Image analysis data obtained from scanning electron microscopy provided data for a detailed evaluation of the separation efficiency for various processes involving the beneficiation of particulate materials. A dry magnetic separation by a drum type magnetic separator served as a case study to visualize effects of processing of a skarn ore with a high content of cassiterite as ore mineral (~4 wt%). For this material, iron oxides and silicates are the main gangue mineral groups. Based on the obtained data, partition curves were generated with the help of local regression. From the partition curves, the separation efficiency was evaluated and the relevant particle properties deduced. A detailed analysis of the bias of the quantitative mineralogical data is presented. This bias was monitored and further analyzed in detail. Thorough analysis of feed and products of magnetic separation enabled identification of the most important factors that control losses of cassiterite to the magnetic product, namely the association with iron oxides and particle sizes below ~40 µm. The introduced methodology is a general approach applicable for the optimization of different separation processes and is not limited to the presented case study.

The principle of the circular economy is to reintroduce end-of-life materials back into the economic cycle. While reintroduction processes, for example, recycling or refurbishing, undoubtedly support this objective, they inevitably present material losses or generation of undesired by-products. Balancing losses and recoveries into a single and logical assessment has now become a major concern. The present work broadens the use of relative statistical entropy and material flow analysis to assess the recycling processes of two lithium-ion batteries previously published in the literature. Process simulation software, that is, HSC Sim^®, was employed to evaluate with a high level of accuracy the performance of such recycling processes. Hereby, this methodology introduces an entropic association between the quality of final recoveries and the pre-processing stages, that is, shredding, grinding, and separation, by a parameter based on information theory. The results demonstrate that the pre-processing stages have a significant impact on the entropy value obtained at the final stages, reflecting the losses of materials into waste and side streams. In this manner, it is demonstrated how a pre-processing system capable of separating a wider number of components is advantageous, even when the final quality of refined products in two different processes is comparable. Additionally, it is possible to observe where the process becomes redundant, that is, where processing of material does not result in a significant concentration in order to take corrective actions on the process. The present work demonstrates how material flow analysis combined with statistical entropy can be used as a parameter upon which the performance of multiple recycling processes can be objectively compared from a material-centric perspective.

This study outlines a recycling initiative conducted at Rekular GmbH, focusing on the recycling of 100 refrigerators. The recycling process employed a combination of manual dismantling, depollution, and mechanical processing techniques. Manual dismantling followed a predefined protocol to extract various materials, while the mechanical and physical processes involved shredding, zigzag, magnetic, and eddy current separation (ECS) to liberate and separate different materials. The resulting ferrous, non-ferrous and polymer product fractions were analyzed and categorized, providing valuable insights into the quality of interim products in the refrigerator recycling process. Simulations were then performed using FactSage^TM version 8.2 and HSC Chemistry 10 version 10.3.7.1 software to simulate the recovery of metals from the ferrous and non-ferrous fractions using pyro metallurgical and hydrometallurgical methods. An electric arc furnace (EAF) was utilized for iron (Fe), while a re-smelter process for aluminium (Al), and the black copper route was simulated for copper (Cu) recovery. The recovery rates including metallurgical, mechanical, and physical processes are as follows: Fe (78%), Al (68.4%), and Cu (52.4%). In contrast, the recovery rates through metallurgical processes are as follows: Al (99%), Fe (79%), and Cu (88%). This discrepancy is attributed to losses of these elements resulting from incomplete liberation in mechanical processing. Additionally, a product/centric approach was applied and the recycling index reached 76% for recovery the Al, Cu, and Fe metals in a refrigerator recycling process. Turning to the environmental impact evaluation within the life cycle assessment (LCA), the process unit with the highest emissions per refrigerator in the recycling process was the use of nitrogen during the shredding process, accounting for 3.7 kg CO₂ eq/refrigerator. Subsequently, the consumption of medium voltage electricity from the German grid during mechanical and physical separations contributed to 0.6 kg CO₂ eq/refrigerator. The EAF, and electrolytic refining stages in the metallurgical recovery process also had a notable impact, generating 10.7 kg CO₂ eq/refrigerator.