Digital system of quarry management as a SAAS solution: Mineral Deposit Module
M. Zarubin1, V. Zarubina1, E. Fionin1, B. Salykov2, O. Salykova2
1Rudny Industrial Institute, Rudny, Kazakhstan
2Kostanay State University named after A. Baytursinov, Kostanay, Kazakhstan
Min. miner. depos. 2019, 13(2):91-102
https://doi.org/10.33271/mining13.02.091
Full text (PDF)
      ABSTRACT
      Purpose. Improving the efficiency of functioning the mining enterprises and aggregation of earlier obtained results into a unified digital system of designing and operative management by quarry operation.
      Methods. Both the traditional (analysis of scientific and patent literature, analytical methods of deposit parameters research, analysis of experience and exploitation of quarries, conducting the passive experiment and processing the statistical data) and new forms of scientific research - deposit modeling on the basis of classical and neural network methods of approximation – are used in the work. For the purpose of the software product realization on the basis of cloud technologies, there were used: for back-end implementation – server-based scripting language php; for the front-end – multi-paradigm programming language javascript, javascript framework jQuery and asynchronous data exchange technology Ajax.
      Findings. The target audience of the system has been identified, SWOT-analysis has been carried out, conceptual directions of 3D-quarry system development have been defined. The strategies of development and promotion of the software product, as well as the strategies of safety and reliability of the application both for the client and the owner of the system have been formulated. The modular structure of the application has been developed, and the system functions have been divided to implement both back-end and front-end applications. The Mineral Deposit Module has been developed: the geological structure of the deposit has been simulated and its block model has been constructed. It has been proved that the use of neural network algorithms does not give an essential increase in the accuracy of the block model for the deposits of 1 and 2 groups in terms of the geological structure complexity. The possibility and prospects of constructing the systems for subsoil users on the basis of cloud technologies and the concept of SaaS have been substantiated.
      Originality.For the first time, the modern software products for solving the problems of designing and operational management of mining operations have been successfully developed on the basis of the SaaS concept.
      Practical implications. The results are applicable for enterprises-subsoil users, working with deposits of 1 and 2 groups in terms of the geological structure complexity: design organizations, as well as mining and processing plants.
      Keywords: 3D-quarry, cloud technologies, strategies of information system development, block model of the deposit, data approximation, artificial neural networks
      REFERENCES
Calistratov, T.A. (2014). Methods and algorithms of neural network structure creation in the context of universal function approximation. Bulletin of TSU, 19(6), 1845-1848.
Gérault, D., Lafourcade, P., Minier, M., & Solnon, C. (2018). Revisiting AES related-key differential attacks with constraint programming. Information Processing Letters, (139), 24-29.
https://doi.org/10.1016/j.ipl.2018.07.001
Gu, Q., Lu, C., Guo, J., & Jing, S. (2010). Dynamic management system of ore blending in an open pit mine based on GIS/GPS/GPRS. Mining Science and Technology (China), 20(1), 132-137.
https://doi.org/10.1016/s1674-5264(09)60174-5
Imamoto, A., & Tang, B. (2008). Optimal piecewise linear approximation of convex functions. Proceedings of the World Congress on Engineering and Computer Science.
Lee, D. (2004). New dimensions in mining software. Institute of Materials, Minerals and Mining (IOM), Materials World, 12(9), 31-33.
Lin, G.-F., & Chen, L.-H. (2004). A spatial interpolation method based on radial basis function networks incorporating a semivariogram model. Journal of Hydrology, 288(3-4), 288-298.
https://doi.org/10.1016/j.jhydrol.2003.10.008
Liu, S., Zhang, Y., Ma, P., Lu, B., & Su, H. (2011). A novel spatial interpolation method based on the integrated RBF neural network. Procedia Environmental Sciences, (10), 568-575.
https://doi.org/10.1016/j.proenv.2011.09.092
Nguyen-Thien, T., & Tran-Cong, T. (1999). Approximation of functions and their derivatives: a neural network implementation with applications. Applied Mathematical Modelling, 23(9), 687-704.
https://doi.org/10.1016/s0307-904x(99)00006-2
Oliver, M.A. (2004). Practical Geostatistics: modelling and spatial analysis. International Journal of Geographical Information Science, 18(1), 102-103.
https://doi.org/10.1080/13658810310001620852
“Organizations” Module. (2019). [online]. Available at:
https://kazdata.kz/01/service-organization.html
Pivniak, H.H., Pilov, P.I., Pashkevych, M.S., & Shashenko, D.O. (2012). Synchro-mining: civilized solution of problems of mining regions’ sustainable operation. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 131-138.
Results of EMS for 2016. (2016). Retrieved from
http://economy.gov.kz/ru/pages/itogi-ser-za-2016-god
Results of EMS for 2017. (2017). Retrieved from
http://economy.gov.kz/ru/pages/itogi-ser-za-2017-god
Results of EMS for 2018. (2018). Retrieved from
http://economy.gov.kz/ru/pages/itogi-ser-za-2018-god
Shafizadeh-Moghadam, H., Hagenauer, J., Farajzadeh, M., & Helbich, M. (2015). Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study. International Journal of Geographical Information Science, 29(4), 606-623.
https://doi.org/10.1080/13658816.2014.993989
Vagonova, O.G., & Volosheniuk, V.V. (2012). Mining enterprises’ economic strategies as derivatives of nature management in the system of social relations. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (2), 127-134.
Zarubin, M., Statsenko, L., Zarubina, V., & Fionin, E. (2017). Developing information systems of operation schedules to stabilize the grade of a mineral. Mining of Mineral Deposits, 11(4), 59-70.
https://doi.org/10.15407/mining11.04.059
Zarubin, M.Yu., & Zarubina, V.R. (2013). The use of artificial neural networks to control the technological processes of iron-enrichment complex. Artificial Intelligence, 4(62), 520-529.