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Record W2397332930 · doi:10.3390/su8050489

Design of Stirrer Impeller with Variable Operational Speed for a Food Waste Homogenizer

2016· article· en· W2397332930 on OpenAlexaff
Idris Kayode, Emmanuel O. Ogedengbe, Marc A. Rosen

Bibliographic record

VenueSustainability · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHomogenizerImpellerMixing (physics)SlurryMechanical engineeringAgitatorProcess engineeringEngineeringRotational speedFood wasteMaterials scienceWaste managementChemistryChromatographyEnvironmental engineeringPhysics

Abstract

fetched live from OpenAlex

A conceptualized impeller called KIA is designed for impact agitation of food waste in a homogenizer. A comparative analysis of the performance of KIA is made with three conventional impeller types, Rushton, Anchor, and Pitched Blade. Solid–liquid mixing of a moisture-rich food waste is simulated under various operational speeds, in order to compare the dispersions and thermal distributions at homogenous slurry conditions. Using SolidWorks, the design of the impellers employs an Application Programming Interface (API) which acts as the canvas for creating a graphical user interface (GUI )for automation of its assembly. A parametric analysis of the homogenizer, at varying operational speeds, enables the estimation of the critical speed of the mixing shaft diameter and the deflection under numerous mixing conditions and impeller configurations. The numerical simulation of the moisture-rich food waste (approximated as a Newtonian carrot–orange soup) is performed with ANSYS CFX v.15.0. The velocity and temperature field distribution of the homogenizer for various impeller rotational speeds are analyzed. It is anticipated that the developed model will help in the selection of a suitable impeller for efficient mixing of food waste in the homogenizer.

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: none
Teacher disagreement score0.665
Threshold uncertainty score0.358

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 head0.204
Teacher spread0.195 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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