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Record W4383695003 · doi:10.3390/min13070924

Energy Efficiency of Microwave-Induced Heating of Crushed Rocks/Ores

2023· article· en· W4383695003 on OpenAlexafffund
Amin Shadi, Parham Samea, Mahdi Rabiei, Seyed Ali Ghoreishi‐Madiseh

Bibliographic record

VenueMinerals · 2023
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrowaveMaterials scienceAbsorption (acoustics)GrindingHeat transferParticle sizeParticle (ecology)Microwave heatingElectromagnetic radiationYield (engineering)Composite materialMechanicsOpticsChemistryGeology

Abstract

fetched live from OpenAlex

The interaction between electromagnetic waves and heat transfer phenomena due to microwave treatment is of utmost importance for an energy-efficient microwave-integrated grinding circuit. In this study, the effect of microwave irradiations on the heat absorptions of crushed particles is carried out by developing a numerical model. Crushed particles are simulated as diced-shaped geometries with different sitting arrangements but similar size distributions. The energy efficiency of the microwave treatment process is studied by introducing temperature-dependent dielectric properties and accounting for the convective heat loss from the particle boundaries to the surrounding environment. The simulations are quantitatively validated with the experimental results for heat over microwave efficiency. Heat absorption of larger particles is found to be significantly higher, and the arrangement of particles exerts a negligible effect on overall energy absorption. It is also found that ores with a larger average diameter can yield higher energy efficiencies, and the maximum absorption can be achieved by placing the particles at certain distances from the waveguide of the microwave.

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 score0.045
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.244
Teacher spread0.224 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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