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ArticleName A contrast study of alumina-containing sweepings to assess the applicability of photometric separation
DOI 10.17580/or.2021.06.06
ArticleAuthor Burdonov A. E., Barakhtenko V. V., Zelinskaya E. V., Gavrilenko L. V.

Irkutsk National Research Technical University (Irkutsk, Russia):
Burdonov A. E., Associate Professor, Candidate of Engineering Sciences, Associate Professor,
Barakhtenko V. V., Associate Professor, Candidate of Engineering Sciences
Zelinskaya E. V., Professor, Doctor of Engineering Sciences, Professor


RUSAL Engineering and Technology Center LLC (Krasnoyarsk, Russia):
Gavrilenko L. V., Manager, Candidate of Engineering Sciences


The article covers the formation and processing of waste at aluminum production enterprises. Based on the preliminary scientific research conducted, it has been established that the waste processing problem may be solved using the method of photometric separation. The contrast range of the sweepings coarse fraction was studied with the aim to assess the possibility of lump processing and to establish the material composition of the medium-grained fraction of the sweepings in order to evaluate the most contrasting industrial properties required to recover the productive part of the sweepings. An effectiveness study was conducted for material separation and processing indicators. The data obtained were used to develop a photometric separation process for the sweepings that reduces the contamination rates. Photometric separation of a sample with a size class of –20+5 mm allowed isolating an alumina-containing product with a mass fraction of Al 30.14 %, Si 0.41 %, and Fe 0.1 %. The aluminum recovery for the total mass of the sample was 2.76 %. Photometric separation enabled a reduction in the content of Si in the product by a factor of four and of Fe by a factor of 4.7. Separation of a sample with a size class of –50+20 mm yielded an alumina-containing product with a mass fraction of Al 22.24 %, Si 0.41 %, and Fe 0.12 %. The aluminum recovery for the total mass of the sample was 4.58 %. The Si content was reduced by a factor of 2.6 and the content of Fe dropped by a factor of 2.1. The main aluminum losses (84.9 %) are associated with the screenings of photometric separation due to their high yield (74.19 %). The loss of aluminum with the photometric separation tailings was 7.76 %. At the same time, the silicon content in the screenings also decreased by a factor of 1.47 as compared to the initial sweepings.

keywords Aluminum, alumina-containing sweepings, removal of contaminants, contrast, photometric separation, waste processing

1. Sizyakov V. M., Vlasov A. A., Bazhin V. Yu. Strategy tasks of the Russian metallurgical complex. Tsvetnye Metally. 2016. No. 1. pp. 32–37. DOI: 10.17580/tsm.2016.01.05.
2. Sizyakov V. M., Vlasov A. A., Bazhin V. Yu., Feshchenko R. Yu. On the interaction between alumina batch and cryolite-alumina melt. Russian Journal of Non-Ferrous Metals. 2014. Vol. 55, No. 4, pp. 331–335.
3. Burdonov A. E., Zelinskaya E. V., Gavrilenko L. V., Gavrilenko A. A. Investigation of substantial composition of alumina-bearing material of aluminium electrolysers for usage in primary aluminium technology. Tsvetnye Metally. 2018. No. 3. pp. 32–38. DOI: 10.17580/tsm.2018.03.05.
4. Vasyunina N. V., Dubova I. V., Belousov S. V., Sharypov N. A. Recycling of electrolytic aluminum production sweepings. Obogashchenie Rud. 2019. No. 2. pp. 39–44. DOI: 10.17580/or.2019.02.07.
5. Burdonov A. E., Barakhtenko V. V., Zelinskaya E. V., Gavrilenko L. V. Purification of alumina-containing sweepings by dry air classification methods. Izvestiya Vuzov. Tsvetnaya Metallurgiya. 2021. Vol. 27, No. 3. pp. 73–84.
6. Fedotov P. K., Petukhov V. I., Fedotov K. V., Burdonov A. E. The Dzhidinsky tungsten-and-molybdenum mining and concentration complex aged dump tailings processing trends analysis. Obogashchenie Rud. 2016. No. 1. pp. 40–46. DOI: 10.17580/or.2016.01.07.
7. Shepeta E. D., Ignatkina V. A., Samatova L. A. Calcium minerals properties contrast increase in scheelite-carbonate ores flotation. Obogashchenie Rud. 2017. No. 3. pp. 41–48. DOI: 10.17580/or.2017.03.07.
8. Liu S. A., Li S., He Y., Huang F. Geochemical contrasts between early Cretaceous ore-bearing and ore-barren high-Mg adakites in central-eastern China: Implications for petrogenesis and Cu–Au mineralization. Geochimica et Cosmochimica Acta. 2010. Vol. 74, Iss. 24. pp. 7160–7178.
9. Vasyunina N. V., Belousov S. V., Dubova I. V., Morenko A. V., Druzhinin K. E. Recovery of silicon and iron oxides from alumina-containing sweepings of aluminum production. Russian Journal of Non-Ferrous Metals. 2018. Vol. 59, No. 3. pp. 230–236.
10. Lois-Morales P., Evans C., Weatherley D. Characterising tensile strength and elastic moduli of altered igneous rocks at comminution scale using the Short Impact Load Cell. Powder Technology. 2021. Vol. 388. pp. 343–356.
11. Hoang D. H., Ebert D., Möckel R., Rudolph M. Impact of sodium hexametaphosphate on the flotation of ultrafine magnesite from dolomite-rich desliming tailings. Minerals. 2021. Vol. 11, Iss. 5. DOI: 10.3390/min11050499.
12. Khakulov V. A., Krapivskiy E. I., Blayev B. K., Shapovalov V. A. Quality formation technology for Tyrnyauz deposit ores using preliminary sorting and beneficiation. Obogashchenie Rud. 2018. No. 5. pp. 33–39. DOI: 10.17580/or.2018.05.06.
13. Khakulov V. A., Shapovalov V. A., Ignatov M. V., Karpova Z. V. Development of hardware and technical means of control and management for mining and technological research at the stage of mining operations. Proc. of the 2020 IEEE International сonference «Quality management, transport and information security, information technologies», IT and QM and IS 2020. pp. 241–245.
14. Rassulov V. A., Nerushchenko E. V. Laser-photometric lump separation of gold-bearing ore. Obogashchenie Rud. 2020. No. 5. pp. 16–22. DOI: 10.17580/or.2020.05.03.
15. Tsypin E. F., Efremova T. A., Elizarov D. B., Ovchinnikova T. Yu. Connection between X-ray radiometric separation indicators and the size of sorted fractions. Izvestiya Vuzov. Gornyi Zhurnal. 2018. No. 6. pp. 77–84.
16. Tsypin E. F., Ovchinnikova T. Yu. Fractional composition of mineral raw materials and required separation accuracy. Obogashchenie Rud. 2008. No. 3. pp. 28–32.
17. Manard B. T., Bostick D. A., Metzger S. C., Rogers K. T., Hexel C. R. Rapid and automated separation of uranium ore concentrates for trace element analysis by inductively coupled plasma — optical emission spectroscopy/triple quadrupole mass spectrometry. Spectrochimica Acta. Part B: Atomic Spectroscopy. 2021. Vol. 179. DOI: 10.1016/j.sab.2021.106097.
18. Poliakov A., Donskoi E. Separation of touching particles in optical image analysis of iron ores and its effect on textural and liberation characterization. European Journal of Mineralogy. 2019. Vol. 31, Iss. 3. pp. 485–505.
19. Burdonov A. E., Barakhtenko V. V., Zelinskaya E. V., Gavrilenko A. A., Novikov Yu. V. Determination of the value of the sign of alumina-containing raw materials separation to assess the possibility of using the photometric separation method. Scientific foundations and practice of processing ores and man-made raw materials. Materials of the XXV International scientific and technical conference, held within the framework of the XVIII Ural mining decade, April 02–11, 2020, Ekaterinburg. pp. 70–74.
20. Bashlykova T. V., Kalinichenko L. S., Fishchenko Yu.Yu., Makavetskas A. R., Belokrys M. A. Situational mineralogical analysis as a tool for gaining technological knowledge. Ratsionalnoye Osvoyenie Nedr. 2020. No. 6. pp. 24–37.
21. Jiang Q., Dai J., Wang D., Tian S. Application of optical remote sensing to identifying granite pegmatite lithium deposits. Mineral Deposits. 2021. Vol. 40, Iss. 4. pp. 793–804.
22. Lobo A., Garcia E., Barroso G., Martí D., Fernandez-Turiel J.-L., Ibáñez-Insa J. Machine learning for mineral identification and ore estimation from hyperspectral imagery in tin–tungsten deposits: Simulation under indoor conditions. Remote Sensing. 2021. Vol. 13, Iss. 16. DOI: 10.3390/rs13163258.

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