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Record W2047063048 · doi:10.1002/jctb.1455

Performance analysis of an integrated tangential microfilter‐fermenter

2006· article· en· W2047063048 on OpenAlexaff
Ghinwa Naja, Bohumil Volesky, André Schnell

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBioreactorFiltration (mathematics)Industrial fermentationVolumetric flow rateRotational speedChromatographySaccharomyces cerevisiaeMaterials scienceFlux (metallurgy)MembraneChemistryYeastFermentationChemical engineeringMechanicsMathematicsBiochemistryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A specially designed bioreactor including an axial microfilter for cell retention was evaluated for continuous‐flow operation with selected liquid media as controls and in aerobic cultivations of Saccharomyces yeasts. In the initial tests, performance characteristics such as filtration rates and cell accumulation were assessed as a function of filter rotational speed, operating pressure, cultivation time and microfilter type (i.e. membrane or porous metal). The bioreactor did not perform satisfactorily when viscous extracellular polymer was present in the liquid. In the continuous‐flow culture enabling cell retention, Saccharomyces cerevisiae yeast cell concentrations were enhanced by as much as 16‐fold over ordinary batch growth. Concomitant filtration rates were stable over operating times of up to 130 h and hence were independent of the cell concentration. The maximum steady‐state flux was enhanced at rotational speeds up to 400‐700 rpm ranging from 22 to 42 L m −2 h −1 . Higher rotation rates offered no further improvements. The maximum stabilized flux was independent of operating pressure. Pressure increases caused momentary flux improvements, which rapidly declined and eventually restabilized. Copyright © 2006 Society of Chemical Industry

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 score0.026
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.226
Teacher spread0.221 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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