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Casthouse Ventilation Design for the Production of Air-Cooled Aluminium Sows

2010· article· en· W2490820986 on OpenAlexaffabout
Tom Plikas, Tony Cesta, Lowy Gunnewiek, Jean Vanasse

Bibliographic record

VenueInternational Journal of Ventilation · 2010
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsAluminerie Alouette (Canada)Hatch (Canada)
Fundersnot available
KeywordsAluminiumVentilation (architecture)Production (economics)Environmental scienceEngineeringMechanical engineeringMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

AbstractThe Aluminerie Alouette Inc. (AAI) smelter in northern Quebec, Canada recently completed a major plant expansion that includes a new casthouse for the continuous production of low-profile, air-cooled aluminium sows. The radiation and convection heat release of 15 MW to the workplace from the aluminium metal solidification and cooling is significantly higher than that experienced in the traditional water-cooled casting process where the majority of the heat is removed by the cooling water. The design of the casthouse ventilation system presented many challenges for achieving acceptable workplace temperatures, and limiting worker exposure to heat stress and hydrogen fluoride (HF) gas for both summer and winter conditions. Computational Fluid Dynamics (CFD) modelling was used extensively to guide the design of the building ventilation system. The casthouse has been operating successfully since start-up in 2005. Field measurements verify that the CFD modelling closely predicts the workplace temperatures throughout the facility. The simple ventilation control strategy is fully automated and provides acceptable temperature levels in the casthouse. There are no periods throughout the year where it is excessively hot or cold. HF concentration levels are well below the threshold levels.Key words:: Industrial ventilationMolten metalaluminiumcastingnatural convectionradiationprocess heat recoveryCFDMeasurementsValidationnumerical simulation

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)0.001
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 score0.511
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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

Citations2
Published2010
Admission routes2
Has abstractyes

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