Cristin-resultat-ID: 2150613
Sist endret: 18. januar 2024, 15:04
NVI-rapporteringsår: 2023
Resultat
Vitenskapelig artikkel
2023

Magnesiothermic Reduction of Silica: A Machine Learning Study

Bidragsytere:
  • Kai Tang
  • Azam Rasouli
  • Jafar Safarian
  • Xiang Ma og
  • Maria Gabriella Tranell

Tidsskrift

Materials
ISSN 1996-1944
e-ISSN 1996-1944
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2023
Publisert online: 2023
Volum: 16
Hefte: 11
Artikkelnummer: 4098
Open Access

Importkilder

Scopus-ID: 2-s2.0-85161532724

Beskrivelse Beskrivelse

Tittel

Magnesiothermic Reduction of Silica: A Machine Learning Study

Sammendrag

Fundamental studies have been carried out experimentally and theoretically on the magnesiothermic reduction of silica with different Mg/SiO2 molar ratios (1–4) in the temperature range of 1073 to 1373 K with different reaction times (10–240 min). Due to the kinetic barriers occurring in metallothermic reductions, the equilibrium relations calculated by the well-known thermochemical software FactSage (version 8.2) and its databanks are not adequate to describe the experimental observations. The unreacted silica core encapsulated by the reduction products can be found in some parts of laboratory samples. However, other parts of samples show that the metallothermic reduction disappears almost completely. Some quartz particles are broken into fine pieces and form many tiny cracks. Magnesium reactants are able to infiltrate the core of silica particles via tiny fracture pathways, thereby enabling the reaction to occur almost completely. The traditional unreacted core model is thus inadequate to represent such complicated reaction schemes. In the present work, an attempt is made to apply a machine learning approach using hybrid datasets in order to describe complex magnesiothermic reductions. In addition to the experimental laboratory data, equilibrium relations calculated by the thermochemical database are also introduced as boundary conditions for the magnesiothermic reductions, assuming a sufficiently long reaction time. The physics-informed Gaussian process machine (GPM) is then developed and used to describe hybrid data, given its advantages when describing small datasets. A composite kernel for the GPM is specifically developed to mitigate the overfitting problems commonly encountered when using generic kernels. Training the physics-informed Gaussian process machine (GPM) with the hybrid dataset results in a regression score of 0.9665. The trained GPM is thus used to predict the effects of Mg-SiO2 mixtures, temperatures, and reaction times on the products of a magnesiothermic reduction, that have not been covered by experiments. Additional experimental validation indicates that the GPM works well for the interpolates of the observations.

Bidragsytere

Kai Tang

  • Tilknyttet:
    Forfatter
    ved Metallproduksjon og prosessering ved SINTEF AS

Azam Rasouli

  • Tilknyttet:
    Forfatter
    ved Institutt for materialteknologi ved Norges teknisk-naturvitenskapelige universitet

Jafar Safarian

  • Tilknyttet:
    Forfatter
    ved Institutt for materialteknologi ved Norges teknisk-naturvitenskapelige universitet

Xiang Ma

  • Tilknyttet:
    Forfatter
    ved Metallproduksjon og prosessering ved SINTEF AS

Maria Gabriella Tranell

  • Tilknyttet:
    Forfatter
    ved Institutt for materialteknologi ved Norges teknisk-naturvitenskapelige universitet
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