Type-2 fuzzy modeling for desulphurization of steel process |
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| Affiliation: | 1. Department of Industrial Engineering, Amirkabir University of Technology, (Polytechnic of Tehran), P.O. Box 15875-4413, Tehran, Iran;2. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King College Road, Toronto, Ont., Canada M5S2H8;1. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing, Jiangsu, China;2. Industrial Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;1. Dip. Matematica e Informatica, Università di Perugia, 06100 Perugia, Italy;2. Dip. S.B.A.I., “La Sapienza” Università di Roma, 00185 Roma, Italy;1. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;2. The Center for Space Automation Technologies & Systems, State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China;1. Key Laboratory of Modern Teaching Technology, Ministry of Education (Shaanxi Normal University), Xi’an 710062, China;2. School of Computer Science, Shaanxi Normal University, Xi’an 710062, China;3. School of Automation, Northwestern Polytechnical University, Xi’an 710072, China;4. Department of Vehicle Engineering, Xi’an Aeronautical, Xi’an 710077, China |
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| Abstract: | This paper presents a new type-2 fuzzy logic system model for desulphurization process of a real steel industry in Canada. The type-2 fuzzy logic system permits us to model rule uncertainties where every membership value of an element has a second order membership value of its own. In this paper, we propose an indirect method to create second order membership grades that are amplitudes of type-2 secondary membership functions, where the primary memberships are extracted by implementation of fuzzy clustering approach. In this research, Gaussian Mixture Model (GMM) is used for the creation of second order membership grades. Furthermore, a reduction scheme is implemented which results in type-1 membership grades. In turn, this leads to a reduction of the complexity of the system. Two methods are used for the estimation of the membership functions: indirect and direct methods. In the indirect method, the system uses an interpolation scheme for the estimation of the most appropriate membership functions. In the direct method, the system is tuned by an inference algorithm for the optimization of the main parametric system. In this case, the parameters are: Schweizer and Sklar t-norm and s-norm, combination of FATI and FITA inference approaches, and Yager defuzzification. Finally, the system model is applied to the desulphurization process of a real steel industry in Canada. It is shown that the proposed type-2 fuzzy logic system is superior in comparison to multiple regression and type-1 fuzzy logic systems in terms of robustness, and error reduction. |
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