Improvement of methods for description of a three-bunker collection conveyor

Pihnastyi, Oleh and Chernіavska, Svіtlana (2022): Improvement of methods for description of a three-bunker collection conveyor. Published in: Eastern-European Journal of Enterprise Technologies , Vol. 5, No. 4(119) (14 October 2022): pp. 33-41.

Abstract

The object of current research is a multi-section transport conveyor. The actual control problem of the flow parameters of a multi-section conveyor-type transport system with a given control quality criterion is solved. Algorithms for optimal control of the flow of material coming from the input accumulating bunkers into the collection section of the conveyor, ensuring the filling of the accumulating tank in the minimum time was synthesized. An admissible control of the material flow from the accumulating bunkers is found, which allows filling the accumulating tank, taking into account the given distribution of the material along the section of the collection conveyor at the initial and final moments of the filling time with minimal energy consumption. The synthesis of algorithms for optimal control of the material flow from accumulating bunkers became possible due to the determination of differential constraints in the optimal control problem based on an analytical distributed model of a transport conveyor section. The distinctive features of the results obtained are that the allowable controls contain restrictions on the maximum allowable load of material on the conveyor belt and take into account the initial and final distribution of material along the collection conveyor section. Also, a feature of the obtained results is the consideration of variable transport delay in the transport conveyor control model. The application area of the results is the mining industry. The developed models make it possible to synthesize algorithms for optimal control of the flow parameters of the transport system for a mining enterprise, taking into account the transport delay in the incoming material at the output of the conveyor section. The condition for the practical use of the results obtained is the presence of measuring sensors in the sections of the transport conveyor that determine the belt speed and the amount of material in the accumulating bunkers.

• Item Type: MPRA Paper
• Language: English
• Keywords: PiKh model; speed control; transport delay; accumulating bunker; similarity criteria
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory; D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity; L - Industrial Organization > L2 - Firm Objectives, Organization, and Behavior > L23 - Organization of Production; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation > Q21 - Demand and Supply ; Prices

References

1. Siemens – innovative solutions for the mining industry. 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. 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. 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, 3 (72), 69–82

5. Jeftenic, B., Ristic, L., Bebic, M., Statkic, S., Mihailovic, I., Jevtic, D. (2010). Optimal utilization of the bulk material transportation system based on speed controlled drives. The XIX International Conference on Electrical Machines – ICEM 2010. https:// doi.org/10.1109/icelmach.2010.5608055

6. 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. https://mpra.ub.uni-muenchen.de/104681/

7. Halepoto, I. A., Shaikh, M. Z., Chowdhry, B. S., Uqaili, M., Uhammad, 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. https://doi.org/10.14257/ijca.2016.9.6.36

8, He, D., Pang, Y., Lodewijks, G., Liu, X. (2018). Healthy speed control of belt conveyors on conveying bulk materials. Powder Technology, 327, 408–419. https://doi.org/10.1016/j.powtec.2018.01.002

9. 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. https://doi.org/10.15588/1607-3274-2018-1-18

10. Kiriia, R., Shyrin, L. (2019). Reducing the energy consumption of the conveyor transport system of mining enterprises. E3S Web of Conferences, 109, 00036. https://doi.org/10.1051/e3sconf/201910900036

11. 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). https://doi.org/10.1109/ dessert50317.2020.9124996

12. Woo, C. K., Sreedharan, P., Hargreaves, J., Kahrl, F., Wang, J., Horowitz, I. (2014). A review of electricity product differentiation. Applied Energy, 114, 262–272. https://doi.org/10.1016/j.apenergy.2013.09.070

13. Cousins, T. (2010). Using Time of Use (TOU) Tariffs in Industrial, Commercial and Residential Applications Effectively. TLC. http://www.tlc.co.za/white_papers/pdf/using_time_of_use_tariffs_in_industrial_commercial_and_residential_applications_effectively.pdf

14. Granell, R., Axon, C. J., Wallom, D. C. H. (2014). Predicting winning and losing businesses when changing electricity tariffs. Applied Energy, 133, 298–307. https://doi.org/10.1016/j.apenergy.2014.07.098

15. Marais, J., Mathews, E., Pelzer, R. (2008). Analysing DSM opportunities on mine conveyor systems. In: Industrial and commercial use of energy conference. Cape Town.

16. Wolstenholme, E. F. (1980). Designing and Assessing the Benefits of Control Policies for Conveyor Belt Systems in Underground Coal Mines. Dynamica, 6 (2), 25–35. https://systemdynamics.org/wp-content/uploads/assets/dynamica/ volume-6/6-2/6.pdf

17. Kazakova, E. I., Govorukha, E. N. (2019). Optimal proactive management of cargo flows. Austrian Journal of Technical and Natural Sciences, 7-8, 25–28. https://doi.org/10.29013/ajt-19-7.8-25-28

18. Bardzinski, P., Jurdziak, L., Kawalec, W., Król, R. (2019). Copper Ore Quality Tracking in a Belt Conveyor System Using Simulation Tools. Natural Resources Research, 29 (2), 1031–1040. https://doi.org/10.1007/s11053-019-09493-6

19. Pihnastyi, O., Khodusov, V. (2020) Neural model of conveyor type transport system. CEUR Workshop Proceedings, 2608. http://ceur-ws.org/Vol-2608/paper60.pdf

20. Więcek, D., Burduk, A., Kuric, I. (2019). The use of ANN in improving efficiency and ensuring the stability of the copper ore mining process. Acta Montanistica Slovaca, 24 (1), 1–14. https://actamont.tuke.sk/pdf/2019/n1/1wiecek.pdf

21. Dong, M., Luo, Q. (2011). Research and Application on Energy Saving of Port Belt Conveyor. Procedia Environmental Sciences, 10, 32–38. https://doi.org/10.1016/j.proenv.2011.09.007

22. Pihnastyi, O. M. (2018). Statistical theory of control systems of the flow production. LAP LAMBERT Academic Publishing, 436.

23. Pihnastyi, O. M., Khodusov, V. D. (2020). Hydrodynamic Model of Transport System. East European Journal of Physics, 1, 121–136. https://doi.org/10.26565/2312-4334-2020-1-11

24. Pihnastyi, O., Khodusov, V., Kotova, A. (2022). The Problem of Combined Optimal Load Flow Control of Main Conveyor Line. Acta Montanistica Slovaca, 27 (1), 216–229. https://doi.org/10.46544/ams.v27i1.16