Pihnastyi, Oleh and Kozhevnikov, Georgii (2020): Control of a Conveyor Based on a Neural Network. Published in: International Conference on Problems of Infocommunications. Science and Technology (PIC S&T) (9 October 2020): pp. 295300.

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Abstract
The present study is devoted to the design of the main flow parameters of a conveyor control system with a large number of sections. For the design of the control system, a neural network is used. The architecture of the neural network is justified and the rules for the formation of nodes for the input and output layers are defined. The main parameters of the model are identified and analyzed. The data set for training the neural network is formed using the analytical model of the transport system. The criterion for the quality of the transport system is written. For the given criterion for the quality of the transport system, the Pontryagin function is defined and the adjoint system of equations is given. It allows calculating optimal control of the transport system. For calculation is used additional model of the transport system with output nodes which are controls. A graphical representation of the results of the study is given
Item Type:  MPRA Paper 

Original Title:  Control of a Conveyor Based on a Neural Network 
Language:  English 
Keywords:  PDEmodel production; PiKhmodel; distributed system; optimal control 
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 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 
Item ID:  111950 
Depositing User:  Oleh Mikhalovych Pihnastyi 
Date Deposited:  16 Feb 2022 16:15 
Last Modified:  16 Feb 2022 16:15 
References:  1. O.Pihnastyi, Statistical theory of control systems of the flow production. Beau Bassin, LAP LAMBERT: Academic Publishing, 2018. 2. Conveyorbeltguide Engineering: Conveyor components, http://conveyorbeltguide.com/examplesofuse.html 3. W.Kung, The Henderson Coarse Ore Conveying System. A Review of Commissioning, Startup, and Operation. Bulk Material Handling by Belt Conveyor 5, Society for Mining, Metallurgy and Exploration, Inc., 2004 4. M.Alspaugh, Latest developments in belt conveyor technology. In: MINExpo 2004, New York, Las Vegas, NV, USA, 2004 5. Siemens. Innovative solutions for the mining industry, www.siemens.com/mining 6. M. Alspaugh, Longer Overland Conveyors with Distributed Power. In: Overland Conveyor Company, Lakewood, USA, 2005 7. Ju. Razumnyj, A. Ruhlov, and A. Kozar, Povyshenie jenergojeffektivnosti konvejernogo transporta ugol'nyh shaht. Gіrnicha elektromehanіka ta avtomatika. vol. 76, pp. 24–28, 2006. 8. O.Pihnastyi, Largest conveyor transport systems. Mendeley Data, vol. V1, 2020. 9. Continous conveyors. Belt conveyors for loose bulk materials. Basics for calculation and dimensioning. DIN 22101:200208. 2002 10. O.Pihnastyi, Control of the belt speed at unbalanced loading of the conveyor. Scientific Bulletin of National Mining University. vol.6, pp. 122–129, 2019. 11. A.Semenchenko, M.Stadnik, P.Belitsky, D.Semenchenko and O.Stepanenko, The impact of uneven loading of a belt conveyor on the loading of drive motors and energy consumption in transportation," EasternEuropean Journal of Enterprise Technologies, vol. 82, 4/1, pp. 42–51, April 2016. 12. E.Wolstenholm, Designing and assessing the benefits of control policies for conveyor belt systems in underground mines. Dynamica. vol. 6(2), 25 35 June 1980 13. H.Lauhoff, Speed Control on Belt Conveyors – Does it Really Save Energy?. Bulk Solids Handling Publ. vol. 25(6), 368 377, 2005 14. O.Pihnastyi, V.Khodusov, Model of conveyer with the regulable speed. Bulletin of the South Ural State University. Ser.Mathematical Modelling, Programming and Computer Software vol. 10, .64–77 (2017). 15. L.Nordell, Z.Ciozda, Transient belt stresses during starting and stopping. Elastic response simulated by finite element methods. Bulk Solids Handling vol. 4(1), pp. 99–104, April 1984. 16. D.He, Y.Pang, G.Lodewijks and X. Liu, Determination of Acceleration for Belt Conveyor Speed Control in Transient Operation. International Journal of Engineering and Technology vol. l.8(3), pp. 206–211, 2016. 17. M.Alspaugh, Latest Developments in Belt Conveyor Technology. Overland Conveyor Co., pp.1–11. In:MINExpo 2004, Las Vegas, NV, USA, 2004. 18. B.Karolewski, P.Ligocki, Modelling of long belt conveyors. Maintenance and reliability. Eksploatacja i Niezawodność vol. 16, no. 2, pp. 179–187, 2014. 19. R.Pascual, V.Meruane and G.Barrientos, Analysis of transient loads on cablereinforced conveyor belts with damping consideration. In: the XXVI Iberian LatinAmerican Congress on Computational Methods in Engineering (CILAMCE2005), pp.1–15, Santo, Brazil 2005. 20. C.Wheeler, Predicting the main resistance of belt conveyors. In International Materials Handling Conference (Beltcon) 12, Johannesburg, South Africa (2003). 21. X.Mathaba and Xia, A parametric energy model for energy management of long belt conveyors. Energies vol. 8(12) pp. 13590–13608, 2015. 22. A. Reutov, Simulation of load traffic and steeped speed control of conveyor. In: IOP Conference Series: Earth and Environmental, 87, pp.1–4. 2017. 23. A.Kirjanów, The possibility for adopting an artificial neural network model in the diagnostics of conveyor belt splices. Interdisciplinary issues in mining and geology vol. 6, pp. 1–11, 2016 24. D.Więcek, A.Burduk and I.Kuric, The use of ANN in improving efficiency and ensuring the stability of the copper ore mining process," Acta Montanistica Slovaca vol. 24(1). pp. 1–14, 2019. 25. 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 vol. 5, pp.1–10, January 2013. 26. L.Xinglei, Yu.Hongbin, The Design and Application of Control System Based on the BP Neural Network. In Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015). pp. 789–793. (2015). 27. Xi Pingyuan, Song Yandong, Application Research on BP Neural Network PID Control of the Belt Conveyor. JDIM vol. 9(6), pp. 266–270, 2011. 28. M.Andrejiova, D.Marasova, Using the classical linear regression model in the analysis of the dependences of conveyor belt life. Acta Montanistica Slovaca vol. 18(2). pp. 77–84, 2013. 29. Yan Lu, Q.Li, A regression model for prediction of idler rotational resistance on belt conveyor," Measurement and Control vol. 52(5), pp. 441–448, June 2019. 30. A.Grincova, D.Marasova, Experimental research and mathematical modelling as an effective tool of assessing failure of conveyor belts. Maintenance and reliability. vol. 16 (2), pp. 229–235, 2014. 31. Bastian Solutions Conveyor System Design Services, https://www.bastiansolutions.com/solutions/technology/conveyorsystems/designservices 32. O.Pihnastyi, V.Khodusov, 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, vol. 4 (166), pp. 138–146, 2018. 33. O.Pihnastyi, V.Khodusovt, Model of a composite magistral conveyor line. In IEEE International Conference on System Analysis & Intelligent computing (SAIC 2018), pp.68–72. Ukraine, Kyiv, 2018. 34. R.Zimroz, R.Krol, Failure analysis of belt conveyor systems for condition monitoring purposes," Mining Science vol. 128(36), pp.255–270, 2009. 35. S.Abdollahpor, A.Mahmoudi, A.Mirzazadeh, Artificial neural network prediction model for material threshing in a combine harvester. Elixir Agriculture, vol. 52, pp. 11621–11626, 2012. 36. Y.Yuan, W.Meng, X.Sun, Research of fault diagnosis of belt conveyor based on fuzzy neural network. The Open Mechanical Engineering Journal, vol. 8, pp. 916–921, 2014. 37. N.Selcuk, Y.Birbir, Application of artificial neural network for harmonic estimation in different produced induction motors. Int. J. of Circuits, Systems and Signal Processing, vol. 4(1), pp. 334–339 2007. 38. O.Pihnastyi, Test data set for the conveyor transport system. Mendeley Data, V2, 2020. 39. O.Pihnastyi, V.Khodusov, Optimal Control Problem for a Conveyor–Type Production Line," Cybern. Syst. Anal. vol. 54(5), pp. 744–753, 2018. 40. R.Krol, W.Kawalec, L.Gladysiewicz, An effective belt conveyor for underground ore transportation systems. In: IOP Conference Series: Earth and Environmental Science, 95(4), pp. 1–4, 2017. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/111950 