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Higher order fuzzy logic in controlling selective catalytic reduction systems

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents research on applications of fuzzy logic and higher-order fuzzy logic systems to control filters reducing air pollution [1]. The filters use Selective Catalytic Reduction (SCR) method and, as for now, this process is controlled manually by a human expert. The goal of the research is to control an SCR system responsible for emission of nitrogen oxide (NO) and nitrogen dioxide (NO2) to the air, using SCR with ammonia (NH3). There are two higher-order fuzzy logic systems presented, applying interval-valued fuzzy sets and type-2 fuzzy sets, respectively. Fuzzy sets and higher order fuzzy sets describe linguistically levels of nitrogen oxides as the input, and settings of ammonia valve in the air filter as the output. The obtained results are consistent with data provided by experts. Besides, we show that the type-2 fuzzy logic controllers allows us to obtain results much closer to desired parameters of the ammonia valve, than traditional FLS.
Rocznik
Strony
743--750
Opis fizyczny
Bibliogr. 20, rys., tab.
Twórcy
  • Institute of Information Technology, Lodz University of Technology, 215 Wólczańska St., 90-924 Łódź, Poland
  • Institute of Information Technology, Lodz University of Technology, 215 Wólczańska St., 90-924 Łódź, Poland
  • Institute of Social Sciences and Computer Science, Higher Vocational State School in Włocławek, 17 3-go Maja St., 87-800 Włocławek, Poland
Bibliografia
  • [1] M. Kacprowicz and A. Niewiadomski, “Managing data on air pollution using fuzzy controller”, Computer Methods in Practice 1, 46-57 (2012).
  • [2] M.N. Cirstea, Neural and Fuzzy Logic Control of Drives and Power Systems, Newnes, London, 2002.
  • [3] J. Smoczek, “Interval arithmetic-based fuzzy discrete-time crane control scheme design”, Bull. Pol. Ac.: Tech. 61 (4), 863-870 (2013).
  • [4] M. Busłowicz, “Controllability, reachability and minimum energy control of fractional discrete-time linear systems with multiple delays in state”, Bull. Pol. Ac.: Tech. 62 (2), 233-239 (2014).
  • [5] J. Smoczek, “P1-ts fuzzy scheduling control system design using local pole placement and interval analysis”, Bull. Pol. Ac.: Tech. 62 (3), 455-464 (2014).
  • [6] R.A. Christian, R.K. Lad, A.W. Deshpande, and N.G. Desai, “Fuzzy MCDM approach for addressing composite index of water and air pollution potential of industries”, Int. J. Digital Content Technology and Its Applications 1, 4-71 (2008).
  • [7] D. Rutkowska, M. Pilinski, and L. Rutkowski, Neural Networks, Genetic Algorithms and Fuzzy Systems, Scientific, Publishing PWN, Warsaw, 1997, (in Polish).
  • [8] R.R. Yager and D.P. Filev, Fundamentals of Modeling and Fuzzy Control, Scientific and Technical Publishing, Warsaw, 1995, (in Polish).
  • [9] J.T. Starczewski, “Extended triangular norms on Gaussian fuzzy sets”, EUSFLAT Conf. 1, 872-877 (2005).
  • [10] J.T. Starczewski, “On defuzzification of interval type-2 fuzzy sets”, Proc. 9th Int. Conf. on Artificial Intelligence and Soft Computing, ICAISC ’08 1, 333-340 (2008).
  • [11] Q. Liang and J.M. Mendel, “Interval type-2 fuzzy logic systems: theory and design”, IEEE Trans. on Fuzzy Systems 8, 535-550 (2000).
  • [12] J.T. Starczewski, “A triangular type-2 fuzzy logic system”, IEEE Int. Conf. on Fuzzy Systems 1, 1460-1467 (2006).
  • [13] J.M. Mendel and J.M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall, London, 2001.
  • [14] L. Rutkowski, Methods and Techniques of Artificial Intelligence, Scientific Publishing PWN, Warsaw, 2009, (in Polish).
  • [15] A. Niewiadomski, Methods for the Linguistic Summarization of Data: Applications of Fuzzy Sets and Their Extensions, Academic Publishing House, Warsaw, 2008.
  • [16] N.N. Karnik and J.M. Mendel, “Centroid of a type-2 fuzzy set”, Information Sciences 132, 195-220 (2001).
  • [17] J.C. Fodor, “On fuzzy implication”, Fuzzy Sets and Systems 42, 293-300 (1991).
  • [18] J.C. Fodor, “Contrapositive symmetry of fuzzy implications”, Fuzzy Sets and Systems 69, 141-156 (1995).
  • [19] A. Sengupta, T.K. Pal, and D. Chakraborty, “Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming”, Fuzzy Sets and Systems 119, 129-138 (2001).
  • [20] PKN Orlen Year Report, PKN Orlen, Warsaw, 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dcc49678-1925-4361-8a21-a6e3498dc967
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