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Record W2510684755 · doi:10.1002/cjce.22635

Optimization of bioreactor operating conditions using computational fluid dynamics techniques

2016· article· en· W2510684755 on OpenAlexvenueno aff
Daniela M. Koerich, Leonardo Machado da Rosa

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulPetrobras
KeywordsTurbulenceDragBioreactorVolumetric flow rateMechanicsFluentBiomass (ecology)Computational fluid dynamicsShear stressFlow (mathematics)Environmental scienceMaterials scienceTurbulence kinetic energyMixing (physics)Nuclear engineeringEngineeringChemistryPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract High hydraulic retention times are commonly used in bioreactors, as they provide greater contact time between substrate and biomass. These reactors also operate at low flow rates to minimize damage to the biomass due to shear stress and drag. However, a high flow rate provides greater mixing, and thus the optimal operation condition is difficult to estimate. In this study, the effect of different flow rates on the behaviour of the phases in an anaerobic sequencing batch bioreactor is evaluated numerically. Fluent 15 code was used, considering the two‐phase, transient, and turbulent flow in a three‐dimensional mesh. The turbulence, the drag of the biomass flocs to the recirculation system, and the shear stress in the remaining solids were evaluated. For the reactor evaluated in this study, the flow rate of 189.4 g/s was found to be the most suitable, not only for the operation of the reactor, but also to provide energy savings in the operation of the recirculation pump.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.263

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 integrity0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 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

Citations18
Published2016
Admission routes1
Has abstractyes

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