater

2018· article· en· W2802678016 on OpenAlexaff
Qirong Dong, Wayne J. Parker, Martha Dagnew

Bibliographic record

VenueWater Science & Technology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Waterloo
FundersSolar Energy Technologies Office
KeywordsWastewaterBioreactorAnaerobic exerciseMembrane bioreactorChemistryPulp and paper industryWaste managementEnvironmental scienceEnvironmental engineeringEngineeringBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A transient study was conducted at pilot scale to assess the impact of Fe dosage on the dynamics of biological and membrane performance of an anaerobic membrane bioreactor (AnMBR) treating authentic municipal wastewater. A transient model of the AnMBR system was employed to assist with interpretation of the observed responses in the mixed liquor under different FeCl3 dosages. A high dosage (43 mg FeCl3/LSewage) resulted in a significant accumulation of fixed suspended solids and volatile suspended solids (VSS) and reduction of colloidal COD in the mixed liquor. The elevated dosages appeared to reduce the biodegradability of VSS that was present in the raw wastewater. Intermediate dosages of FeCl3 (21–12 mg/L) had less effect on these responses and did not appear to affect VSS biodegradation. Membrane performance was significantly affected by FeCl3 dosage as indicated by reversible resistance (RR) and physically irreversible resistance (IR). RR was closely related to the colloidal COD in the mixed liquor, thus responded quickly to Fe dosage. Physically, IR had a delayed response to changes in the colloidal COD concentrations in the mixed liquor and this was attributed to the effect of slow mass transfer of colloidal matter between the mixed liquor and the membrane.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.005
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.005
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.238
Teacher spread0.229 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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