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Three-dimensional fuzzy control of ultrasonic cleaning

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Consideration of ultrasonic cleaning as a process with distributed parameters enables reduction of power consumption. This approach is based on establishment of control over the process depending on fixed values of ultrasonic responses in set points. The initial intensity of radiators is determined using a three-dimensional (3D) interval type-2 fuzzy logic controller essentially created for processes with distributed parameters, as well as complex expert evaluation of the input data. The interval membership functions for the input and output data consider the space heterogeneity of ultrasonic cleaning. A rule base is formed, which is 2D and not dependent upon the number of input and output parameters. A model illustrating ultrasonic cleaning with a 3D interval type-2 fuzzy logic controller is designed. Comparative analysis of the output parameters of the proposed model and the traditional method indicates an increase in the energy efficiency by 41.17% due to application of only those ultrasonic radiators that are located next to the contamination.
Rocznik
Strony
169--176
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
  • Kryvyi Rih National University, 11, Vitaliy Matusevych Street, Kryvyi Rih 50027, Ukraine
  • Kryvyi Rih National University, 11, Vitaliy Matusevych Street, Kryvyi Rih 50027, Ukraine
Bibliografia
  • 1. Duran F., Teke M. (2018), Design and implementation of an intelligent ultrasonic cleaning device, Intelligent Automation and Soft Computing, 441-450.
  • 2. Karnik N., Mendel, J. (2001), Centroid of a type-2 fuzzy set, Inform.Sci., 132, 195-220.
  • 3. Li H., Zhang X., Li, S. (2007), A Three-Dimensional Fuzzy Control Methodology for a Class of Distributed Parameter Systems, IEEE Transactions on Fuzzy Systems, 15(3), 470-481.
  • 4. Mamdani Е. (1974), Application of fuzzy logic algorithms for control of simple dynamic plant, Proceedings of the Institution of Electrical Engineers, 121(12), 1585-1588.
  • 5. Mendel J. M., John R. I., Liu, F. (2006), Interval Type-2 Fuzzy Logic Systems Made Simple, IEEE Transactions on Fuzzy Systems, 14(6), 808-821.
  • 6. Morkun V., Morkun N., Tron V. (2015d), Model synthesis of nonlinear nonstationary dynamical systems in concentrating production using Volterra kernel transformation, Metallurgical and Mining Industry, 7(10), 6-9.
  • 7. Morkun V., Morkun N., Pikilnyak A. (2014a), Modeling of ultrasonic waves propagation in inhomogeneous medium using fibered spaces method (k-space). Metallurgical and Mining Industry, 6(2), 43-48.
  • 8. Morkun V., Morkun N., Pikilnyak A. (2014b), The adaptive control for intensity of ultrasonic influence on iron ore pulp, Metallurgical and Mining Industry, 6(6), 8-11.
  • 9. Morkun V., Morkun N., Tron V. (2015a), Distributed closed-loop control formation for technological line of iron ore raw materials beneficiation, Metallurgical and Mining Industry, 7(7),16-19.
  • 10. Morkun V., Morkun N., Tron V. (2015b), Distributed control of ore beneficiation interrelated processes under parametric uncertainty, Metallurgical and Mining Industry, 7(8), 18-21.
  • 11. Morkun V., Morkun N., Tron, V. (2015c), Identification of control systems for ore-processing industry aggregates based on nonparametric kernel estimators, Metallurgical and Mining Industry, 7(1), 14-17.
  • 12. Nigmetzyanov R. I., Kazantsev V. F., Prikhod’ko V. M., Sundukov S. K., Fatyukhin D. S. (2019), Improvement in Ultrasound Liquid Machining by Activating Cavitational Clusters, ISSN 1068-798X, Russian Engineering Research, 8, 699–702.
  • 13. Porkuian O., Morkun V., Morkun N. (2020), Measurement of the ferromagnetic component content in the ore suspension solid phase, Ultrasonics, 105, 106103.
  • 14. Porkuian O., Morkun V., Morkun N., Serdyuk O. (2019), Predictive Control of the Iron Ore Beneficiation Process Based on the Hammerstein Hybrid Model, Acta Mechanica et Automatica, 13(4), 262-270.
  • 15. Rahim A., Bardoshadi H. and Sarrafi S. (2011), Design and Manufacture an Ultrasonic Dispersion System, Sensors and Transducers Journal, 126(3), 52-63.
  • 16. Roohia R., Abedib E., Hashemi S. M. B., Marszałek K., Lorenzo J. M., Barbae F. (2019). Ultrasound-assisted bleaching: Mathematical and 3D computational fluid, Innovative Food Science and Emerging Technologies, 55, 66-79.
  • 17. Tangsopha W., Thongsri J., Busayaporn W. (2017). Simulation of ultrasonic cleaning and ways to improve the efficiency. 5th International Electrical Engineering Congress, 8-10.
  • 19. Tangsopha W., Thongsri J., (2020), A Novel Ultrasonic Cleaning Tank Developed by Harmonic Response Analysis and Computational Fluid Dynamics, Metals, 10(335), 1-18.
  • 20. Treeby B. E., Jaros J., Rendell A. P., Cox B. T. (2012), Modeling nonlinear ultrasound propagation in heterogeneous media with power law absorption using a k-space pseudospectral method, Acoustical Society of America, 131(6), 4324–4336.
  • 21. Treeby B.E., Cox T. (2010), k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields, Journal of Biomedical Optics, 15(2), 021314.
  • 22. Xu H., Tu J., Niu F., Yang, P. (2016), Cavitation dose in an ultrasonic cleaner and its dependence on experimental parameters, Applied Acoustics, 101, 179–184.
  • 23. Zadeh L. (1975), The concept of a linguistic variable and its application to appro1imate reasoning, Inform.Sci., 8, 199-249.
  • 24. Zhang X., Fu Z.-Q., Li S.-Y., Zou T., Wang B. (2017), A time/space separation based 3D fuzzy modeling approach for nonlinear spatially distributed systems, International Journal of Automation and Computing, 15, 1-14.
Uwagi
Błędna numeracja bibliografii.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-095d584a-8fec-4b60-b858-93cfbf025ccd
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