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Record W2154700894 · doi:10.1002/bit.24473

A model for cellulase production from <i>Trichoderma reesei</i> in an airlift reactor

2012· article· en· W2154700894 on OpenAlexaff
Rachid Bannari, Abdelfettah Bannari, Patrick Vermette, Pierre Proulx

Bibliographic record

VenueBiotechnology and Bioengineering · 2012
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTrichoderma reeseiCellulaseCelluloseBiomass (ecology)FermentationLactoseFluid dynamicsMass transferAirliftChemistryBioreactorPopulationComputational fluid dynamicsMechanicsChemical engineeringChromatographyFood sciencePhysicsBiologyBiochemistryEngineeringEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

A mathematical model for cellulase production by Trichoderma reesei RUT-C30 grown in a cellulose medium with lactose as fed batch in an airlift reactor is proposed. To describe adequately the mass transfer between the air bubbles and the broth, it uses computational fluid dynamics (CFD) including multiphase Eulerian-Eulerian formulation, with a detailed description of the bubble size distribution through the use of the population balance model (PBM) and the class method (CM). The kinetics of the biomass growth is further coupled to the fluid flow conditions using partial differential equations for all the species involved, providing detailed information of important reactor conditions such as the distribution of oxygen, cellulose, and the shear stress within the reactor over the entire period of fermentation. Predicted results agree well with the available overall measurements for a typical fed-batch operation and detailed profiles of the predicted concentration fields are discussed from an engineering viewpoint.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.214
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations21
Published2012
Admission routes1
Has abstractyes

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