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Development of molten salt–based processes through thermodynamic evaluation assisted by machine learning

Article dans une revue avec comité de lecture
Author
ROACH, Lucien
525101 Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
ccERRIGUIBLE, Arnaud
1002421 Institut de Mécanique et d'Ingénierie [I2M]
AYMONIER, Cyril
525101 Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]

URI
http://hdl.handle.net/10985/25647
DOI
10.1016/j.ces.2024.120433
Date
2024-11
Journal
Chemical Engineering Science

Abstract

Molten salt–based processes and hydrofluxes are highly sensitive to mixture composition and require knowledge of the combined melting point for successful materials syntheses. In particular processes using hydroxide–based fluxes (pure salt melts) and hydrofluxes (salt melts containing 15–50% HO) have been shown to be interesting environments to synthesize inorganic materials in high oxidation states. The development of tools to predict these properties is desirable to inform the implementation of processes using these mixtures. In this work, we use an artificial neural network model to estimate the melting points of fluxes and hydrofluxes comprising of quaternary mixtures of NaOH, KOH, LiOH, and H2O. A database of 1644 data points collected from 47 different sources was used in the training of the model. Melting points were predicted from the molar fractions of each component (4 independent variables)...

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