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Record W3134509773 · doi:10.1002/cjce.24095

A coupled hydrodynamic‐biokinetic simulation of three‐phase flow in an oxidation ditch using <scp>CFD</scp>

2021· article· en· W3134509773 on OpenAlexvenueno aff
Hossein Norouzi‐Firouz, Mohammad‐Hossein Sarrafzadeh, Reza Zarghami, Iman Moshiri‐Tabrizi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsBaffleAerationOxidation ditchMechanicsActivated sludgeFlow (mathematics)Computational fluid dynamicsVolume of fluid methodChemistryMixing (physics)Phase (matter)Volume fractionVolume (thermodynamics)Flow velocityMaterials scienceEnvironmental scienceEnvironmental engineeringWastewaterThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Coupling the hydrodynamic and biokinetic models, was done on a lab‐scale oxidation ditch as the bioreactor of the activated sludge process and validated against the available experimental data. The simulation was carried out in three‐phase, three‐dimensional conditions with k‐є and Eulerian–Eulerian methods. A simplified activated sludge model No.1 (ASM1) has been added to transport terms to account for the biokinetic model. Rotatory speed for surface aerator was initially increased and its corresponding effects on solid volume fraction, dissolved oxygen, and velocity profile and values were observed. A 150 rpm increase in the rotational velocity resulted in 36% higher average liquid phase velocity for the one‐aerator arrangement, which was 3% more than that of the two‐aerator oxidation ditch. The 150% increase in the solid volume fraction led to a 10% reduction in the maximum liquid phase velocity. The effect of aerators number has been considered explicitly on baffle performance and liquid phase velocity profile. Finally, a new design for the aerator has been offered that provides an ordered flow field and optimal conditions for the performance of the baffle and oxidation process.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.547

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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