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Record W2319236058 · doi:10.1021/ie3030467

Sensitivity of Carbon Anode Baking Model Outputs to Kinetic Parameters Describing Pitch Pyrolysis

2013· article· en· W2319236058 on OpenAlexaff
François Grégoire, Louis Gosselin, Houshang Alamdari

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAnodePyrolysisKinetic energyMaterials scienceCarbon fibersRaw materialAluminiumPorosityWork (physics)Chemical engineeringComposite materialThermodynamicsChemistryElectrodeOrganic chemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Carbon anode blocks, used in aluminum electrolysis cells, are usually baked in furnaces for several days, during which they release volatiles due to pitch pyrolysis. Therefore, numerical modeling of anode baking furnaces has to include some representation of pitch pyrolysis via a set of kinetic parameters. These kinetic parameters can vary with raw materials and baking parameters and are tedious to determine experimentally. In this work, we studied how the main outputs of an anode baking model are affected by the variance of the kinetic parameters. Results show that certain model outputs are not considerably influenced by changes in the kinetic parameters (e.g., spatial variation of anode porosity, maximum heating value from volatiles), while others are significantly affected (e.g., time evolution of anode porosity, time of maximum heating value of volatiles, internal pressure of anode), in particular by activation energy variability.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.106
GPT teacher head0.290
Teacher spread0.183 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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