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Record W2162617397 · doi:10.1002/ep.12180

Effect of pretreatment using ultrasound and hydrogen peroxide on digestion of waste activated sludge in an anaerobic membrane bioreactor

2015· article· en· W2162617397 on OpenAlexaff
Priyanka Joshi, Wayne J. Parker

Bibliographic record

VenueEnvironmental Progress & Sustainable Energy · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of WaterlooAlberta Health Services
Fundersnot available
KeywordsBioreactorChemistryMembrane foulingHydrogen peroxideActivated sludgeAnaerobic digestionSpargingSonicationMembrane bioreactorAnaerobic exercisePulp and paper industryMembrane reactorBiodegradationFoulingWaste managementChromatographyMembraneChemical oxygen demandWastewaterBiochemistryMethaneOrganic chemistryBiology

Abstract

fetched live from OpenAlex

This study investigated combining pretreatment of waste activated sludge with anaerobic digestion in a submerged anaerobic membrane bioreactor. Preliminary tests revealed that a hydrogen peroxide and ultrasound pretreatment resulted in a chemical oxygen demand solubilization of 40% when a dose of 50 gH 2 O 2 /kgTS and sonication for 60 min were employed. Raw and pretreated waste activated sludge were fed to an anaerobic membrane bioreactor to determine if this increase in solubilization affected the biodegradability of the sludge. It was observed that pretreatment increased the extent to which the sludge was digested and hence it was concluded that the combined action of ultrasound and peroxide increased the biodegradable fraction of the sludge, while reducing the bioreactor and waste stream solids concentration. At a constant flux of 2.75 L/m 2 /hour, the transmembrane pressure and the fouling rate remained low and relatively constant over the course of operation, despite reduced mass flow of solids. The performance of the membrane was attributed to the fouling minimizing protocol that consisted of regular maintenance cleaning, a relaxed mode of operation, and continuous gas sparging. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1724–1730, 2015

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 score0.125
Threshold uncertainty score0.988

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 communication0.0000.001
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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