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The recycling-oriented material characterization of hard disk drives with special emphasis on NdFeB magnets

Dańczak A., Chojnacka I., Matuska S., Marcola K., Leśniewicz A., Wełna M., Żak A., Adamski Z., Rycerz L.

Physicochemical Problems of Mineral Processing

2018

Vol. 54, iss. 2

363--376

Hard disk drives (HDDs) consist of many components made from various materials: e.g. aluminum, steel, copper and rare earth elements (REEs). Recycling and reuse of these materials is desirable for economic and environmental reasons. Developing of potential HDDs recycling methods requires knowledge about HDDs material characteristic. The study aims to explore knowledge about structure and chemical composition of HDDs main components with special emphasis on NdFeB magnets. HDDs collected for the experiments came from Desktop PCs and Notebooks. The dependence between the average mass of HDDs components and such parameters as producer, year and country of production and disk capacity was analyzed. Chemical composition of NdFeB magnets and the heaviest components (i.e. top cover, mounting chassis, platters and metallic plates from magnet assembly of actuator) was analyzed by various analytical methods. The heaviest HDDs main components: top cover and mounting chassis, with the highest recycling potential, are made of aluminum and steel respectively. The majority of HDDs components showed also the existence of different alloy additions: C, Mg, Si, P, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Sn and Pb. NdFeB magnets constitute 2.2 ± 1.1% of the average HDD from Desktop PC (517.3 ± 64.2 g) and 3.2 ± 1.2% from Notebook (108.2 ± 24.3 g). The chemical composition of NdFeB magnets from collected HDDs changes in the wide range: Fe (53–62%), Nd (25–29%), Pr (2–13%), Dy (0.1– 1.4%), Ni (2–6%), Co (0.5–3.6%), B (0.8–1.0%). Recycling of permanent magnets based on NdFeB alloys is potential remedy to fill the gap in the supply of rare earth elements on the global REEs market.

Pressure leaching of sulfide concentrate produced by Lubin Concentrator (KGHM Polska Miedz SA, Poland)

Matuska S., Ochromowicz K., Chmielewski T.

Physicochemical Problems of Mineral Processing

2018

Vol. 54, iss. 3

781--792

The rapidly declining quality of ores and concentrates produced by Lubin Concentrator (KGHM) makes the flotation concentrates exceptionally difficult-to-process by flash smelting. Currently, a hydrometallurgical process, using sulfuric acid with the presence of oxidants as a leaching medium, seems to be either a reasonable alternative or a complementary option to pyrometallurgy. The unique lithological, mineralogical and chemical properties of the concentrate were considered in a selection of the process route. Results of a pressure leaching of the copper sulfide concentrate from Lubin Concentrator (ZWR Lubin) with oxygenated aqueous H2SO4 solutions are presented. The effect of temperature from 120 to 200 °C, initial concentration of H2SO4 from 60 to 100 g/dm3, and oxygen partial pressure ranging from 0.5 to 2.0 MPa were investigated. The pressure leaching appeared to be an efficient process for recovering copper from the Lubin concentrate. At optimal leaching conditions (140 °C, 100 g/dm3 H2SO4, 1.0 MPa O2) 96% of Cu was extracted after 4 h.

Classification of animals to determine the migration potential at the construction of new infrastructure

Matuska S., Hudec R., Benco M., Zachariasova M.

Archives of Transport System Telematics

2013

Vol. 6, iss. 4

26--30

At the planning and construction of new infrastructures, the information about migration potential of animals in a target area is needed. This information will be used to design of migration corridors for wild animals. To determine the migration potential of animals based on distributed video camera system, new methods for object recognition and classification are developed. In general, an object recognition system consists of three steps, namely, the image feature extraction from the training database, training the classifier and evaluation of query image of object/animal. In this paper, an extraction of local key point by SIFT or SURF descriptors, bags of key points method in combination with SVM classifier and two hybrid key points detection methods are proposed in detail.