A coupled approach combining the Discrete Element Method (DEM) and the Pore Network Model (PNM) is developed to simulate particle-fluid flows at equivalent solving scales, specifically, the particle scale via DEM for capturing solid-phase dynamics, and the pore scale via PNM for characterizing fluid flow. Initially, the DEM-PNM model yields results that are largely consistent with those of the DEM-Lattice Boltzmann Method (DEM-LBM) in simulating a dynamic cell under low Reynolds number conditions. Subsequently, the model is employed to replicate the formation of a stable expanded bed composed of fine particles. By analyzing pore-scale fluid flow, tortuous flow paths, and particle-particle force chains, the results reveal that the development of a stable expanded bed corresponds with microscopic structural evolutions that reduce resistance to gas flow and enhance mechanical stability. Finally, leveraging the micromechanical interactions at the particle and pore scales, a quantitative correlation is derived to predict the minimum bubbling velocity of fine cohesive particles. This correlation explicitly incorporates particle-scale properties, including the Hamaker constant, as well as pore structure characteristics within the particle assembly. Overall, the study demonstrates that the DEM-PNM approach, operating at an equivalent particle-pore scale, holds significant promise for advancing the understanding of particle-fluid flow micromechanics.

Mineral grinding often represents a major fraction of total energy costs and coarse pre-concentration can significantly decrease unnecessary processing of barren material. Compressed-air ejection is effective at industrial scale, but suffers from low accuracy at millimeter scale. An opto-magnetic sorting process for coarse pre-concentration of REE-bearing particles before grinding was developed and assessed at labscale. The process combines image-based optical thresholding, water-based wetting of selected particles, magnetite adhesion to wetted surfaces, and magnetic lifting. This process thus couples selective magnetite coating (enabled by localized wetting) and magnetic lifting for particle sorting. The process was run in a reject-oriented mode to facilitate early mass rejection before subsequent comminution. Lab-scale experiments on rauhaugite revealed increasing pre-concentration with decreasing particle size, resulting in a low-grade fraction of 30.4 wt% of the 2–4 mm feed for possible early rejection. The high-grade fraction (57% of the 2–4 mm feed) achieved a TREO concentration of 2.32%, reflecting an enrichment factor of approximately 1.35 compared to the feed (1.71%), consistent with a partial realization of the intrinsic upgrading potential of the ore at this mass yield, as inferred from the TREO distribution of RGB-classified particles. The lab system processed 84 kg/h, corresponding to approximately 1 tonne of feed processed within 12 h. Based on an instantaneous power demand of ∼ 0.8 kW, this corresponds to an energy consumption of ∼ 9.6 kWh/tonne under steady-state conditions. The process also exhibited low water usage (∼5.7 L/tonne feed) and > 99% magnetite recyclability (after 3 runs). Beyond REE beneficiation, the proposed approach shows potential for selective pre-concentration of heterogeneous particulate streams requiring localized actuation.

The packing of multi-sized wet spheres is highly intricate, shaped by the interplay of interparticle forces induced by the presence of liquid. This study presents a comprehensive and quantitative analysis of the microscopic particle arrangement within a multi-sized wet sphere packing. To achieve this, a multi-sized wet sphere packing is obtained experimentally and is then characterized by various analytical techniques, in terms of coordination number (CN), pair correlation function (PCF), topological and metric properties of the Voronoi-Delaunay tessellation. Through CN and PCF analysis, distinctive packing features such as agglomerates and particle chains are identified and characterized. Furthermore, the application of the Voronoi and Delaunay tessellation techniques uncovers the existence of heterogeneous clusters of particles in contact and non-contact states. These tessellation methods also shed light on the distorted pore structure that emerges within the packing. The insights gained from this study may serve to enhance the assessment and development of innovative simulation methods where capillary and liquid-related forces acting on wet particles with a size distribution are considered.

In packed beds, bed structure significantly influences heat transfer between particles and fluids. A pore-network model (PNM) incorporating conduction, convection, and radiation is developed to investigate heat transfer in packed beds at the particle-pore scale. The model reveals how structural variations, such as bed porosity and pore geometry, influence heat transfer mechanisms. Validation against experimental data from demonstrates strong agreement in temperature evolution and heat transfer coefficients, confirming the model's accuracy. Bed-scale simulations reveal that bed porosity, gas velocity, and temperature collectively determine the dominant heat transfer mode. Convective heat transfer prevails at higher gas velocities, accounting for over 80 %, particularly in loosely packed beds. Conduction is more significant in denser beds and at lower velocities, contributing up to 40 %. Radiative heat transfer becomes substantial, accounting for up to 30 % only at elevated temperatures (e.g., 1000 °C), surpassing conduction in loosely packed configurations. At the pore scale, denser beds exhibit more uniform pore geometries that enhance local convective transfer through pores with near-regular shapes, such as smaller pores approximately half the particle size, typically observed in cells with porosities below 0.4. Conversely, increased heterogeneity in high-porosity beds promotes advective transport through larger pore throats. This model offers convenience in exploring and quantifying how bed porosity and local structural heterogeneity governs heat transport in granular systems and offers a flexible modelling framework for exploring structure–property relationships under diverse thermal and flow conditions.

The high-precision scrap sorting for effective metal recycling can bring substantial environmental and economic benefits. This article presents a magnetic image sensor that can help to identify the ferrous contaminants inside nonferrous scraps of large sizes. First, the concept and the theory for detecting ferrous contaminants are described. In particular, an inversion algorithm is proposed to characterize the size and position of ferrous contaminants inside the main scrap bodies. Then, based on computed and measured results, the feasibility of sensor design using either 1-D Hall arrays or 1-D pickup coils is demonstrated. Finally, methods are suggested to minimize disturbing signals from very large nonferrous pieces passing through the slightly uneven magnetic field. The obtained findings in this study may apply not only to nonferrous scraps but many other materials of which the mass ratio of the ferrous contaminant to the main material is small.

The agglomeration of cohesive particles can deteriorate fluidization quality and cause the defluidization of a bed, which is a common issue found in the applications of fluidized beds. This study aims to gain a better understanding of particle cohesion on agglomeration/fluidization behaviors and the effective methods for achieving a better fluidization quality, through numerical simulations based on the coupled approach of computational fluid dynamics and discrete element method (CFD-DEM). The effects of particle cohesion, gas velocities or flow conditions, and the bed geometry on the agglomeration and fluidization behaviors are analyzed. It is shown that the increase of particle cohesion can lead to deteriorated particle mixing, significant agglomeration of particles, and defluidization of the bed; the agglomeration-induced defluidization of highly cohesive particles is difficult to mitigate in a conventional flat-bottom fluidized bed. As large-sized agglomerates are more frequently found in the bottom of the bed, the spouted gas flow is then utilized and demonstrated to be effective in assisting the deagglomeration and fluidization of highly cohesive particles. Through the comparison of various spouted beds and spouted fluidized beds, the effective design of the bed bottom is identified for achieving a higher fluidization quality. Corresponding mechanisms underlying spout-assisted deagglomeration and fluidization are found to be much related to not only the enhanced particle-fluid but also particle-wall interactions in the confined space of a conical bed bottom, thus explaining the effectiveness and the importance of the bottom conical geometry of spouted beds. The obtained findings may help to understand the agglomeration-induced defluidization of fluidized beds and assist the fluidization of highly cohesive particles by the effective design of spouted beds.

An in-depth exploration of the reaction kinetics and thermo-chemical behaviors of the raceway can offer practical insights for optimizing the operations of blast furnace (BF), thus achieving a more effective iron and steel production process. In this study, the dynamic characteristics and the flow, heat and mass transfer behaviors in the BF raceway were simulated by Discrete Element Method-Computational Fluid Dynamics (DEM-CFD) method at a particulate scale. The effects of coke size distribution and blast velocity on coke combustion characteristics, thermochemical behavior (particle volume fraction, raceway size, carbon loss, and coke temperature) and microscopic properties (coordination number (CN), contact normal force, pore structure and stress) were systematically investigated. The results show that as the blast velocity decreases or the size ratio λ (the largest coke particle size divided by the smallest coke particle size) increases, the raceway size becomes smaller, resulting in a smaller area of high oxygen (O₂) concentration and low carbon monoxide (CO) concentration in the raceway, and higher CO concentration in the packed bed. For the thermal-chemical behaviors, a lower blast velocity or a higher λ value decreases the number of particles experiencing mass loss, as well as increases individual particle mass loss, the average coke temperature and its variance. For microscopic properties, the CN distribution becomes wider as λ increases. The contact normal force in the coke bed with λ > 1 is significantly higher than that of λ = 1. As λ increases or blast velocity decreases, the pore distribution curve shifts to the left and the average pore volume decreases. The stress acting on the particles in the raceway increases with the blast velocity or λ. These new understandings of the complex reactive flow behaviors in the raceway will shed light on energy utilization and process optimization.

Mixing structures and characteristics are crucial to the mixing quality of spherical/cylindrical binary granular systems like the biomass-coal mixtures which can affect energy release and carbon emissions. In this work, the mixing of binary spheres/cylinders in a rotating drum was numerically reproduced by using discrete element method (DEM). Systematic parametric studies were conducted to identify the role of various parameters such as rotation speed (ω) of the drum, aspect ratio (AR), mass fraction (φ_c) and volume fraction (φ_v) of cylindrical particles, and the density difference (φ_ρ) between the binary particles; meanwhile, corresponding mechanisms were also explored by analyzing the kinetic energy. Results show that when the flow regime is rolling/cascading, the mixing quality can be effectively improved; however, when the flow regime is cataracting, the mixing quality becomes worse. With AR close to 1.0, the interlocking effect between particles becomes weaker and the porosity becomes smaller, which leads to the higher contact efficiency and thus improves the mixing quality. Binary mixtures with different φ_c are synergistically affected by energy input and interlocking structure. The volume of spherical particles is more conducive to improving the mixing quality than that of cylindrical particles when the volume of the granular system is at the same level. The φ_ρ can cause segregation behavior of particles so as to make the mixing quality worse.