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Mass transfer coefficients across dynamic liquid steel/slag interface

   Leandro Dijon de Oliveira Campos

  Soutenances de thèse de l'équipe TCM

Titre : Mass transfer coefficients across dynamic liquid steel/slag interface

Date : Le 10 Janvier 2017, à 11h00

Lieu : Salle N20Bis du bâtiment Lavoisier

Jury : 

Stéphane Zaleski (rapporteur)

Jean-Luc Estivalezes (rapporteur)

Jean-Marie Buchlin 

Charles-Henri Bruneau 

Pascal Gardin (encadrant de thèse)

Jean-Paul Caltagirone (directeur de thèse)

Stéphane Vincent (co-directeur de thèse)



In order to characterize the mass transfer coefficients (MTC) of different species across liquid steel/slag interface, a multiphase Computational Fluid Dynamic (CFD) model was developed. MTC’s are estimated from models based on physicochemical and hydrodynamic parameters, such as mass diffusivity, interface shear and divergence strength. These models were developed for gas-liquid interactions with relative low Schmidt (Sc=ν⁄D) numbers (Sc≈200). However, the industrial processes involve mass transfer of chemical species with Sc number ranging from 103 to 104. To evaluate the applicability of these existing models, the fluid flow in the vicinity of a liquid/liquid interface is investigated. Computational Fluid Dynamic (CFD) and Laser Doppler Anemometry (LDA) were used to calculate and measure the velocity field on a continuous casting (CC) water model configuration. The work provides new insights and original measures to understand the fluid flow near liquid-liquid interfaces.


The mass transfer model of an industrial continuous casting (CC) mold showed that the mass transfer coefficients are not homogeneously distributed, and slag properties should follow this trend. This non-homogeneity was confirmed by physical experiments performed with a water model of a CC configuration and its CFD representation. The calculated flow was used to predict the MTC and the interface area between phases, since the interface is constantly moving. These parameters will be the input of thermodynamic models to predict slag composition and viscosity. This methodology is currently under validation, and it will also be applied to improve steel plant performance in the desulphurization process.

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