Abstract
The discovery of new metallic materials is of prime importance for the development of new technologies in many fields such as electronics, aerial and ground transportation as well as construction. These materials require metals which are obtained from various pyrometallurgical processes. Moreover, these materials need to be synthesized under extreme conditions of temperature where liquid solutions are produced and need to be contained. The design and optimization of all these pyrometallurgical processes is a key factor in this development. We present several examples in which computational thermochemistry is used to simulate complex pyrometallurgical processes including the Hall–Heroult process (Al production), the PTVI process (Ni production), and the steel deoxidation from an overall mass balance and energy balance perspective. We also show how computational thermochemistry can assist in the material selection in these extreme operation conditions to select refractory materials in contact with metallic melts. The FactSage thermochemical software and its specialized databases are used to perform these simulations which are proven here to match available data found in the literature.
Highlights
Computational thermochemistry is a powerful tool used by engineers and scientists as it allows the theoretical exploration of the energetic behavior of multicomponent and multiphasic systems for a wide range of conditions at a small computational cost
For disordered solid solutions and molten phases, the Kopp–Neumann rule has been proven to be a good approximation in many cases. For stoichiometric compounds, it has severe limitations, mainly related to the difference in the electronic structure between the compound and its constituent elements. To overcome this lack of experimental data, it is nowadays possible to use atomistic simulations based on Density Functional Theory (DFT) using the Kohn–Sham approximation [7,8]
The heat capacities of these two compounds have been predicted from DFT simulations and compared with available experimental data
Summary
Computational thermochemistry is a powerful tool used by engineers and scientists as it allows the theoretical exploration of the energetic behavior of multicomponent and multiphasic systems for a wide range of conditions at a small computational cost. Its algorithmic efficiency resides in (1) the use of simple and derivable mathematical functions to describe the energetic behavior of each phase, (2) the relatively small number of variables used to define the equilibrium state of the system, and (3) the need to respect thermodynamic constraints at equilibrium such as the Gibbs phase rule The latter greatly speeds up the calculations as it can be used to identify a good initial estimate of the equilibrium state of the system [1]. After an equilibrium state candidate is identified, other constrained minimization problems need to be solved [3] to evaluate the activity of each potentially stable phase not considered in the phase assemblage This step is of fundamental importance as it confirms that the global Gibbs free energy minimum for some imposed constraints has been reached. This paper is intended to serve as a reference for engineers and materials scientists who want to deepen their knowledge of computational thermochemistry and explore its various uses in research and development
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