Metastability, disorder and jamming are the typical characteristics of amorphous systems, while the related structure changes remain unclear. Sphere packing is often used as a structure model for amorphous and crystalline states. In this article, sphere packing systems with packing densities ranging from 0.50 to 0.74 were simulated by using Discrete Element Method (DEM), and the obtained packing structures were assessed to investigate the densification process and jamming properties. An order parameter that can effectively distinguish the order and disorder of packing structures was proposed based on the distribution characteristics of jamming angles. Then the evolution of jamming characteristics during the transition from Random Loose Packing (RLP) to Random Close Packing (RCP) and the jamming-jamming relations of different packing structures were demonstrated. On this basis, a correlation between order-jamming-metastable states from the microscopic structural perspective was established, which is of valuable theoretical and practical implications for the characterization and synthesis of crystalline and amorphous materials.

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.

Critical raw materials (CRMs) are one of the enablers of a sustainable future due to their importance in green technologies. Yet, their own circularity and end-of-life recycling rates have been lacking as their concentrations are too low in waste products to be efficiently recycled. This is not the case, however, for discarded printed circuit boards where different types of electronics components (ECs) use specific CRMs in high concentrations. Furthermore, due to worldwide manufacturing standards these ECs are consistent between different printed circuit boards (PCBs) in their physical characteristics such as size, shape and material composition. Yet at the moment no sorting methods exist that can separate ECs from modern PCBs. Therefore, we aim to evaluate multiple simple yet effective mechanical separation methods to sort said ECs with CRM recovery in mind. First of all, ECs from flat-panel displays were sieved into a small (<4 mm), medium (4 - 10 mm) and large fraction (>10 mm). This was followed by a roll sorter to separate thinner components, like IC chips, from similarly sized thicker ECs. In order to separate the components based on ferromagnetic composition an innovative overbelt ferromagnetic separator was developed where the magnetic field strength continuously decreased over the length of the belt. Lastly, the different types of ECs were analysed by laser-induced breakdown spectroscopy to identify the presence of any CRMs. Our study shows that by combining these three different sorting technologies it is possible to sort the ECs in a way that the majority of CRM containing components are concentrated in only 21.09 wt% of the total weight. This in turn results in significantly higher CRM concentrations, thus removing a major limitation to their recovery and improving CRM circularity for a more sustainable future.

The growing demand for aluminium worldwide makes aluminium recycling critical to realising a circular economy and increasing the sustainability of our world. One effective way to improve the impact of aluminium recycling is to develop cost-efficient automated sorting technologies for obtaining pre-defined high-quality aluminium scrap products, thus reducing undesirable downcycling and increasing environmental/economic benefits. In this work, an innovative facility, which includes singulation, sensor scanning, and ejection, is optimised for the automated sorting of aluminium scraps. The sorting facility is computationally studied by a virtual experiment model based on the discrete element method. The model considers particle-scale dynamics of complex-shaped scraps and mimics the automated operation of the facility. Based on virtual experiment modelling, the flow of scrap is optimized by computation, with the feasible operation of the sorting facility being proposed. Accordingly, the sorting facility has been built and model predictions are confirmed in actual operation.

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.