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Record W2899942418 · doi:10.3390/ma11112197

Thermodynamic Study of the Corrosion of Refractories by Sodium Carbonate

2018· article· en· W2899942418 on OpenAlexaff
Ying Zhao, Guishi Cheng, Fei Long, Changqing Dong

Bibliographic record

VenueMaterials · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsCorrosionMaterials scienceSodium carbonateGibbs free energyMagnesiumChromiumChemical stabilityAluminiumMetallurgySodiumThermodynamics

Abstract

fetched live from OpenAlex

The corrosion of refractories by sodium salts in waste liquid at high temperature has become a serious problem. This paper focuses on the thermodynamic characterization of sodium carbonate (Na₂CO₃) corrosion of six refractories by FactSage modelling in combination with X-ray diffraction (XRD). Three of the refractories are oxides (Fe₂O₃, Al₂O₃, and Cr₂O₃), and the other three are synthetics spinels (magnesium chromium, MgO·Cr₂O₃; magnesioferrite, MgO·Fe₂O₃; and, magnesium aluminium, MgO·Al₂O₃). First, thermodynamic simulations were carried out with the FactSage thermodynamics model using the reaction package to predict the direction of the Na₂CO₃ corrosion reaction in terms of the Gibbs free energy. Then, the reactions between the six refractories and Na₂CO₃ were conducted through a series of refractories/Na₂CO₃ reaction tests. The XRD analytical method was used to describe and understand the chemistry and interpret mineral matter transformation. The products of the tests were also determined by X-ray diffraction and the experimental observations were compared with the results of the thermodynamic simulations. Furthermore, the strength of sodium corrosion of the refractory materials was comprehensively discussed. The results show that MgO·Al₂O₃ has the best thermal stability and it is hard to corrode by Na₂CO₃, while the chrome-containing refractory reacts easily with Na₂CO₃ with a considerably high amount of corrosion product at a temperature of 600 °C. These experimental results are in agreement with the thermodynamic calculations.

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 score0.027
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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