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Optimization of an External Nanofiltration Anaerobic Membrane Bioreactor Treating a High-Strength Starch-Based Wastewater

2018· article· en· W2791412879 on OpenAlexaff
Joshua Snowdon, Kripa S. Singh, Gustavo Zanatta

Bibliographic record

VenueJournal of Environmental Engineering · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsWastewaterChemical oxygen demandMembrane reactorMembraneChemistryBioreactorNanofiltrationChemical engineeringPulp and paper industryChromatographyMembrane bioreactorContinuous stirred-tank reactorStarchMaterials scienceEnvironmental engineeringEnvironmental scienceOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Membrane and reactor performance for an anaerobic membrane bioreactor (AnMBR) treating a high-strength starch-based wastewater [average chemical oxygen demand (COD) of 84.8±8.9 g/L] was examined under six varied cleaning protocols to find optimal operating conditions. The AnMBR was composed of a 1,000-L anaerobic continuous stirred tank reactor (CSTR) integrated with an external, tubular nanofiltration membrane module operated in cross-flow mode. The polyvinylidene fluoride–based membrane module had a nominal pore size of 30 nm, a total membrane area of 0.42 m2, and a cross-flow velocity of 2.5 m/s. The different cleaning protocols were provided by varying the membrane’s permeate backwash duration, frequency, and flow rate and by incorporating chemically enhanced backwash cycles with the use of a mixed 250-ppm sodium hydroxide and 250-ppm hypochlorite solution. Reactor performance did not appear to be cleaning protocol dependent; however, a decline in reactor-specific methanogenic activity (SMA) was noticed over the duration of the study. A single, longer-duration chemically enhanced backwash offered the highest improvement in membrane performance over all other cleaning protocols.

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 categoriesInsufficient payload (model declined to judge)
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 score0.206
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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
Published2018
Admission routes1
Has abstractyes

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