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Record W1201483785 · doi:10.2166/wrd.2015.058

Anaerobic membrane bioreactor for high-strength wastewater treatment: batch and continuous operation comparison

2015· article· en· W1201483785 on OpenAlexaff
D. Hufnagel, Sheng Chang, Yoon‐Ki Hong, Pan Wu, Richard G. Zytner

Bibliographic record

VenueJournal of Water Reuse and Desalination · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsBioreactorWastewaterEffluentFiltration (mathematics)Chemical oxygen demandMembrane foulingChemistryPulp and paper industryMembraneAnaerobic exerciseMembrane bioreactorChromatographySewage treatmentFoulingHydraulic retention timeMembrane reactorEnvironmental engineeringEnvironmental scienceBiologyBiochemistryEngineeringMathematics

Abstract

fetched live from OpenAlex

The anaerobic membrane bioreactor (AnMBR) is a recent development in high-rate anaerobic bioreactors. This study assessed the treatment of high-strength wastewater by an AnMBR using batch and continuous feeding operation. The results showed that the AnMBR could establish a biomass concentration of 6–8 g/L in approximately 20 days due to retention of micro-organisms by the membrane, resulting in 86% chemical oxygen demand (COD) removal efficiency in the treatment of high-strength brewery wastewater. Batch operation was proven to be effective for an organic loading rate (OLR) up to 2 gCOD/L/day and was beneficial to the membrane filtration. However, the treatment capacity of the AnMBR with batch feeding was limited by the high instantaneous OLR during the feeding period. Compared to batch operation, continuous feeding can achieve improved stability and better effluent quality, but prolonged continuous permeation may make the membrane more susceptible to fouling. Although a critical flux of 22 L/m2/h was determined for the membrane filtration in the AnMBR tested, a decrease in the membrane permeability was still observed in the long-term filtration at a flux of approximately 10 L/m2/h.

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 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 score0.039
Threshold uncertainty score0.263

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)0.0000.000

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 head0.028
GPT teacher head0.268
Teacher spread0.239 · 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.

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

Citations9
Published2015
Admission routes1
Has abstractyes

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