Development of molten salt-based processes through thermodynamic evaluation assisted by machine learning - Archive ouverte HAL
Article Dans Une Revue Chemical Engineering Science Année : 2024

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

Résumé

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)...
Fichier principal
Vignette du fichier
RoachL_ChemSciEng_2024.pdf (1.99 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04650280 , version 1 (16-07-2024)

Licence

Identifiants

Citer

Lucien Roach, Arnaud Erriguible, Cyril Aymonier. Development of molten salt-based processes through thermodynamic evaluation assisted by machine learning. Chemical Engineering Science, 2024, 299, pp.120433. ⟨10.1016/j.ces.2024.120433⟩. ⟨hal-04650280⟩
22 Consultations
9 Téléchargements

Altmetric

Partager

More