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Record W2016136604 · doi:10.3390/su6095554

Mixing Performance of a Suspended Stirrer for Homogenizing Biodegradable Food Waste from Eatery Centers

2014· article· en· W2016136604 on OpenAlexaff
Olumide Babarinsa, Emmanuel O. Ogedengbe, Marc A. Rosen

Bibliographic record

VenueSustainability · 2014
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHomogenizerSlurryMixing (physics)Food wasteMechanicsMaterials scienceBinConservation of massEnvironmental scienceWaste managementEnvironmental engineeringMechanical engineeringChemistryEngineeringPhysicsChromatography

Abstract

fetched live from OpenAlex

Numerical simulation of a suspended stirrer within a homogenizing system is performed towards determining the mixing performance of a homogenizer. A two-dimensional finite volume formulation is developed for the cylindrical system that is used for the storage and stirring of biodegradable food waste from eatery centers. The numerical solver incorporates an analysis of the property distribution for viscous food waste in a storage tank, while coupling the impact of mixing on the slurry fluid. Partial differential equations, which describe the conservation of mass, momentum and energy, are applied. The simulation covers the mixing and heating cycles of the slurry. Using carrot-orange soup as the operating fluid (and its thermofluid properties) and assuming constant density and temperature-dependent viscosity, the velocity and temperature field distribution under the influence of the mixing source term are analyzed. A parametric assessment of the velocity and temperature fields is performed, and the results are expected to play a significant role in designing a homogenizer for biodegradable food waste.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.193
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2014
Admission routes1
Has abstractyes

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