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Record W3094910285 · doi:10.1002/cjce.23919

Experimental study on aeration efficiency in a pilot‐scale decelerated oxidation ditch equipped with fine bubble diffusers and impellers

2020· article· en· W3094910285 on OpenAlexvenueno aff
Xiaofei Xu, Wenze Wei, Fengxia Liu, Wei Wei, Zhijun Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAerationBubbleMass transfer coefficientMass transferAqueous solutionMaterials scienceTap waterFroude numberImpellerMechanicsChemistryEnvironmental engineeringEnvironmental scienceChromatographyFlow (mathematics)Physics

Abstract

fetched live from OpenAlex

Abstract This work is an experimental study on the oxygen transfer capability and efficiency in a pilot‐scale decelerated oxidation ditch equipped with fine bubble diffusers and impellers (inducing horizontal liquid flows). Aqueous solutions of carboxymethyl cellulose (CMC) exhibiting shear‐thinning rheological properties are selected to simulate activated sludge in the oxidation ditch process. The effect of air flow rate and horizontal liquid velocity along the loop channel on oxygen transfer capability and aeration efficiency is examined in tap water and CMC aqueous solutions. The standard volumetric mass transfer coefficient K L a 20 , standard oxygen transfer efficiency (SOTE), and standard aeration efficiency (SAE) are introduced to study the oxygen mass transfer performance and energy consumption efficiency in the oxidation ditch. The results show that K L a 20 , SOTE, and SAE in CMC aqueous solutions follow similar trends observed in tap water. Due to the effect of shear‐thinning rheological properties, K L a 20 , SOTE, and SAE in CMC aqueous solutions are smaller than those in tap water. Two dimensionless numbers, Froude number and Ohnesorge number are introduced to study the oxygen transfer efficiency through the consideration of the combined influence of aeration, crossflow, gas bubble size, and physical properties of the liquid phase.

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 score0.436
Threshold uncertainty score0.366

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 head0.184
Teacher spread0.175 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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