Investigation of graphite oxidation kinetics in MgO–C composite via artificial neural network approach

In this study an artificial neural network (ANN) model was developed to predict the oxidation behavior of magnesia graphite composites. After mechanism evaluation in different conditions, the kinetic parameters such as effective diffusion coefficient and diffusion activation energy of oxidation were...

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Bibliographic Details
Published in:Computational materials science 2007-06, Vol.39 (4), p.723-728
Main Authors: Ali Nemati, Z., Moetakef, Pouya
Format: Article
Language:English
Subjects:
ANN
Cross-disciplinary physics: materials science
rheology
Diffusion
Exact sciences and technology
Graphite oxidation
Kinetics
Materials science
MgO–C composite
Physics
Shrinking core model
Surface treatments
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Summary:In this study an artificial neural network (ANN) model was developed to predict the oxidation behavior of magnesia graphite composites. After mechanism evaluation in different conditions, the kinetic parameters such as effective diffusion coefficient and diffusion activation energy of oxidation were calculated from ANN predicted results at different graphite content. The obtained mechanism and kinetic parameters were compared with experimental data. First of all, the reliability of the model was checked with different available data. It was found that the model results were in good agreement with experimental data prediction. The results showed that the main mechanism of oxidation was pore diffusion and effective diffusion coefficient as well as diffusion activation energy were comparable with previous works. Effective diffusion coefficient and diffusion activation energy which were calculated versus graphite content are in good agreement with experimental values.
ISSN:0927-0256
1879-0801
DOI:10.1016/j.commatsci.2006.09.008