Een multischalig computationeel kader toegepast op de pyrometallurgische recyclage van platinumgroepmetalen

Since the 1970s, automotive catalysts have been used to regulate air pollution resulting from incomplete fuel combustion. Their active components are Platinum Group Metals (PGM), more specifically Pt, Pd, and Rh, typically present as nanoparticles dispersed within a porous γ-Al2O3 washcoat layer mounted on a cordierite (2MgO-2Al2O3-5SiO2) substrate. At the end of their service life, efficient recycling of PGMs from spent automotive catalysts (SACs) is essential for both sustainability and economic reasons. Pyrometallurgical smelting is the most widely applied industrial recycling method, used by companies with advanced metallurgical and refining technologies, including Umicore (Belgium). In this process, ground SACs are melted with fluxes (mostly CaO), a collector metal (typically Cu for cordierite-based catalysts), and a reducing agent. During the smelting process, a slag phase forms from the molten cordierite and flux, while dispersed Cu droplets capture the PGMs, leaving the Al2O3-carrier in the slag. Despite its industrial maturity, the atomic and microscale mechanisms controlling PGM collection remain poorly understood. This thesis develops a comprehensive, multi-scale modeling framework to quantitatively simulate the recovery of PGM nanoparticles by Cu droplets within a slag environment. The approach integrates a thermodynamically consistent, multi-phase, multi-component phase-field model with kinetic parameters obtained from molecular dynamics (MD) simulations. The first part of this work extends conventional phase-field models, which often encounter limitations under large thermodynamic driving forces or in the presence of stoichiometric compounds. By rederiving the governing equations for stoichiometric compounds and generalizing the high-driving-force adaptation to multi-component systems, a robust framework was established and validated. This enables stable, quantitative simulations of diffusion-controlled transformations across all driving forces and length scales. The second part addresses the scarcity of kinetic data for oxide melts, a key limitation for quantitatively simulating Al2O3 washcoat dissolution in CaO-Al2O3-SiO2 slags. A high-throughput MD study quantified the composition- and temperature-dependence of Ca, Al, Si, and O self-diffusion in these melts. Bouhadja's empirical force field was identified as most accurate for CaO-Al2O3-SiO2 melts through benchmarking against experiments, empirical models, and ab initio MD for the structural and transport properties of the melt. Using this force field, 119 simulations were performed across 40 compositions and five temperatures. The resulting diffusivities were parameterized using the pair-fraction-based model of Thibodeau and Jung. Both MD-only and hybrid (MD+experiment) fits were successfully validated against diffusion couples and dissolution experiments. Finally, the integrated framework was applied to simulate key PGM recovery mechanisms. Simulations showed that PGM dissolution in liquid Cu occurs almost instantaneously once contact is established, whereas the release of encapsulated PGMs from the wash coat is several orders of magnitude slower, making it the rate-limiting step in early smelting. The results highlight slag design as a critical lever for accelerating washcoat dissolution, while maximizing collision probability, through optimized collector content and slag compositions that promote effective mixing, emerges as the most impactful strategy for recovery efficiency. This thesis demonstrates how phase-field modeling and molecular dynamics can be tightly coupled to quantitatively predict the fundamental small-scale mechanisms underpinning PGM recovery in smelting conditions. The combination of methodological advances, including an extended phase-field framework and an MD workflow to obtain reliable kinetic parameters for oxide melts, with systematic application to Cu-based PGM collection, provides both new insight into industrially relevant processes and a transferable modeling strategy. By linking atomic-scale transport with mesoscale microstructure evolution, this work establishes a foundation for predictive simulations in metallurgical systems where complex chemistries and multi-phase interactions govern performance.