Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Niewiadomski, Adam; Kacprowicz, Marcin

doi:10.2478/jaiscr-2021-0006

Abstract

The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO₂). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.

References

[1] M. Agarwal and S. Goel, Expert system and it’s requirement engineering process, in Recent Advances and Innovations in Engineering (ICRAIE), 2014, pp. 1–4, May 2014.10.1109/ICRAIE.2014.6909306
Search in Google Scholar Back to article
[2] K. Zalis, Application of expert systems in diagnostics of high voltage insulating systems, in Solid Dielectrics, 2004. ICSD 2004. Proceedings of the 2004 IEEE International Conference on, vol. 2, pp. 691–694 Vol. 2, July 2004.
Search in Google Scholar Back to article
[3] K. Bilal and S. Mohsin, Mu/hadith: A Cloud Based Distributed Expert System for Classification of Ahadith, in Frontiers of Information Technology (FIT), 2012 10th International Conference on, pp. 73–78, Dec 2012.10.1109/FIT.2012.22
Search in Google Scholar Back to article
[4] J. T. Starczewski, General type-2 FLS with uncertainty generated by fuzzy rough sets, in Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, pp. 1–6, July 2010.10.1109/FUZZY.2010.5584238
Search in Google Scholar Back to article
[5] R. K. Nowicki, B. A. Nowak, J. T. Starczewski, and K. Cpałka, The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features, in Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 3759–3766, July 2014.10.1109/IJCNN.2014.6889857
Search in Google Scholar Back to article
[6] J. T. Starczewski, A Triangular Type-2 Fuzzy Logic System, in Fuzzy Systems, 2006 IEEE International Conference on, pp. 1460–1467, 2006.10.1109/FUZZY.2006.1681901
Search in Google Scholar Back to article
[7] N. C. Long and P. Meesad, Meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction, in Computational Intelligence Applications (IWCIA), 2013 IEEE Sixth International Workshop on, pp. 69–74, July 2013.10.1109/IWCIA.2013.6624787
Search in Google Scholar Back to article
[8] M. Kinoshita, T. Fukuzaki, T. Satoh, and M. Miyake, An automatic operation method for control rods in BWR plants, Meeting on In-Core Instrumentation and Reactor Core Assessment, Cadarache, France, 1988.
Search in Google Scholar Back to article
[9] J. A. Bernard, Use of rule-based system for process control, IEEE Contr. Sys. Mag, pp. 3–13, 1988.10.1109/37.7735
Search in Google Scholar Back to article
[10] F. Fujitec, FLEX-8800 series elevator group control system, Fujitec Co., Ltd., Osaka, Japan, 1988.
Search in Google Scholar Back to article
[11] S. Yasunobu, S. Miyamoto, and H. Ihara, Fuzzy control for automatic train operation system, IFORS Int. Congress on Control in Transportation Systems, Baden-Baden, 1983.10.1016/B978-0-08-029365-3.50010-9
Search in Google Scholar Back to article
[12] O. Itoh, K. Gotoh, T. Nakayama, and S. Takamizawa, Application of fuzzy control to activated sludge process, in Proc. 2nd IFSA Congress, Tokyo, Japan, pp. 282–285, July, 1987.
Search in Google Scholar Back to article
[13] O. Yagishita, O. Itoh, and M. Sugeno, Application of fuzzy reasoning to the water purification process, in Industrial Applications of Fuuy Control, M. Sugeno, Ed. Amsterdam: North-Holland, pp. 19–40, 1985.
Search in Google Scholar Back to article
[14] M. Kacprowicz and A. Niewiadomski, On Dedicated Fuzzy Logic Systems for Emission Control of Industrial Gases, in Trends in Logic XIII (A. Indrzejczak, J. Kaczmarek, and M. Zawidzki, eds.), pp. 113–130, 2014.
Search in Google Scholar Back to article
[15] A. Niewiadomski and M. Kacprowicz, Higher order fuzzy logic in controlling selective catalytic reduction systems, Bulletin of the Polish Academy of Sciences Technical Sciences, vol. 62, no. 4, pp. 743–750, 2014.10.2478/bpasts-2014-0080
Search in Google Scholar Back to article
[16] M. Kacprowicz and A. Niewiadomski, Managing Data on Air Pollution Using Fuzzy Controller, in Computer Methods in Practice (A. Cader, M. Yatsymirskyy, and K. Przybyszewski, eds.), pp. 46–57, Exit Publishing House, Warsaw, Poland, 2012.
Search in Google Scholar Back to article
[17] K. Renkas, A. Niewiadomski, and M. Kacprowicz, Learning Rules for Hierarchical Fuzzy Logic Systems with Selective Fuzzy Controller Activation, Lecture Notes in Computer Science - Springer, vol. 9119, pp. 260–270, 2015.10.1007/978-3-319-19324-3_24
Search in Google Scholar Back to article
[18] S. Prabhakar, M. Karthikeyan, K. Annamalai, and V. N. Banugopan, Control of emission characteristics by using Selective Catalytic Reduction (SCR) in D.I. diesel engine, IEEE Conference Publications, 2010.10.1109/FAME.2010.5714808
Search in Google Scholar Back to article
[19] R. R. Yager and D. P. Filev, Fundamentals of modeling and fuzzy control (in Polish: Podstawy modelowania i sterowania rozmytego). WNT, Warsaw, 1995.
Search in Google Scholar Back to article
[20] J. Jantzen, Foundations of Fuzzy Control. John Wiley & Sons Ltd., England, 2007.10.1002/9780470061176
Search in Google Scholar Back to article
[21] D. Majumder and K. Dwijesh, Fuzzy logic and its application in technology and management. Narosa, 2007.
Search in Google Scholar Back to article
[22] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall, 2001.
Search in Google Scholar Back to article
[23] J. C. Fodor, Contrapositive symmetry of fuzzy implications, Fuzzy Sets and Systems, (1995).10.1016/0165-0114(94)00210-X
Search in Google Scholar Back to article
[24] J. C. Fodor, On fuzzy implication, Fuzzy Sets and Systems, vol. 42, pp. 293–300, 1991.10.1016/0165-0114(91)90108-3
Search in Google Scholar Back to article
[25] O. Cordon, F. Herrera, and P. Villar, Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base, Fuzzy Systems, IEEE Transactions on, vol. 9, no. 4, pp. 667–674, 2001.10.1109/91.940977
Search in Google Scholar Back to article
[26] R. Hammell and T. Sudkamp, Learning Fuzzy Rules From Data, in The Application of Information Technologies (Computer Science) to Mission Systems, pp. 1–10, 1998.
Search in Google Scholar Back to article
[27] K. Mittal, A. Jain, K. S. Vaisla, O. Castillo, and J. Kacprzyk, A comprehensive review on type-2 fuzzy logic applications: Past, present and future, Engineering Applications of Artificial Intelligence, vol. 95, 2020.10.1016/j.engappai.2020.103916
Search in Google Scholar Back to article
[28] S. Türk, M. Deveci, E. Özcan, F. Canıtez, and R. John, Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations, Information Sciences, vol. 547, pp. 641–666, 2021.10.1016/j.ins.2020.08.076
Search in Google Scholar Back to article
[29] E. Ontiveros, P. Melin, and O. Castillo, Comparative study of interval Type-2 and general Type-2 fuzzy systems in medical diagnosis, Information Sciences, vol. 525, pp. 37–53, 2020.10.1016/j.ins.2020.03.059
Search in Google Scholar Back to article
[30] R. Jafelice and W. Lodwick, Interval analysis of the HIV dynamics model solution using type-2 fuzzy sets, Mathematics and Computers in Simulation, vol. 180, pp. 306–327, 2021.10.1016/j.matcom.2020.08.022
Search in Google Scholar Back to article
[31] Z. Ashraf, M. L. Roy, P. K. Muhuri, and Q. Danish Lohani, Interval type-2 fuzzy logic system based similarity evaluation for image steganography, Heliyon, vol. 6, no. 5, p. e03771, 2020.10.1016/j.heliyon.2020.e03771721511632420466
Search in Google Scholar Back to article
[32] E. Ontiveros-Robles and P. Melin, A hybrid design of shadowed type-2 fuzzy inference systems applied in diagnosis problems, Engineering Applications of Artificial Intelligence, vol. 86, pp. 43–55, 2019.10.1016/j.engappai.2019.08.017
Search in Google Scholar Back to article
[33] J. McCulloch and C. Wagner, On the choice of similarity measures for type-2 fuzzy sets, Information Sciences, vol. 510, pp. 135–154, 2020.10.1016/j.ins.2019.09.027
Search in Google Scholar Back to article
[34] Q. Liang and J. M. Mendel, Interval Type-2 Fuzzy Logic Systems: Theory and Design, IEEE Transactions on Fuzzy Systems, vol. 8, pp. 535–550, (2000).10.1109/91.873577
Search in Google Scholar Back to article
[35] L. Wang and J. M. Mendel, Generating Fuzzy Rules by Learning from Examples, IEEE Transactions on Fuzzy Systems, vol. 22, pp. 1414–1427, 1992.10.1109/21.199466
Search in Google Scholar Back to article
[36] D. Wu and J. M. Mendel, Recommendations on designing practical interval type-2 fuzzy systems, CoRR, vol. abs/1907.01697, 2019.10.1016/j.engappai.2019.06.012
Search in Google Scholar Back to article
[37] H. H. Li and M. M. Gupta, Fuzzy Logic and Intelligent Systems. Springer, 2007.
Search in Google Scholar Back to article
[38] PKN ORLEN, Annual Report 2012 PKN Orlen, tech. rep., PKN ORLEN, 2012.
Search in Google Scholar Back to article

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Abstract

Paradigm

My account