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Record W2323332686 · doi:10.1021/ie101171j

New Impeller for Viscous Fermentation: Power Input and Mass Transfer Coefficient Correlations

2011· article· en· W2323332686 on OpenAlexaff
Yun Lin, Zisheng Zhang, Jules Thibault

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsImpellerReciprocating motionMass transfer coefficientMixing (physics)Mass transferCarboxymethyl celluloseMechanicsMaterials scienceBioreactorRotational speedAgitatorSlip factorVolume (thermodynamics)ChemistryMechanical engineeringChromatographyPhysicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

To mix viscous fermentation broths more efficiently, a new close-clearance impeller, the rotational reciprocating plate impeller (RRPI), was designed to partly mimic the axial reciprocating plate impeller (ARPI). Akin to the axial movement of a reciprocating plate impeller, this new RRPI rotates back and forth driven by a three-arm linkage system. Its power consumption and overall mass transfer coefficient ( K L a ) were determined in a 22 L laboratory scale bioreactor using model fluids of different viscosities. Results obtained with this new impeller were compared to those of a triple Rushton impeller (TRI) and an ARPI. In water, results were the same as those of the TRI. However, the ARPI gave significantly lower K L a than the other two impellers for the same power input per unit volume. In highly viscous non-Newtonian carboxymethyl cellulose (CMC) solutions, the new impeller showed similar performance as the ARPI, whereas the TRI was not able to provide acceptable mixing due to the presence of stagnant zones. Therefore, this new impeller proves to be a suitable alternative to existing impellers for use in fermentations having rheologically evolving broth.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.759

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.285
Teacher spread0.217 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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