Development of a method for calculating statistical characteristics of the input material flow of a transport conveyor

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.289931

Keywords:

transport conveyor, similarity criteria, statistical characteristics, correlation function, material flow typification

Abstract

The object of this study is the material flow incoming the conveyor. The actual problem of calculating the stochastic characteristics of the input material flow of a transport system, based on the typification of the input material flow, is being solved. When constructing a model of the input material flow, methods of similarity theory were used. A criterion has developed for dividing the realization of the input material flow into a deterministic and stochastic component, which makes it possible to represent the stochastic component of the input flow in the form of an realization of a centered ergodic process. A method is presented for calculating amplitude and phase frequency spectra for the components of the input material flow, based on specified types of theoretical correlation functions. The calculating accuracy of the normalized correlation function values is e~0.05. Distinctive features of the obtained results are that the typification method of the input material flow is based on the use of the amplitude spectrum for the input material flow. A special feature of the results obtained is that a single realization of the input material flow was used to model the input material flow. The scope of application of the obtained results is the mining industry. The developed methodology for calculating the statistical characteristics of the input material flow allow to improve the accuracy of algorithms for optimal control of the flow parameters of the transport system for a mining enterprise. The condition for the practical application of the obtained results is the presence in the sections of the transport conveyor of measuring sensors that determine the speed of the belt and the amount of material in the bunker

Author Biographies

Oleh Pihnastyi, National Technical University "Kharkiv Polytechnic Institute"

Doctor of Technical Sciences, Professor

Department of Distributed Information Systems and Cloud Technologies

Dmytro Kudii, National Technical University "Kharkiv Polytechnic Institute"

PhD, Associate Professor

Department of Cybersecurity

References

  1. Siemens – innovative solutions for the mining industry. Available at: https://im-mining.com/advertiser_profile/siemens-innovative-solutions-mining-industry/
  2. Pihnastyi, O., Ivanovska, O. (2022). Improving the prediction quality for a multi-section transport conveyor model based on a neural network. Proceedings of International Scientific Conference Information Technology and Implementation, 3132, 24–38. Available at: http://ceur-ws.org/Vol-3132/Paper_3.pdf
  3. Bajda, M., Błażej, R., Jurdziak, L. (2019). Analysis of changes in the length of belt sections and the number of splices in the belt loops on conveyors in an underground mine. Engineering Failure Analysis, 101, 436–446. doi: https://doi.org/10.1016/j.engfailanal.2019.04.003
  4. Koman, M., Laska, Z. (2014). The constructional solution of conveyor system for reverse and bifurcation of the ore flow, Rudna mine KGHM Polska Miedź SA. CUPRUM, 3 (72), 69–82.
  5. Pihnastyi, O., Khodusov, V. (2020). Development of the controlling speed algorithm of the conveyor belt based on TOU-tariffs. Proceedings of the 2nd International Workshop on Information-Communication Technologies & Embedded Systems, 2762, 73–86. Available at: https://mpra.ub.uni-muenchen.de/104681/
  6. Halepoto, I. A., Shaikh, M. Z., Chowdhry, B. S., Uqaili, M. u hammad A. (2016). Design and Implementation of Intelligent Energy Efficient Conveyor System Model Based on Variable Speed Drive Control and Physical Modeling. International Journal of Control and Automation, 9 (6), 379–388. doi: https://doi.org/10.14257/ijca.2016.9.6.36
  7. He, D., Pang, Y., Lodewijks, G., Liu, X. (2018). Healthy speed control of belt conveyors on conveying bulk materials. Powder Technology, 327, 408–419. doi: https://doi.org/10.1016/j.powtec.2018.01.002
  8. Korniienko, V. I., Matsiuk, S. M., Udovyk, I. M. (2018). Adaptive optimal control system of ore large crushing process. Radio Electronics, Computer Science, Control, 1, 159–165. doi: https://doi.org/10.15588/1607-3274-2018-1-18
  9. Kiriia, R., Shyrin, L. (2019). Reducing the energy consumption of the conveyor transport system of mining enterprises. E3S Web of Conferences, 109, 00036. doi: https://doi.org/10.1051/e3sconf/201910900036
  10. Pihnastyi, O., Kozhevnikov, G., Khodusov, V. (2020). Conveyor Model with Input and Output Accumulating Bunker. 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT). doi: https://doi.org/10.1109/dessert50317.2020.9124996
  11. Kawalec, W., Król, R. (2021). Generating of Electric Energy by a Declined Overburden Conveyor in a Continuous Surface Mine. Energies, 14 (13), 4030. doi: https://doi.org/10.3390/en14134030
  12. Curtis, A., Sarc, R. (2021). Real-time monitoring of volume flow, mass flow and shredder power consumption in mixed solid waste processing. Waste Management, 131, 41–49. doi: https://doi.org/10.1016/j.wasman.2021.05.024
  13. Stadnik, N. (2012). Frequency-Controlled Electric Drive of Band Conveyors Based on Self-Ventilating Engines. Scientific Bulletin of the Donetsk National Technical University, 2, 226–232.
  14. Bhadani, K., Asbjörnsson, G., Hulthén, E., Hofling, K., Evertsson, M. (2021). Application of Optimization Method for Calibration and Maintenance of Power-Based Belt Scale. Minerals, 11 (4), 412. doi: https://doi.org/10.3390/min11040412
  15. Carvalho, R., Nascimento, R., D’Angelo, T., Delabrida, S., G. C. Bianchi, A., Oliveira, R. A. R. et al. (2020). A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry. Sensors, 20 (8), 2243. doi: https://doi.org/10.3390/s20082243
  16. Vasić, M., Miloradović, N., Blagojević, M. (2021). Speed control of high power multiple drive belt conveyors. IMK-14 - Istrazivanje i Razvoj, 27 (1), 9–15. doi: https://doi.org/10.5937/imk2101009v
  17. Pihnastyi, O., Burduk, A. (2022). Analysis of a Dataset for Modeling a Transport Conveyor. Proceedings of the 2nd International Workshop on Information Technologies: Theoretical and Applied Problems (ITTAP 2022), 3309, 319–328. Available at: https://ceur-ws.org/Vol-3309/paper20.pdf
  18. Pihnastyi, O. M. (2018). Statistical theory of control systems of the flow production. LAP LAMBERT Academic Publishing, 436.
  19. Azarenkov, N., Pihnastyi, O., Khodusov, V. (2011). To the question of similarity of technological processes of production and technical systems. Reports of the National Academy of Sciences of Ukraine, 2, 29–35.
Development of a method for calculating statistical characteristics of the input material flow of a transport conveyor

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Published

2023-10-31

How to Cite

Pihnastyi, O., & Kudii, D. (2023). Development of a method for calculating statistical characteristics of the input material flow of a transport conveyor. Eastern-European Journal of Enterprise Technologies, 5(2 (125), 87–96. https://doi.org/10.15587/1729-4061.2023.289931