This study aimed to facilitate efficient arsenic detoxification by inducing the microbial methylation of inorganic arsenic. Recombinant Escherichia coli strains K31 was transformed to overexpress arsenic methyltransferase, and the methylation of inorganic arsenic was evaluated using the enzyme extracted from the strain. To ensure continuous reactions by maintaining enzymatic activity, the extracted enzyme was immobilized in microcapsules (MC) to catalyze the methylation of inorganic arsenic. The total yield of methylated organic arsenic compounds in strain K31 was 57.0% (after 2 h of incubation at pH 6.5 and 40°C), of which trimethyl arsenic compounds (TMAC) accounted for 17.9%. When arsenic was methylated using MC prepared with the crude enzyme solution, the total yield of methylated organic arsenic compounds was 15.6%. The residual enzymatic activity was calculated to be 55.8%. Future studies should aim to increase the efficiency of conversion to TMAC, thus elevating the yield of organic arsenic compounds, by optimizing the conditions for enzyme immobilization in MC.

This study is an engineering application of an optimum design approach using the discrete element method (DEM) and a genetic algorithm (GA) which is one of the most commonly used evolutionary computation algorithms. The three-dimensional shapes of waste electrical and electronic equipment (WEEE) were directly applied in a DEM simulation. The DEM-GA simulation was applied to design the tray set on a single-item feeder for recycling WEEEs; as a result, we obtained the left-right asymmetrical tray shape. Moreover, unlike conventional symmetrical trays, the asymmetrical shape induced complex side forces to prevent clogging and to untangle any clogs that had formed. We manufactured a zig-zag type asymmetrical tray and examined its feeding performance. Finally, we confirmed that the asymmetrical tray was more effective to prevent the feeding stop when compared to the symmetrical tray.

In order to remove smelting repellent elements such as Al and to efficiently recover valuable elements such as Au from waste printed circuit boards, the author have developed an algorithm that automatically detects electronic devices containing a large amount of repellent elements and valuable elements from 2D images of circuit boards by applying image recognition technology. The two types of detection algorithms based on rule-based image processing and deep learning networks were proposed. The former focuses on the appearance characteristics (color, size, shape) of each device and sequentially performs relatively simple image processing. As a result of the detection tests on images of actual waste PC boards using, almost 100% of the heat sinks and CPU socket holders, which are repellent devices, could be detected. The detection ratio was about 80% for the aluminum electrolytic capacitors, and about 80 to 90% for the ICs and connectors, which are valuable devices, even for large ones. On the other hand, when using an algorithm based on a deep learning network, in detection tests targeting the aluminum electrolytic capacitors, ICs, and connectors mounted on waste PC boards, the detection ratio of 95% or more and false detection ratio of less than 5% were successfully achieved.

The effects of alumina (Al₂O₃) and magnesia (MgO) impurities on the precipitation of spinels such as magnetite (Fe₃O₄) due to the oxidation of fayalite (Fe₂SiO₄) slag in copper smelters were investigated. Molten fayalite slag was oxidized in alumina, magnesia, and quartz sample boats, and spinel precipitation behaviors were compared. The inclusion of Al₂O₃ and MgO as impurities promoted the precipitation and growth of spinel crystals. It was assumed that this effect was attributed to the rapid formation of hercynite at the interface between the alumina sample boat and slag, growth of spinel due to the substitution of Al and Fe(III) in hercynite, and the solid solution of Mg in spinels. However, as the experiments with quartz sample boats were conducted unavoidably with SiO₂ saturated slag, the possibility of suppression of solute diffusion due to increased viscosity and decreased FeO activity in the slag cannot be ignored. Therefore, it is considered that the effects of Al₂O₃ and MgO on spinel precipitation must be further investigated.

Wasted printed circuit boards, PCBs, contain not only valuable metals such as Cu, Au, etc., but also smelting-repellent components such as Al, Br, and Sb. The latter should be removed before feeding them to the smelting process. In this study, we applied the processes as a pretreatment such as (i) heating and screening to remove devices on the PCBs, (ii) roasting to remove/decompose Br and Sb from PCBs, (iii) two-stage selective grinding to concentrate Cu and to remove Al bearing glass fiber, and (iv) high gradient magnetic separation to concentrate Ni bearing Cu and Au. Then, we demonstrated that the above process concentrated Au and Cu with high recovery and remove most of the Al, Br, and Sb components from the wasted PCBs.