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of the National Academy of Sciences of Ukraine

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A model of investigation of change of inventories for optimization multi-parameter processes of mining production

A.A. Khorolskyi1*

1Branch for Physics of Mining Processes of the M.S. Poliakov Institute of Geotechnical Mechanics the National Academy of Sciences of Ukraine, Dnipro, Ukraine

*Corresponding author: e-màil: andreykh918@gmail.com

 

Physical and technical problems of mining production, 2023, (25), 153-175.

 

https://doi.org/10.37101/ftpgv25.01.012

full text (pdf)

 

ABSTRACT

 

Purpose. Develop a new approach to the design of processes that accompany the development of mineral deposits. This will allow considering the multiplicity and different degree of influence of the parameters on the overall efficiency.

Methods. A decomposition approach is applied to optimize multi-parameter processes of mining production. Network models and optimization algorithms on networks and graphs for finding the optimal strategy for the development of mineral deposits. A model of the study of changes in the state of reserves for the design of technological processes that accompany the extraction of minerals.

Results. The method of optimization of the parameters of the development of mineral deposits was proposed based on the presentation of alternative options of technological processes in the form of a network model. Depending on the type of mineral, as well as the expediency of mining waste enrichment, two design strategies are proposed. If it is advisable to enrich the waste, then it is necessary to maximize the value of a ton of rock mass. If it is impractical to enrich mining waste, it is necessary to minimize the costs of mining a ton of mineral. To increase the efficiency of exploitation of ore deposits of minerals, it is proposed to use mining waste as a component mixture for paving the produced space. The volume of work on the establishment of the developed space is determined on the basis of marginal analysis. In order to increase the efficiency of the operation of coal deposits, it is proposed to minimize the amount of waste that comes to the surface. Alternative options for the exploitation of the coal deposit were considered and two strategies were proposed: one consists in the minimization of waste, which involves selective extraction and laying of the produced space, and the other strategy consists in the maximization of the extraction of associated minerals, which involves combine extraction, separation of cargo flows, additional enrichment of waste.

Scientific novelty. Scientific novelty consists in the development of a new approach, as well as in the creation of models for the development of mineral deposits. The algorithm for designing the process of development of mineral deposits is given. If it is impractical to enrich mining waste, then we apply the programming of the alternative graph to the minimum, which will minimize the cost of extracting 1 ton of useful mineral, in the other case, the programming of the alternative graph to the maximum, which will allow to maximize the cost of 1 ton of mining mass. Fulfillment of these conditions will increase the efficiency of production and reduce the man-made load on the environment.

Practical implication. It consists in the creation of a package of application programs for designing the processes of development of mineral deposits.

Keywords: strategy, production waste, design, ecology, technological scheme, graph, software.

 

REFERENCES

 

1. Amosha, O.I., Salli, V.I., Tryfonova, O.V., & Symonenko, O.I. (2007). Kilʹkisni parametry investytsiynoyi pryvablyvosti vuhilʹnykh shakht. Dnipropetrovsʹk: NHU. 110 p. (in Ukrainian)

2.           Huang, S., Li, G., Ben-Awuah, E., Afum, B. O., & Hu, N. (2020). A robust mixed integer linear programming framework for underground cut-and-fill mining production scheduling. International Journal of Mining, Reclamation and Environment, 34(6), 397-414. https://doi.org/10.1080/17480930.2019.1576576

3.           Khodayari, F., & Pourrahimian, Y. (2016, October). Quadratic programming application in block-cave mining. In 1st International Conference on underground Mining, Santiago, Chile (pp. 427-438).

4.           Topal, J. L. E. (2011). Strategies to assist in obtaining an optimal solution for an underground mine planning problem using Mixed Integer Programming. International Journal of Mining and Mineral Engineering, 3(2), 152-172. https://doi.org/10.1504/IJMME.2011.042429

5.           MacNeil, J.A., & Dimitrakopoulos, R.G. (2017). A stochastic optimization formulation for the transition from open pit to underground mining. Optimization and Engineering, 18, 793-813. https://doi.org/10.1007/s11081-017-9361-6

6.           Khorolskyi, A., Hrinov, V., & Kaliushenko, O. (2019). Network models for searching for optimal economic and environmental strategies for field development. Procedia Environmental Science, Engineering and Management, 6(3), 463-471.

7.           Balezentis, T., Streimikiene, D., & Siksnelyte-Butkiene, I. (2021). Energy storage selection for sustainable energy development: The multi-criteria utility analysis based on the ideal solutions and integer geometric programming for coordination degree. Environmental Impact Assessment Review, 91, 106675. https://doi.org/10.1016/j.eiar.2021.106675

8.           Hill, A., Brickey, A. J., Cipriano, I., Goycoolea, M., & Newman, A. (2022). Optimization Strategies for Resource-Constrained Project Scheduling Problems in Underground Mining. INFORMS Journal on Computing, 34(6), 3042-3058. https://doi.org/10.1287/ijoc.2022.1222

9.           Özyurt, M.C., & Karadogan, A. (2020). A new model based on artificial neural networks and game theory for the selection of underground mining method. Journal of Mining Science, 56, 66-78. https://doi.org/10.1134/S1062739120016491

10.         Khorolskyi, A., Hrinov, V., Mamaikin, O., & Fomychova, L. (2020). Research into optimization model for balancing the technological flows at mining enterprises. In E3S Web of Conferences (Vol. 201, p. 01030). EDP Sciences. https://doi.org/10.1051/e3sconf/202020101030

11.         Petlovanyi, M., Sai, K., Malashkevych, D., Popovych, V., & Khorolskyi, A. (2023, April). Influence of waste rock dump placement on the geomechanical state of underground mine workings. In IOP Conference Series: Earth and Environmental Science (Vol. 1156, No. 1, p. 012007). IOP Publishing. DOI 10.1088/1755-1315/1156/1/012007

12.         Kwinta, A., & Gradka, R. (2020). Analysis of the damage influence range generated by underground mining. International Journal of Rock Mechanics and Mining Sciences, 128, 104263. https://doi.org/10.1016/j.ijrmms.2020.104263

13.         Vayenas, N., & Peng, S. (2014). Reliability analysis of underground mining equipment using genetic algorithms: A case study of two mine hoists. Journal of Quality in maintenance Engineering, 20(1), 32-50. https://doi.org/10.1108/JQME-02-2013-0006

14.         Zarębska, K., Baran, P., Cygankiewicz, J., & Dudzińska, A. (2012). Prognosticating fire hazards in goafs in Polish collieries. AGH Drilling, Oil, Gas, 29(4).

15.         Yueze, L., Akhtar, S., Sasmito, A. P., & Kurnia, J. C. (2017). Prediction of air flow, methane, and coal dust dispersion in a room and pillar mining face. International Journal of Mining Science and Technology, 27(4), 657-662. https://doi.org/10.1016/j.ijmst.2017.05.019

16.         Bazaluk, O., Ashcheulova, O., Mamaikin, O., Khorolskyi, A., Lozynskyi, V., & Saik, P. (2022). Innovative activities in the sphere of mining process management. Frontiers in Environmental Science, 304. https://doi.org/10.3389/fenvs.2022.878977

17.         Hrinov, V., & Khorolskyi, A. (2018). Improving the process of coal extraction based on the parameter optimization of mining equipment. In E3S Web of Conferences (Vol. 60, p. 00017). EDP Sciences. https://doi.org/10.1051/e3sconf/20186000017

18.         Saaty, T.L., Vargas, L.G., Saaty, T.L., & Vargas, L.G. (2013). The analytic network process (pp. 1-40). Springer US.

19.         Balusa, B.C., & Singam, J. (2018). Underground mining method selection using WPM and PROMETHEE. Journal of the Institution of Engineers (India): Series D, 99, 165-171. https://doi.org/10.1007/s40033-017-0137-0

20.         Balusa, B. C., & Gorai, A. K. (2019). A comparative study of various multi-criteria decision-making models in underground mining method selection. Journal of The Institution of Engineers (India): Series D, 100, 105-121. https://doi.org/10.1007/s40033-018-0169-0

21.         Liang, W., Zhao, G., Wu, H., & Chen, Y. (2019). Assessing the risk degree of goafs by employing hybrid TODIM method under uncertainty. Bulletin of Engineering Geology and the Environment, 78, 3767-3782. https://doi.org/10.1007/s10064-018-1340-4

22.         Pak, M. C., Han, U. C., & Kim, D. I. (2022). Suitable Mining Method Selection using HFGDM-TOPSIS Method: a Case Study of an Apatite Mine. Journal of Mining and Environment, 13(2), 357-374. https://doi.org/10.22044/jme.2022.11713.2163

23.         Alavi, I., & Alinejad-Rokny, H. (2011). Comparison of Fuzzy AHP and Fuzzy TOPSIS methods for plant species selection (case study: reclamation plan of sungun Copper Mine; Iran). Australian journal of basic and applied sciences, 5(12), 1104-1113.

24.         Sahoo, S., Dhar, A., Kar, A., & Ram, P. (2017). Grey analytic hierarchy process applied to effectiveness evaluation for groundwater potential zone delineation. Geocarto International, 32(11), 1188-1205. https://doi.org/10.1080/10106049.2016.1195888

25.         Yang, W., Xia, X., Pan, B., Gu, C., & Yue, J. (2016). The fuzzy comprehensive evaluation of water and sand inrush risk during underground mining. Journal of Intelligent & Fuzzy Systems, 30(4), 2289-2295. DOI: 10.3233/IFS-151998

26.         Pérez, J., Jimeno, J. L., & Mokotoff, E. (2006). Another potential shortcoming of AHP. Top, 14, 99-111. https://doi.org/10.1007/BF02579004

27.         Paravarzar, S., Pourrahimian, Y., Askari-Nasab, H., & Emery, X. (2021). Short-term underground mine planning: a review. International Journal of Mining and Mineral Engineering, 12(1), 1-33. https://doi.org/10.1504/IJMME.2021.114902

28.         Reinhart, R., Dang, T., Hand, E., Papachristos, C., & Alexis, K. (2020, May). Learning-based path planning for autonomous exploration of subterranean environments. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1215-1221). IEEE. https://doi.org/10.1109/ICRA40945.2020.9196662

29.         Li, S., Huang, Q., Hu, B., Pan, J., Chen, J., Yang, J., & Yu, H. (2023). Mining method optimization of difficult-to-mine complicated orebody using Pythagorean fuzzy sets and TOPSIS method. Sustainability, 15(4), 3692. https://doi.org/10.3390/su15043692

30.         Erdogan, G., & Yavuz, M. (2017, December). Application of Three Existing Stope Boundary Optimisation Methods in an Operating Underground Mine. In IOP Conference Series: Earth and Environmental Science (Vol. 95, No. 4, p. 042077). IOP Publishing. DOI 10.1088/1755-1315/95/4/042077

31.         Brazil, M., & Grossman, P. (2008, September). Access layout optimization for underground mines. In Australian Mining Technology Conference, Queensland, 119-128.

32.         Emdini Gliwan, S., & Crowe, K. (2022). A Network Flow Model for Operational Planning in an Underground Gold Mine. Mining, 2(4), 712-724. https://doi.org/10.3390/mining2040039

33.         Bellman, R.E. (2010). Dynamic programming. Princeton university press. https://doi.org/10.1515/9781400835386

34.         Burfield, C. (2013). Floyd-Warshall algorithm. Massachusetts Institute of Technology.

35.         Weber, A., Kreuzer, M., & Knoll, A. (2020, May). A generalized Bellman-Ford algorithm for application in symbolic optimal control. In 2020 European Control Conference (ECC) (pp. 2007-2014). IEEE. https://doi.org/10.23919/ECC51009.2020.9143743

36.         Broumi, S., Bakal, A., Talea, M., Smarandache, F., & Vladareanu, L. (2016, November). Applying Dijkstra algorithm for solving neutrosophic shortest path problem. In 2016 International conference on advanced mechatronic systems (ICAMechS) (pp. 412-416). IEEE. https://doi.org/10.1109/ICAMechS.2016.7813483

37.         Khorolskyi, A.O. (2021). Naukovi osnovy obgruntuvannia mezh oblasti ratsionalnoho proiektuvannia pry vidpratsiuvanni rodovyshch korysnykh kopalyn. Fyzyko-tekhnycheskye problemy hornoho proyzvodstva, (23), 149-173. (In Ukrainian). https://doi.org/10.37101/ftpgp23.01.011

38.         Khorolskyi, A.O. (2022). Rezulʹtaty doslidzhenʹ iz rozrobky systemy pidtrymky pryynyattya rishenʹ dlya proyektuvannya protsesiv osvoyennya rodovyshch korysnykh kopalyn. Journal of Donetsk Mining Institute, 2(51), 122-135. https://doi.org/10.31474/1999‐981X‐2022‐2‐122‐135  (in Ukrainian)

39. Hrinov V.H., Khorolskyi A.O. (2022). Vyznachennya dotsilʹnosti vidpratsyuvannya rodovyshch na stadiyi peredproyektnykh doslidzhenʹ ratsionalʹnoyi stratehiyi yikh osvoyennya. Mineralni resursy Ukrayiny, ¹2, pp. 12-17. https://doi.org/10.31996/mru.2022.2.12-17

40. Khorolskyi A.O. (2023). Proyektuvannya protsesiv osvoyennya rodovyshch korysnykh kopalyn na osnovi doslidzhennya zminy stanu zapasiv. Naukovi pratsi DonNTU. Seriya «Hirnycho-heolohichna», 1(29), 83-97. https://doi.org/10.31474/2073-9575-2023-1(29)-83-97

 

 

 

 

 

 

 

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