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Record W2419019456 · doi:10.1002/er.3508

Energy and exergy inventory in aluminum smelter from a thermal integration point-of-view

2016· article· en· W2419019456 on OpenAlexafffund
Ruijie Zhao, Cassandre Nowicki, Louis Gosselin, Carl Duchesne

Bibliographic record

VenueInternational Journal of Energy Research · 2016
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsUniversité Laval
FundersFonds de Recherche du Québec - Santé
KeywordsExergySmeltingWaste managementWork (physics)Environmental scienceExergy efficiencyWaste heatWaste heat recovery unitAluminium smeltingEnvironmental engineeringEngineeringMetallurgyMechanical engineeringMaterials scienceHeat exchanger

Abstract

fetched live from OpenAlex

Primary aluminum production is a very energy-intensive industry (~13 MWh per ton of aluminum produced), involving several complex processes within a plant. The paper presents a mass, energy and exergy analysis of an entire smelter. This work was motivated by the need to evaluate the potential for waste heat recovery/thermal integration in such a plant. Three of the main sectors of a smelter are studied, namely the carbon anode production, the electrolytic reduction and the casting. The analysis is applied to a typical smelter producing 260 000 MTAl per year. It was found that the most important waste heat source is the exhaust gases, with an exergy of 0.57 MWh per ton of aluminum produced. The total exergy destroyed in the plant was found to be around 7.7 MWh/MTAl. The potential for doing useful work associated with heat dissipated at process boundaries is also evaluated. Results highlight sources of thermodynamic inefficiencies and indicate where exergy is mainly destroyed throughout the plant. The results contribute in developing a better understanding of the energy and exergy flow within a smelter. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.307
Teacher spread0.273 · 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 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

Citations19
Published2016
Admission routes2
Has abstractyes

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