This paper reviews recycling technologies in Astec-irie Co., Ltd. Repellent substances and valuable metals, such as precious metals and rare metals, contained in waste electronics were separated and concentrated using superheated steam, an aqueous iron (III) chloride solution, and AI. The electronic components and substrates were separated by melting the solder with superheated steam, and the repellent substance in noniron smelting was removed from them. The gold-plated parts contained in the electronic components were separated back into gold by dissolving the copper and nickel in the aqueous iron (III) chloride solution. The copper and nickel dissolved into the solution were separated and recovered respectively as solid components by adding iron powder. The useful metals were concentrated from the separated electronic components, which are sorted out using AI. As a result, valuable metals such as precious metals could be concentrated while reducing the concentration of repellents, thus making it possible to treat the removed repellents as useful substance.
The superheated steam has used in the field of food engineering when it has the remarkable character such as the operation in the ordinary pressure, the good efficiency for heat transfer and the treatment in the steam without oxygen. It must be applied for the recycling process to recover the metal and remove the impurity.The examples were introduced to apply the technology for the removal of oil attached to the magnesium cutting powder and the treatment of metal plated resin with painting. We succeeded to remove less than 0.1% of oil for magnesium cutting powder. It was a value enough to bring it to the reconstructive process. The painting was damaged for the metal plated resin to recover the nickel and copper in the plating dissolution process. The both were promising results of the research and development for industrial-government-academic complex in north Kyushu district.
The field of recycling for waste electronic components, which is the typical example of an urban mine, requires the development of useful sorting techniques. In this study, a sorter based on image identification by deep learning was developed to select electronic components into four groups. They were recovered from waste printed circuit boards and should be separated to depend on the difference after treatment. The sorter consists of a workstation with GPU, camera, belt conveyor, air compressor. A small piece (less than 3.5 cm) of electronic components on the belt conveyor (belt speed: 6 cm/s) was taken and learned as teaching data. The accuracy of the image identification was 96% as kinds and 99% as groups. The optimum condition of sorting was determined by evaluating accuracies of image identification and recovery rates by blowdown when changing the operating condition such as belt speed and blowdown time of compressed air. Under the optimum condition, the accuracy of image classification in groups was 98.7%. The sorting rate was more than 70%.
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