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Automation of the control process by the shearer drum in terms of coal seam hypsometry

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Category: Content №3 2021

Last Updated on 23 June 2021

Published on 30 November -0001

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Authors:

A.V.Bublikov, orcid.org/0000-0003-3015-6754, Dnipro University of Technology, Dnipro, Ukraine, email: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Tkachov, orcid.org/0000-0002-2079-4923, Dnipro University of Technology, Dnipro, Ukraine, email: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.L.Kolosov, orcid.org/0000-0003-0585-5908, Dnipro University of Technology, Dnipro, Ukraine, email: This email address is being protected from spambots. You need JavaScript enabled to view it.

G.Gruhler, orcid.org/0000-0002-3624-5259, Reutlingen University, Reutlingen, Federal Republic of Germany, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.I.Stadnik, orcid.org/0000-0003-2109-6219, Vinnytsia National Agrarian University, Vinnytsia, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, (3): 005 - 013

https://doi.org/10.33271/nvngu/2021-3/005

Abstract:

Purpose. To develop a method for synthesizing a fuzzy automatic control system for a shearer drum in terms of coal seam hypsometry basing on the information criterion of the beginning of rock cutting-off by the drum to reduce ash content of the extracted coal.

Methodology. Taking into consideration peculiarities of determining a distinct information criterion of the beginning of rock cutting-off by the drum and regularities of its variations during the shearer operation, a fuzzy inference algorithm is developed for a system of fuzzy automatic drum control in terms of seam hypsometry. In this context, rules of fuzzy productions, parameters of the membership functions of terms of the output linguistic variable system, and fuzzy operations are substantiated according to the recommendations of a classic Mamdani fuzzy inference algorithm. Studies are carried out to analyze the efficiency of the proposed fuzzy inference algorithm basing on the introduced relative parameter of the number of effective control actions formed by the fuzzy control system. Simulation modeling makes it possible to perform comparative analysis of the efficiency of the drum control.

Findings. In the course of research, an algorithm of fuzzy control of the shearers upper drum in terms of coal seam hypsometry has been developed basing on the determination of direct and inverse transfer from coal breaking near the seam roof by the shearer drum to rock breaking with the help of statistical analysis of the stator power of a cutting drive motor.

Originality. For the first time, a method of synthesis of fuzzy automatic control of the drum in terms of seam hypsometry has been proposed.

Practical value. The proposed method is the theoretical basis to solve important scientific and applied problem of the automation of the coal shearer drum in terms of seam hypsometry to reduce ash content of the produced coal.

Keywords: coal shearer, fuzzy inference algorithm, coal seam hypsometry, coal ash content

References.

1.Stadnik, M., Semenchenko, D., Semenchenko, A., Belytsky, P., Virych, S., & Tkachov, V. (2019). Improving energy efficiency of coal transportation by adjusting the speeds of a combine and a mine face conveyor. Eastern-European Journal of Enterprise Technologies, 1, 60-70. https://doi.org/10.15587/1729-4061.2019.156121.

2.Makowski, P., & Tymoshenko, Ye. (2018). The quality of coal in Poland, Russia and Ukraine and its effect on dust emission. Technical transactions, 9, 1-21. https://doi.org/10.4467/2353737xct.18.138.8977.

3.Samorodov, V., Bondarenko, A., Taran, I., & Klymenko, I. (2020). Power flows in a hydrostatic-mechanical transmission of a mining locomotive during the braking process. Transport Problems, 15, 17-28. https://doi.org/10.21307/tp-2020-030.

4.Dinh, V.Ch., Nguyen, K.L., Nguyen, V.X., & Le, T.B. (2020). Influence of the winding angle of auger shearer-loader on the efficiency of coal loading on the downhole conveyor. Ugol, (1), 46-50. https://doi.org/10.18796/0041-5790-2020-1-46-50.

5. Sdvyzhkova, O., Golovko, Y., Dubytska, M., & Klymenko,D. (2016). Studying a crack initiation in terms of elastic oscillations in stress strain rock mass. Mining of Mineral Deposits, 10(2), 72-77. https://doi.org/10.15407/mining10.02.072.

6.Sdvyzhkova, O., Babets, D., Moldabayev, S., Rysbekov, K., & Sarybayev, M. (2020). Mathematical modeling a stochastic variation of rock properties at an excavation design. International Multidisciplinary Scientific GeoConference: SGEM, 20(1.2), https://doi.org/10.5593/sgem2020/1.2/s03.021.

7.Tron, V., Tsokurenko, O., Paraniuk, D., & Haponenko, I. (2019). Formation of the adaptive fuzzy model of the rock geological structure for exploratory drilling. E3S Web of Conferences, 123, 01037. https://doi.org/10.1051/e3sconf/201912301037.

8.Morkun, V., Morkun, N., Tron, V., Paraniuk, D., & Sulyma, T. (2019). Adaptive control of drilling by identifying parameters of object model under nonstationarity conditions. Mining of Mineral Deposits, 14, 100-106. https://doi.org/10.33271/mining14.01.100.

9. Sinchuk, O., Kupin, A., Sinchuk, I., Dozorenko, O., & Krasnopolsky, R. (2020). Algorithms Design for Fuzzy Control by Power Streams in Conditions of Underground Extraction of Iron Ore. 2020 IEEE 7^th International Conference on Energy Smart Systems (ESS). 330334. https://doi.org/10.1109/ESS50319.2020.9160168.

10. Bublikov, A.V., & Tkachov, V.V. (2019). Automation of the control process of the mining machines based on fuzzy logic. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 112-118. https://doi.org/10.29202/nvngu/2019-3/19.

11.Xu, J., Wang, Z., Tan, C., Si, L., & Liu, X. (2018). Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence-Based Variable Translation Wavelet Neural Network. Sensors (Basel, Switzerland), 18(2), 382. https://doi.org/10.3390/s18020382.

12.Si, L., Wang, Z., Liu, X., Tan, C., Liu, Z., & Xu, J. (2016). Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm. Sensors (Basel, Switzerland), 16(1), 90. https://doi.org/10.3390/s16010090.

13.Zhou, X., Wang, Z., Ji, R., & Liu, X. (2015). A Novel Approach for Shearer Memory Cutting Based on Fuzzy Optimization Method. Advances in Mechanical Engineering, 5, 319272-319272. https://doi.org/10.1155/2013/319272.

14.Bublikov, A.V. (2019). Algorithm for fuzzification of the input value of the fuzzy control system of the executive body of the mining combine by hypsometry of coal seam. Collection of scientific works of the National Mining University, (57), 114-129. https://doi.org/10.33271/crpnmu/57.114.

15.Ge, S., Zhao, R., Li, W., Li, J., Liu, Y., & Wang, Z. (2020). Sliding-mode control for coal shearer drum height adjustment based on variable speed reaching law. Journal of Vibroengineering, 22(8), 1782-1797. https://doi.org/10.21595/jve.2020.21223.

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