Distribution of iron and sulfur compounds: A case study of hydraulic waste fills

In view of increasingly difficult geological conditions of mining and due to shortage of mineral resources, the man has to find new sources of minerals. In the face of the growing interest in the deep sea mining and development of deposits in the areas of extreme weather, the most promising method to replenish mineral reserves and resources in many countries in the years to come is commercial-level processing of huge mining waste accumulations. Alongside with production of useful components, waste treatment can largely improve the environmental situation in mining and metallurgical regions. In Russia the major constraints of mining waste management are: inefficient regulatory and legal framework, lack of reliable information of the amount, composition and properties of waste, as well as the absence of special investigation methods for mining waste accumulation. Mining and processing waste has some peculiarities which should be taken into account in geochemical analysis, or in estimation of qualitative and quantitative figures on potential mineral raw material. Inclusion of the spatial variability in properties of waste accumulations, in the first turn, hydraulic fills, as well as the use of advance techniques of geostatistics and computer technologies considerably reduces the content of the required analyses, enables modeling of dumps, tailings ponds and other waste accumulations in compliance with the modern standards, and, consequently, allows cutting of total expenses connected with the research. Mineral processing waste (tailings, slag, etc.) are the most promising in terms of recycling. The models of agglomerated cake storage and the estimate of distributions of magnetite, pyrite and sulfur show that in hydraulic filling, despite segregation of materials due to sizing by gravity, some compounds are sufficiently uniformly distributed in a fill owing to the selected technology for formation of particle sizes. At the same time, minerals with high specific density (for example, copper and lead sulfides) tend to accumulating at the pulp slurry outlets. The revealed distribution patterns can improve quality of zoning of waste accumulations in accordance with their chemical composition and, thus, can help detect pockets which are most promising in terms of processing. At the stages of waste processing, these distribution patterns can ensure selection of an optimal processing sequence, and also will contribute to the efficient control over characteristics of mineral feed at all process stages.

Keywords: mining, fills and dumps, mining waste, geostatistics, waste processing, hydraulic fill, weighted distance discriminant approach, sulfides, tailings pond.
For citation:

Cheskidov V. V., Barabanov N. N., Lozhkin M. O., Smirnov P.A., Lagutina A. A. Distribution of iron and sulfur compounds: A case study of hydraulic waste fills. MIAB. Mining Inf. Anal. Bull. 2021;(3):142-153. [In Russ]. DOI: 10.25018/0236-1493-2021-3-0-142-153.

Issue number: 3
Year: 2021
Page number: 142-153
ISBN: 0236-1493
UDK: 550.8.053
DOI: 10.25018/0236-1493-2021-3-0-142-153
Article receipt date: 13.05.2020
Date of review receipt: 16.09.2020
Date of the editorial board′s decision on the article′s publishing: 10.02.2021
About authors:

V.V. Cheskidov1, Cand. Sci. (Eng.), Assistant Professor, Deputy Director of the College of Mining, e-mail: vcheskidov@misis.ru,
N.N. Barabanov1, Graduate Student; Geologist, Geosolutions, 119590, Moscow, Russia, e-mail: m1605561@edu.misis.ru,
M.O. Lozhkin1, Graduate Student,
P.A. Smirnov1, Graduate Student; Technical Support Engineer, Orika CIS, 125315, Moscow, Russia,
A.A. Lagutina1, Graduate Student,
1 Mining Institute, National University of Science and Technology «MISiS», 119049, Moscow, Russia


For contacts:

N.N. Barabanov, e-mail: m1605561@edu.misis.ru.


1. Kuznetsov Yu. N., Stadnik D. A., Stadnik N. M., Kakorina N. M., Volkov S. S.Quality improvement in forecasting geological information in automated mine planning and design for stratified deposits. MIAB. Mining Inf. Anal. Bull. 2016, no 3, pp. 164—171. [In Russ].

2. Li R., Wang G., Carranza E. J. M. GeoCube: A 3D mineral resources quantitative prediction and assessment system. Computers & Geosciences. 2016. Vol. 89. Pp. 161—173. DOI: 10.1016/j.cageo.2016.01.012.

3. Cheskidov V., Kassymkanova K.-K., Lipina A., Bornman M. Modern methods of monitoring and predicting the state of slope structures. E3S Web of Conferences. 2019. Vol. 105. Article 01001. DOI: 10.1051/e3sconf/201910501001.

4. Cheskidov V. V., Lipina A. V., Melnichenko I. A. Integrated monitoring of engineering structures in mining. Eurasian Mining. 2018. Vol 2. Pp. 18—21. DOI 10.17580/em.2018.02.05.

5. Antonov V. A. Methodology of geoinformation display experimental mining-technological regularities. MIAB. Mining Inf. Anal. Bull. 2017, no 10, pp. 17—24. [In Russ]. DOI: 10.25018/0236-1493-2017-10-0-17-24.

6. Dem'yanov V. V., Savel'eva E. A. Geostatistika: teoriya i praktika [Geostatistics: Theory and practice], Moscow, Nauka, 2010, 327 p.

7. Geostatisticheskie metody v otsenke zapasov mineral'nogo syr'ya. Tezisy dokladov 2-go Vsesoyuznogo seminara po geostatistike, Petrozavodsk, 1—5 oktyabrya 1990 g. [Geostatistical methods in appraisal of mineral reserves. Proceedings of the 2nd All-Union Workshop on Geostatistics. Petrozavodsk, 1–5 October, 1990, Petrozavodsk, October 1—5, 1990], Petrozavodsk, 1990, 89 p. [In Russ].

8. Kurguzov K. V. Stokhasticheskoe modelirovanie litotekhnicheskikh sistem [Stochastic modeling of litho-technical systems], Candidate’s thesis, Moscow, RGGU, 2019, 161 p.

9. Strizhenok A. V., Ivanov A. V. An advanced technology for stabilizing dust producing surfaces of built-up technogenic massifs during their operation. Power Technology and Engineering. 2016. Vol. 50. Pp. 240—243. DOI: 10.1007/s10749-016-0690-y.

10. Bystrov V. P., Vernigora A. S., Kamkin R. I., Mamaev A. Y., Kuznetsov A. V., Paretsky V. M. Vanukov furnace technology: Application experience for processing different types of raw materials and general development trends. TMS Annual Meeting. 2nd International Symposium on High-Temperature Metallurgical Processing. 2011. Pp. 59—66. DOI: 10.1002/9781118062081.ch8.

11. Zawadzki J., Szuskiewicz M., Fabijanczyk P., Magiera T. Geostatistical discrimination between different sources of soil pollutants using a magneto-geochemical data set. Chemosphere. 2016. Vol. 164. Pp. 668—676. DOI: 10.1016/j.chemosphere.2016.08.145.

12. Zuo R., Carranza J. Geoinformatics in applied geochemistry. Journal of Geochemical Exploration. 2016. Vol. 164. Pp. 1—2. DOI: 10.1016/j.gexplo.2016.03.003.

13. Singh P., Verma P. A comparative study of spatial interpolation technique (IDW and kriging) for determining groundwater quality. GIS and Geostatistical Techniques for Groundwater Science. Chapter 5. 2019. Pp. 43—56. DOI: 10.1016/B978-0-12-815413-7.00005-5.

14. Bech J., Bini C., Pashkevich M. A. Assessment, restoration and reclamation of mining influenced soils. London: Academic Press, 2017. 497 p.

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