Optimal control of an industrial electrostatic rotating electrode separator using artificial intelligence technics

• Autorzy: Dahou Omar , Touhami Seddik , Ayache Zouaoui

Identyfikatory

DOI

10.15199/48.2019.11.44

Warianty tytułu

PL

Optymalne sterowanie przemysłowym separatorem elektrostatycznym z ruchomymi elektrodami

• Języki publikacji: EN

Abstrakty

EN

The main purpose of this study is the multicriterion optimization in a dynamic context of the operation of an industrial electrostatic separation process with rotating electrode. A study of the operation of this process, performed by using an artificial neural network (ANN), has shown the complexity of adjusting the control variables for use in the industrial field. In this context, a multifactorial control approach has been proposed using meta-heuristics based on artificial intelligence.

PL

W artykule zaprezentowano multikryterialną optymalizację przemysłowego separatora elektrostatycznego z ruchomymi elektrodami. Do optymalizacji wykorzystano sztuczne sieci neuronowe.

Słowa kluczowe

EN

electrostatic separator; optimization; control; ANN; BBO

PL

separator elektrostatyczny; optymalizacja; sztuczna sieć neuronowa

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2019
• Tom: R. 95, nr 11
• Strony: 170--175
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Dahou Omar

• Department of Electrical Engineering, University Mustapha Stambouli of Mascara, Algeria,BP 305, route de Mamounia 29000, Mascara, Algeria

autor

Touhami Seddik

• Department of Electrical Engineering,University Djilali Liabes of Sidi Be lAbbes 22000 Algeria

autor

Ayache Zouaoui

• University Center Belhadj Bouchaib of Ain Temouchent,46000,Algeria

Bibliografia

• [1] L. Dascalescu, A. Mihalcioiu, A. Tilmatine, M. Mihailescu, A. Iuga, A. Samuila, “ Electrostatic separation processes“, Industry Applications Magazine, IEEE. Vol 10, pp. 19-25, 2004.
• [2] A. Tilmatine, K. Medles, S. Bendimered, F. Boukhoulda, and L. Dascalescu, “Electrostatic separators of particles. Application to plastic/metal, metal/metal and plastic/plastic mixtures," Waste Manag vol. 29, pp. 228-232, 2009.
• [3] K. Medles, A. Tilmatine, A. Bendaoud, M. Rahli, L. Dascalescu. “Set Point Identification and Robustness Testing of Electrostatic Separation Processes", IEEE Trans. Ind. Appl, Vol.3, 2007.
• [4] O. Dahou, K. Medles, S. Touhami, M.F. Boukhoulda, A.Tilmatine, and L.Dascalescu, “Application of genetic algorithms to the optimization of a roll-type electrostatic separation process" IEEE Trans. Ind. Appl., vol. 47, pp 2218- 2223, 2011.
• [5] S. Touhami , K. Medles, O. Dahou, A. Tilmatine, , A. Bendaoud, L. Dascalescu “Modeling and Optimization of a Roll- Type Electrostatic Separation Process Using Artificial Neural Networks" IEEE Trans. Ind. Appl., vol. 49, pp 1773-1780, 2013
• [6] M. Younes, A. Tilmatine, K. Medles, and L. Dascalescu “Fuzzy Control of an Electrostatic Separation Process," IEEE Trans. Ind. Appl., vol. 44, pp. 09-14, 2008.
• [7] D.E. Goldberg, “Genetic algorithms in search, optimisation and machine learning", Addison-Wesley, 1989.
• [8] J.M. Renders, “Algorithmes génétiques et réseaux de neurones", Hermès, Paris, 1995
• [9] M. Dorigo and L. M. Gambardella. “Guest editorial special on ant colony optimization". IEEE Transactions on evolutionary computation, Vol 4, pp.317-319, 2002.
• [10] M. Dorigo and C. Blum. “Ant colony optimization theory: a survey". Theoretical Computer Science, Vol. 344, No. 2-3, pp. 243-278, 2005.
• [11] J. Kennedy and R. C. Eberhart. “Particle Swarm Optimization". Proceedings of the IEEE International Conference on Neural Networks IV, pp. 1942-1948, 1995.
• [12] M. Clerc and J. Kennedy. “The particle swarm: explosion, stability, and convergence in multi-dimensional complex space". IEEE Transactions on Evolutionary Computation, Vol. 6, pp. 58-73, 2002.
• [13] J. Holland, “Adaptation in natural and artificial systems", Second Edition, MIT Press, 1992
• [14] H.P. Schwefel and T .Bäck, “Artificial Intelligence: How and why?" Genetic Algorithms and evolution strategies in engineering and computer science, Edited by D. Quagliarella & al, J. Wiley Editions, 1997.
• [15] V. Kecman, ’Learning And Soft Computing : Support Vector Machines, Neural Networks, and Fuzzy Logic Models’, MIT Press, USA.2001
• [16] A.G. Looney, “Advances in feedforward neural networks: demystifying knowledge acquiring black boxes," IEEE Trans. Knowledge & Data Eng., vol. 8, pp. 211-220. 1996
• [17] K. C. Laia,, S. K. Limb, P. C. Teha, K. H. Yeapa, “Modeling electrostatic separation process using artificial neural network (ANN)" Procedia Computer Science 91 ,pp.372 - 381, 2016
• [18] D. Simon," Biogeography-based optimization", IEEE Transactions on Evolutionary Computation, Vol 12, pp. 702- 713, 2008.
• [19] R. MacArthur & E. Wilson. “The Theory of Biogeography". Princeton University Press, Princeton, NJ, 1967.
• [20] S. Habib A. Rahmati, M. Zandieh “A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem" Int J Adv Manuf Technol, Vol 58, pp. 1115-1129, 2011.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).