MétaCan
Menu
Back to cohort
Record W2286688707 · doi:10.1080/09593330.2015.1135990

Analysis of submerged membrane for a sludge-bed anaerobic membrane bioreactor treating prehydrolysis liquor

2015· article· en· W2286688707 on OpenAlexafffund
Mayur Milan Kale, Kripa Shankar Singh

Bibliographic record

VenueEnvironmental Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of New Brunswick
FundersAtlantic Canada Opportunities AgencyNew Brunswick Innovation Foundation
KeywordsMembrane foulingChemistryFoulingMembranePulp and paper industryMembrane bioreactorMesophileBioreactorChromatographyChemical engineeringOrganic chemistryBacteriaBiochemistry

Abstract

fetched live from OpenAlex

An analysis of foulants and the performance of membranes in innovative sludge-bed anaerobic membrane bioreactors (SB-AnMBRs) were evaluated at mesophilic (35°C for approx. 400 days) followed by thermophilic (55°C for approx. 400 days) temperatures while treating the prehydrolysis liquor (PHL) waste stream from a dissolving pulp production plant. The membrane fouling of SB-AnMBR was analyzed for 0.1, 0.15 and 0.2 m(3)/m(2)/d flux conditions. Physico-chemical analyses of the membrane showed that the combination of 5% citric acid, 0.5% NaOCl and 2% NaOH solutions was effective in achieving more than 80% recovery of membrane flux. Chemical characterization of foulants showed that proteins were more predominant in membrane fouling than carbohydrates. Sugars and lignin contribution were negligible as compared to proteins in the total organic carbon content of the foulant. Membrane fouling occurred through a biofilm-dominated process and organic fouling. Combination of cleaning chemicals which included 0.5% NaClO and 2% NaOH solutions was most effective in the removal of the organic foulants. SEM analysis showed the pictorial evolution of the impact of fouling on the pore openings and the effect of cleaning on the membrane surface.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental TechnologySame topicMembrane Separation TechnologiesFrench-language works237,207