Detailed numerical modeling of the iron ore direct reduction process

The iron and steelmaking industry is among the largest contributors to global CO₂, driven by its energy-intensive processes and reliance on carbon-rich fuels. As steel demand rises in an increasingly competitive global market, innovative and sustainable production methods are urgently needed. The Direct Reduction (DR) process using syngas or hydrogen presents a promising approach to reducing CO₂ emissions and energy consumption in steel production. Computational modeling plays a crucial role in accelerating technology development, yet traditional models, such as the shrinking core model, fail to fully capture the complexities of the reduction process. More advanced models are necessary to simulate interactions between iron ore pellets and reducing gases and to scale effectively to industrial applications. To address these challenges, this dissertation introduces an improved porous solid model that overcomes biases in the shrinking core model, accurately representing the reduction of single iron ore pellets in H₂ and CO environments while accounting for carbon deposition and porosity changes. A comprehensive reduction mechanism is developed and validated against extensive experimental data without parameter adjustments, providing a solid foundation for advanced simulations. Besides the stand-alone kinetic model, a Computational Fluid Dynamics (CFD) solver has been developed, bringing the considerations to a spatially resolved environment, including pellet and gas phase regions. Building on the insights gained from single-pellet models, the research advances to the more complex modeling of fixed beds; a crucial step in bridging the gap between detailed small-scale studies and the case of industrial-sized reactors. A comprehensive methodology is developed, incorporating realistic packed-bed structures and high-quality computational meshes, for particle-resolved CFD simulations of the hydrogen-based direct reduction process in a fixed-bed setup. These simulations reveal crucial insights into the reduction process, such as the non-uniform reduction within the bed, the presence of gas pockets, and the impact of temperature variations due to the endothermic nature of hydrogen-based reduction. Finally, the study explores the influence of pellet sizes and shapes in the DR process via different reconstruction techniques, including computed tomography. By examining beds with various particle characteristics and structures, the research highlights the significant impact of these factors on reduction efficiency and overall conversion rates. This comprehensive modeling approach offers critical insights for optimizing the hydrogen-based direct reduction process, paving the way for its application in industrial-scale reactors. By addressing these research questions and providing innovative solutions, this dissertation contributes to the advancement of DR-technology, offering a path toward more sustainable steel production.