Pihnastyi, Oleh and Khodusov, Valery (2020): Neural model of conveyor type transport system. Published in: Proceedings of The Third International Workshop on Computer Modeling and Intelligent Systems (CMIS-2020) , Vol. 2608, (1 May 2020): pp. 804-818.
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Abstract
In this paper, a model of a transport conveyor system using a neural network is demonstrated. The analysis of the main parameters of modern conveyor systems is presented. The main models of the conveyor section, which are used for the design of control systems for flow parameters, are considered. The necessity of using neural networks in the design of conveyor transport control systems is substantiated. A review of conveyor models using a neural network is performed. The conditions of applicability of models using neural networks to describe conveyor systems are determined. A comparative analysis of the analytical model of the conveyor section and the model using the neural network is performed. The technique of forming a set of test data for the process of training a neural network is presented. The foundation for the formation of test data for learning neural network is an analytical model of the conveyor section. Using an analytical model allowed us to form a set of test data for transient dynamic modes of functioning of the transport system. The transport system is presented in the form of a directed graph without cycles. Analysis of the model using a neural network showed a high-quality relationship between the output flow for different conveyor sections of the transport system
Item Type: | MPRA Paper |
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Original Title: | Neural model of conveyor type transport system |
English Title: | Neural model of conveyor type transport system |
Language: | English |
Keywords: | conveyor; PDE– model; distributed system; transport delay |
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 > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities 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 |
Item ID: | 101527 |
Depositing User: | Oleh Mikhalovych Pihnastyi |
Date Deposited: | 05 Jul 2020 18:58 |
Last Modified: | 05 Jul 2020 18:58 |
References: | 1. Pihnastyi, O.: Statistical theory of control systems of the flow production. Lambert Academic Publishing, Beau Bassin (2018) 2. Conveyorbeltguide Engineering: Conveyor components. http://conveyorbeltguide.com/examples-of-use.html 3. Kung, W.: The Henderson Coarse Ore Conveying System. A Review of Commissioning, Start-up, and Operation, Bulk Material Handling by Belt Conveyor, Society for Mining, Metallurgy and Exploration (2004) 4. Alspaugh, M.: Latest developments in belt conveyor technology. In: MINExpo 2004, New York, Las Vegas (2004) 5. Siemens. Innovative solutions for the mining industry. http://www.siemens.com/mining 6. Alspaugh, M.: Longer Overland Conveyors with Distributed Power. In: Overvand Convey-or Company, Lakewood. (2005) 7. DIN 22101:2002-08. Continous conveyors. Belt conveyors for loose bulk materials. Basics for calculation and dimensioning. [Normenausschuss Bergbau (FABERG), Deutsches Insti-tut für Normung e.v. Normenausschuss Maschinenbau (NAM)]. (2002). 8. Pihnastyi O.M.: Control of the belt speed at unbalanced loading of the conveyor. Scientific bulletin of National Mining University. 6, 122–129 (2019). doi:10.29202/nvngu/2019-6/18 9. Semenchenko, A., Stadnik,M., Belitsky, P., Semenchenko, D., Stepanenko, O.: The impact of an uneven loading of a belt conveyor on the loading of drive motors and energy consump-tion in transportation. EasternEuropean Journal of Enterprise Technologies, 4/1(82), 42 –51 (2016). https://doi.org/10.15587/1729-4061.2016.75936 10. Wolstenholm, E.: Designing and assessing the benefits of control policies for conveyor belt systems in underground mines. Dynamica. 6(2), 25 35 (1980) 11. Lauhoff, H.: Speed Control on Belt Conveyors – Does it Really Save Energy? Bulk Solids Handling Publ. 25(6), 368 377 (2005) 12. Pihnastyi, O., Khodusov, V.: Model of conveyer with the regulable speed. Bulletin of the South Ural State University. Ser.Mathematical Modelling, Programming and Computer Software 10, .64–77 (2017). https://doi.org/10.14529/mmp170407. 13. Nordell, L.K., Ciozda, Z.P.: Transient belt stresses during starting and stopping: Elastic re-sponse simulated by finite element methods. Bulk Solids Handling 4(1), 99–104 (1984). http://www.ckit.co.za/secure/conveyor/papers/troughed/transient/transient-belt-stresses.htm 14. He, D., Pang, Y., Lodewijks, G., Liu, X.: Determination of Acceleration for Belt Conveyor Speed Control in Transient Operation. International Journal of Engineering and Technology l.8(3), 206–211 (2016). http://dx.doi.org/10.7763/IJET.2016.V8.886 15. Alspaugh, M.A.: Latest Developments in Belt Conveyor Technology. Overland Conveyor Co., In: MINExpo 2004, pp.1–11. Las Vegas, (2004). http://www.overlandconveyor.com/pdf/Latest Developments in Belt Conveyor Technolo-gy.pdf 16. Karolewski, B., Ligocki, P.: Modelling of long belt conveyors. Maintenance and reliability. 16 (2), 179–187 (2014). 17. Pascual, R., Meruane, V., Barrientos, G.: Analysis of transient loads on cable-reinforced conveyor belts with damping consideration. In: the XXVI Iberian Latin-American Congress on Computational Methods in Engineering, pp.1–15, Santo, Brazil (2005). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.494.34&rep=rep1&type=pdf 18. Wheeler, C.A.: Predicting the main resistance of belt conveyors. In International Materials Handling Conference (Beltcon) 12, Johannesburg, South Africa (2003). http://www.saimh.co.za/beltcon/beltcon12/paper1208.htm 19. Mathaba and Xia, 2015 T. Mathaba, X. Xia, A parametric energy model for energy man-agement of long belt conveyors. Energies 8(12), 13590–13608 (2015). https://doi.org/10.3390/en81212375 20. Reutov, A.: Simulation of load traffic and steeped speed control of conveyor. In: IOP Con-ference Series: Earth and Environmental, 87, pp.1–4 (2017). https://doi.org/10.1088/1755-1315/87/8/082041. 21. Kirjanów, A.: The possibility for adopting an artificial neural network model in the diagnos-tics of conveyor belt splices. Interdisciplinary issues in mining and geology 6, 1–11 (2016) 22. Więcek, D., Burduk, A., Kuric, I.: The use of ANN in improving efficiency and ensuring the stability of the copper ore mining process. Acta Montanistica Slovaca 24(1), 1–14 (2019). https://actamont.tuke.sk/pdf/2019/n1/1wiecek.pdf 23. Li, W., Wang, Z., Zhu, Zh., Zhou, G., Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine. Advances in Mechanical Engineering 2013, 1–10 (2013) http://dx.doi.org/10.1155/2013/797183 24. Xinglei L., Hongbin, Yu.: The Design and Application of Control System Based on the BP Neural Network. In the 3rd International Conference on Mechanical Engineering and Intelli-gent Systems, pp. 789–793 (2015). https://doi.org/10.2991/icmeis-15.2015.148 25. Pingyuan, Xi, Yandong, Song : Application Research on BP Neural Network PID Control of the Belt Conveyor. JDIM 9(6), 266–270 (2011). 26. Andrejiova, M, Marasova, D.:. Using the classical linear regression model in analysis of the dependences of conveyor belt life. Acta Montanistica Slovaca 18(2), 77–84 (2013). https://actamont.tuke.sk/pdf/2013/n2/2andrejiova.pdf 27. Grincova, A., Li, Q.: A regression model for prediction of idler rotational resistance on belt conveyor. Measurement and Control 52(5), 441–448 (2019). https://doi.org/10.1177/0020294019840723 28. Karolewski, B., Marasova, D.: Experimental research and mathematical modelling as an ef-fective tool of assessing failure of conveyor belts. Maintenance and reliability. 16 (2), 229–235 (2014). http://www.ein.org.pl/sites/default/files/2014-02-09.pdf 29. Pihnastyi , O., Khodusov, V.: Calculation of the parameters of the composite conveyor line with a constant speed of movement of subjects of labour. Scientific bulletin of National Mining University. 4 (166), 138–146 (2018). https://doi.org/10.29202/nvngu/2018-4/18 30. Pihnastyi , O., Khodusov, V.: Model of a composite magistral conveyor line. In IEEE Inter-national Conference on System analysis & Intelligent computing, pp.68–72 (2018). https://doi.org/10.1109/saic.2018.8516739 31. Bastian Solutions Conveyor System Design Services. https://www.bastiansolutions.com/solutions/technology/conveyor-systems/design-services 32. Zimroz, R., Krol, R.: Failure analysis of belt conveyor systems for condition monitoring purposes. Mining Science 128(36), 255–270 (2009). 33. Abdollahpor, S., Mahmoudi, A., Mirzazadeh, A.: Artificial neural network prediction model for material threshing in combine harvester. Elixir Agriculture 52, 11621–11626 (2012). https://www.elixirpublishers.com/articles/1353477517_52%20(2012)%2011621-11626.pdf 34. Xinglei, L., Hongbin, Yu.: The Design and Application of Control System Based on the BP Neural Network. In the 3rd International Conference on Mechanical Engineering and Intelligent Systems, pp.789–793 (2015). https://doi.org/10.2991/icmeis-15.2015.148 35. Yuan, Y., Meng, W., Sun, X. Research of fault diagnosis of belt conveyor based on fuzzy neural network. The Open Mechanical Engineering Journal 8, 916–921 (2014). https://doi.org/10.2174/1874155X01408010916 36. Selcuk, N., Birbir, Y.: Application of artificial neural network for harmonic estimation in dif-ferent produced induction motors. Int. J. of Circuits, Systems and Signal Processing 4(1), 334–339 (2007). https://pdfs.semanticscholar.org/ba2f/6d5ea4c91720be4044e0f3544efb60fd6bb4.pdf 37. Chen, W., Li, X. : Model predictive control based on reduced order models applied to belt conveyor system. ISA Transactions 65, 350–360 (2016). doi:10.1016/j.isatra.2016.09.007 38. Pihnastyi, O.: Test data set for the conveyor transport system, Mendeley Data, V3, (2020). http://dx.doi.org/10.17632/4vcb843t76.3 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/101527 |