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Fuzzy models of the biological-chemical processes in the west-water treatment plant

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the work issues related to control of the electrical blowers installed in the waste water treatment plant (WWTP) are demonstrated. After a short introduction a description of a real treatment plant is presented. Then the main sewage drives are described. Next the commonly used control strategies for the biological-chemical process are presented. The model ASM1 of the biological chemical transformation used in the advanced MPC (model predictive control) strategy is introduced. Then the fuzzy system based on the TSK model is presented. The ability of the TSK fuzzy system to estimate the biological-chemical variables are demonstrated.
Rocznik
Tom
Strony
230--241
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
  • Wroclaw University of Technology 50-372 Wrocław, ul. Smoluchowskiego 19
  • Wroclaw University of Technology 50-372 Wrocław, ul. Smoluchowskiego 19
Bibliografia
  • [1] M.A. Brdys, M. Grochowski, T. Gminski, K. Konarczak, M. Drewa. Hierarchical predictive control of integrated wastewater treatment systems, Control Engineering Practice, vol. 16, Issue 6, 2008, 751-767.
  • [2] A. Stare, D. Vrecko, N. Hvala, S. Strmcnik, Comparison of control strategies for nitrogen removal in an activated sludge process in terms of operating costs: simulation study, Water research, vol. 41, 2007, 2004-2014.
  • [3] M. Yong, P. Yongzhen, U. Jeppsson, Dynamic evaluation of integrated control strategies for enhanced nitrogen removal in activated sludge processes, Control Engineering Practice, vol. 14pp. 1269-1278, 2006.
  • [4] Olsson G., Newell B., Wastewater Treatment Systems, Modeling, Diagnosis and Control, IWA Publishing, 1999.
  • [5] http://www.ensic.inpl-nancy.fr/benchmarkWWTP/Bsm1/Benchmark1.htm
  • [6] M. Ekman, B. Bjorlenius, M. Andersson, Control of the aeration volume in an activated sludge process using supervisory control strategies, Water research, vol. 40, pp. 1668 - 1676, 2006.
  • [7] J. Maciejowski, Predictive control with constraints. Prentice Hall, 2002.
  • [8] K. Szabat, C. T. Kowalski, Rozmyty model procesów biologiczno-chemicznych w oczyszczalniach ścieków, XV Conference ZKwE '10, 2010, 321-322.
  • [9] K. Szabat, C.T. Kowalski, Fuzzy models of the biological-chemical processes in the sewage treatment plant for an advanced control of electrical blowers, accepted to IECON’10, 2010.
  • [10] Borowa A., Brdys M. A., Mazur K., Modeling of wastewater treatment plant for monitoring and control purposes by state-space wavelet networks, International Journal of Communications & Control, vol. 2, 2007, 121-131.
  • [11] M. Henze, C. P. L. Jr Grady, G. V. R. Marais, T. Matsuo, Activated Sludge Model No. 1, IAWPRC Scientific and Technical Reports No 1., IAWPRC, London. IWA Task Group on Mathematical Modelling for Design and Operation of Biological Wastewater Treatment. London, IWA Publishing, 2000.
  • [12] A. Piegat. Fuzzy Modeling and Control. Springer-Heidelberg, New York, 2001.
  • [13] X. Li, X. Lu, J. Tian, P. Gao, H. Kong, and G. Xu, Application of Fuzzy c-Means Clustering in Data Analysis of Metabolomics, Analytical Chemistry, 2009, vol. 81, pp. 4468-4475.
  • [14] W. Pedrycz, V. Loia, S. Senatore, Fuzzy Clustering With Viewpoints IEEE Transactions On Fuzzy Systems, Vol. 18, No. 2, 2010.
  • [15] R. K. Brouwer, A. Groenwold, Modified fuzzy c-means for ordinal valued attributes with particle swarm for optimization, Fuzzy Sets and Systems, vol. 161, (2010) 1774-1789.
  • [16] D. Graves, W. Pedrycz, Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study, Fuzzy Sets and Systems, 161 (2010) 522-543.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-14e2537c-735a-43c9-b851-58e86d6061ea
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