Type-2 fuzzy modeling for desulphurization of steel process

• Published in: Expert systems with applications Vol. 32; no. 1; pp. 157 - 171
• Main Authors: Fazel Zarandi, M.H., Türkşen, I.B., Torabi Kasbi, O.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2007
• Subjects: Desulphurization; Gaussian mixture model; Interpolation method; Type-2 fuzzy logic systems; Type-2 membership functions; Type-2 membership functions; Desulphurization; Gaussian mixture model; Interpolation method; Type-2 fuzzy logic systems
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2005.11.023

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.