In blast furnace ironmaking, a mixture of iron ore pellets and sinter is charged in layers at the furnace top, with particle velocities reaching up to ∼10 m/s at the stock surface. The inherent differences in particle size, shape, and density between pellets and sinter pose challenges for maintaining a uniform mixture during this high-velocity charging, leading to segregation and uneven material distribution. This non-uniformity can negatively affect furnace efficiency and stability. Understanding segregation during charging is therefore crucial for optimizing the ironmaking process. The Discrete Element Method (DEM) can offer valuable insights, provided that the model parameters are calibrated and verified. This study presents a calibrated DEM model for a pellet–sinter mixture with a 50–50 mass ratio of both components. A novel high-velocity laboratory setup was used to simultaneously measure five different key performance indicators (KPIs) related to flow and packing behavior at various discharge heights, corresponding to different flow velocities. Calibration was performed at the highest flow velocity, representative of actual blast furnace conditions. The process involved creating response surface models for each KPI and using a multi-objective optimization approach with a desirability function to determine the model parameters. A step-wise calibration strategy was employed, first optimizing pellet and sinter interaction parameters individually, followed by calibration of the pellet–sinter interaction parameters. This approach proved effective, as the calibrated model accurately reproduced experimental data. Results also suggest that the calibration outcome is flow-invariant in this setup, with the model successfully predicting flow and packing behavior at lower discharge heights.

Steel production begins by charging a mixture of different iron-ore particle types into a blast furnace, a tall shaft reactor designed to extract iron through melting and chemical reduction. The main materials are pellets, fired near-spherical agglomerates of iron-ore concentrates, and sinter, fused agglomerates of fine ore and fluxes. The mixture is charged in layers, and these layers collectively form the packed bed in the furnace. During operation, hot reducing gas must flow through the void space of this packed bed to drive the reduction reactions and provide heat for melting, which makes bed permeability critical. Observing how these materials distribute during charging is difficult because of harsh operating conditions and scale, yet this distribution governs bed permeability and thereby the gas flow that affects efficiency, energy use, and emissions. As a result, it remains unclear whether the deposited mixture is uniform and, if not, how to improve it. Despite decades of industrial use, the blast furnace therefore remains, to some extent, a “black box”.

The Discrete Element Method (DEM) is well suited to this problem because it resolves the motion of individual particles and their interactions, which enables a detailed assessment of mixture distribution within and between layers. This thesis develops a calibrated DEM framework to predict mixed-layer formation and to assess its implications for blast furnace permeability. The focus is on component distribution (pellet versus sinter) and packing distribution as key descriptors of the burden structure in the charged layers. The model adopts the Hertz-Mindlin contact law with rolling model C for coarse, free-flowing materials and treats a 15-parameter interaction set for the pellet–sinter mixture.

A pre-calibration step evaluates three inter-component parameters: restitution, sliding friction, and rolling friction, using porosity as a proxy for permeability. Sensitivity analyses show that all three parameters strongly affect porosity and component distribution, and they are therefore retained in the full calibration. Robust calibration requires conditions representative of charging and multiple constraints. To that end, a high-velocity laboratory setup with a 4.7 m drop height enables systematic piling tests for pellets, sinter, and their 50/50 mixture. Five KPIs (hopper discharge time, heap mass, heap contour, heap center height, and heap porosity) are used in a stepwise calibration, first for the single components and then for the inter-component interactions. The calibrated model reproduces experiments across discharge heights with maximum deviations of 5.5% at the highest drop, which indicates reliable predictive performance over a range of velocities.

Industrial-scale charging is addressed via particle up-scaling. While a factor of 2 is insufficient for practical runtimes, factors up to 5 preserve layering, segregation, and porosity patterns when mass flow rate is adjusted, without compromising the final packed-layer structure. Using this up-scaled model, case studies quantify how operating conditions shape component and packing distributions in successive layers, and they offer practical guidance for charging strategies that improve mixture distribution and, consequently, overall furnace performance.

Bed permeability is a crucial factor in blast furnace performance which depends on the material distribution achieved through charging. Since a homogeneous bed of pellet and sinter is recommended, it is crucial to understand whether segregation of the pellet-sinter mixture occurs during charging. The Discrete Element Method is useful in this regard; however, simulations of pellet-sinter mixture charging currently lack credibility since pellet-sinter interaction parameters have not yet been calibrated and validated. Determining pellet-sinter interaction parameters will require significant efforts, so it is useful to know whether mixture segregation and the resulting bed permeability are sensitive to these parameters. In this work, we investigate to what extent the restitution coefficient, sliding friction coefficient and rolling friction coefficient between pellet and sinter affect segregation during bed formation and the resulting permeability in terms of porosity using a simplified charging setup. The investigation is done for different mixture compositions and flow velocities, and analysis settings including sample size and sampling directions. We conclude that all parameters affect segregation and porosity, regardless of the composition and velocity. Hence, all mixture parameters including the interaction parameters between the components must be carefully calibrated when developing a model for predicting permeability.

In this paper we introduce the open-source code MercuryDPM: a code for simulating discrete particles. The paper discusses software and management issues that may be interesting for the developers of other open-source codes. Then we review the new features that have been added since the last publication: an improved Hertz-Mindlin model; a new liquid bridge model of Lian and Seville; a droplet-spray model; better support for re-creating complex, measured particle size distributions; a new implementation of rigid clumps; an implementation of elastic membranes; a wear model for walls; a soft-kill feature and a cloud-deployment interface for AWS.

Segregation control is a challenging yet crucial aspect of bulk material handling processes. The discrete element method (DEM) can offer useful insights into segregation phenomena, provided that reliable models are developed. The main challenge in this regard is finding a good balance between including particle-level details and managing the computational load. This is especially true for industrial applications, where multi-component flows consisting of particles with various irregular shapes and wide size distributions are encountered in huge amounts. In this work, we review the state of the art in DEM modelling of segregation in industrial applications involving the gravity-driven flow of dry, cohesionless granular materials. We start by introducing a novel scientific notation to distinguish between different types of mixtures. Next, we review how parameters for mixture models are determined in the current literature, and how segregation is affected by material, geometric and operational parameters based on these models. Finally, we review existing segregation indices and their applicability to multi-component segregation. We conclude that systematic calibration procedures for segregation models are currently missing in the literature, and realistic models representing multi-component mixtures have not yet been developed. Filling these gaps will pave the way for optimising industrial processes dealing with segregation.

Bed permeability is a crucial factor in blast furnace efficiency and stability. The Discrete Element Method (DEM) has been used extensively to model material flow in different parts of the furnace and holds great potential for optimizing the permeability. The inherent computational load is the main bottleneck in using this method to provide detailed descriptions of different blast furnace granular phenomena on an industrial scale. In recent years, computing capabilities have been rapidly increasing and more elaborate models are being developed for the furnace as a whole. This paper reviews the recent progress in modelling relevant phenomena related to the burden distribution, and how they affect the bed permeability, using DEM. We conclude that significant efforts have been made in modelling the burden distribution; however, these models generally do not investigate the permeability. Hence, understanding of how the permeability can be optimized still requires significant efforts towards model development.

We focus on the main new developments underway in MercuryDPM. New features include deformable clusters (agglomerates), experimental coarse-graining, melting particles, particle-solid interactions, multi-resolution particle-fluid coupling, pressurecontrolled Lees-Edwards boundaries, better hybrid openMP-MPI parallelisation, and more advanced STL/STEP readers for reading in industrial geometries. Some of these new features will be demonstrated for industrial relevant examples, such as industrial mixers, selective laser sintering, and a tunnel boring machine.