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Dedicated type-2 fuzzy logic systems: a novel approach to DeNOx filtration systems

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Języki publikacji
EN
Abstrakty
EN
The paper presents some novel research on applications of Type-2 Fuzzy Logic Systems to support the Selective Catalytic Reduction process (SCR). The aim of the research is to design and test higher order fuzzy logic systems and their genuine modifications to manage data in DeNOx systems responsible for controlling emission of nitrogen oxides (NO, NO2). Since in real applications, it is still performed under the supervision of a human expert, the scope is to replace, at least partially, his/her participation with dedicated type-2 fuzzy logic systems. As the result, it is shown that the proposed systems with new means of learning fuzzy IF-THEN rules allow us to compute parameters much closer to those determined by experts, even in a comparison to some earlier approaches based on traditional fuzzy logic.
Rocznik
Strony
117--130
Opis fizyczny
Bibliogr.19 poz., il. kolor.
Twórcy
  • Institute of Information Technology, ul. Wólczańska 215, 90-924 Łódź, Lodz University of Technology, Łódź, Poland
  • Institute of Information Technology, ul. Wólczańska 215, 90-924 Łódź, Lodz University of Technology, Łódź, Poland
Bibliografia
  • [1] Starczewski, J., Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty, Vol. 284 of Studies in Fuzziness and Soft Computing, Springer, 2013.
  • [2] Kashyap, S. K., IR and color image fusion using interval type 2 fuzzy logic system, In: Cognitive Computing and Information Processing (CCIP), 2015 International Conference on, March 2015, pp. 1-4.
  • [3] Yasunobu, S. and Hasegawa, T., Evaluation of an automatic container crane operation system based on predictive fuzzy control, Control Theory Adv. Technol., vol. 2, no. 3, 1986, pp. 419-432.
  • [4] Fujitec, F., FLEX-8800 series elevator group control system, Fujitec Co., Ltd., Osaka, Japan, 1988.
  • [5] Itoh, O., Gotoh, K., Nakayama, T., and Takamizawa, S., Application of fuzzy control to activated sludge process, in Proc. 2nd IFSA Congress, Tokyo, Japan, July, 1987, pp. 282-285.
  • [6] Yagishita, O., Itoh, O., and Sugeno, M., Application of fuzzy reasoning to the water purification process, in Industrial Applications of Fuuy Control, M. Sugeno, Ed. Amsterdam: North-Holland, 1985, pp. 19-40.
  • [7] Prabhakar, S., Karthikeyan, M., Annamalai, K., and Banugopan, V. N., Control of emission characteristics by using Selective Catalytic Reduction (SCR) in D.I. diesel engine, IEEE Conference Publications, 2010.
  • [8] Mamdani, E., Applications of fuzzy algorithms for control of a simple dynamic plant, Proc.IEE 121, 1974.
  • [9] Tanaka, K., Sano, M., and Watanabe, H., Identification and analysis of fuzzy model for air pollution-an approach to self-learning control of CO concentration, IEEE Conference Publications, Vol. 3, 1992, pp. 1431 - 1436.
  • [10] Wang, B. and Chen, Z., A GIS-based Fuzzy Aggregation Modeling Approach for Air Pollution Risk Assessment, IEEE Conference Publications, Vol. 2, 2012, pp. 957-961.
  • [11] Cai, D. L. and Chen, W. K., Knowledge-BaseD Air Quality Management Study by FuzzY Logic Principle, IEEE Conference Publications, Vol. 2, 2009, pp. 3064-3069.
  • [12] Ramesh, L., Chowdhury, S., Chowdhury, S., Saha, A., and Song, Y., Effciency Optimization of Induction Motor Using a Fuzzy Logic Based Optimum Flux Search Controller, IEEE Conference Publications, 2006, pp. 1-6.
  • [13] Zadeh, L. A., The concept of a linguistic variable and its application to approximate reasoning, Inform, Vol. 8, (1975), pp. 199-249.
  • [14] Mendel, J. M., Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall, 2001.
  • [15] Kacprowicz, M. and Niewiadomski, A., On Dedicated Fuzzy Logic Systems for Emission Control of Industrial Gases, Trends in Logic XIII, 2014.
  • [16] Kacprowicz, M. and Niewiadomski, A., Managing Data on Air Pollution Using Fuzzy Controller, Computer Methods in Practice, (2012), pp. 46-57.
  • [17] Niewiadomski, A. and Kacprowicz, M., Higher order fuzzy logic in controlling selective catalytic reduction systems, Bulletin of the Polish Academy of Sciences Technical Sciences, Vol. 62, No. 4, 2014, pp. 743-750.
  • [18] Kacprowicz, M., Niewiadomski, A., and Renkas, K., Learning Rules for Type-2 Fuzzy Logic System in the Control of DeNOx Filter, Lecture Notes in Computer Science - Springer, Vol. 9119, 2015.
  • [19] ORLEN, P., Annual Report 2012 PKN ORLEN, Tech. rep., PKN ORLEN, 2012.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b9287d66-a527-4b9f-b13b-c558083a94c3
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