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.

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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.@en

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.

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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.

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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.@en