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Record W2063679576 · doi:10.2202/1542-6580.1642

Optimization of Continuously Stirred Tank Bioreactor Design for Cost Minimization: Effect of Microbial Species and Operating Conditions

2008· article· en· W2063679576 on OpenAlexaff
Erin E Powell, Gordon A. Hill

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAerationImpellerBioreactorOperating costProcess engineeringBiomass (ecology)Mixing (physics)Continuous stirred-tank reactorMass transferEnvironmental scienceEnvironmental engineeringEngineeringWaste managementMechanical engineeringChemical engineeringEcologyChemistry

Abstract

fetched live from OpenAlex

The operation of continuously stirred tank bioreactors (CSTBs) at minimum cost is a major concern of operators. In this work, a CSTB design strategy is presented where impeller stirring speed and aeration rate are optimized to meet the oxygen demand of growing cells, simultaneously minimizing the capital and operating cost. The operating cost is limited to the cost of utilities. The optimization scheme assumes a given fermentor tank size, and that the properties of the culture medium and the oxygen respiratory requirements of the microorganisms being cultivated are known. It is possible to choose between two different turbine impellers during the design process. The equations, constraints, and the CSTB design strategy employed by the program are described. The effect of microbial species, ions in the culture medium, impeller style, as well as changing CSTB size and biomass input density on the optimum operating conditions, is examined. The mass transfer coefficient, gas holdup, mixing speed, and aeration rate are all reported at optimized cost conditions. A study of the effects of various parameters on the CSTB design are shown.

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 score0.308
Threshold uncertainty score0.502

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

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