Authors: Ning Li, Qian-fang Liao, Xue-cong Hu
Addresses: Department of Automation, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, P.R. China. ' Department of Automation, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, P.R. China. ' Department of Automation, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, P.R. China
Abstract: In order to model and minimise the effects of uncertainties in SCR (selective catalysis reduction) flue gas denitration, an approach of Type-II T-S fuzzy modelling based on data clustering is proposed in this paper. Type-II T-S fuzzy model, with the parameter fluctuation ranges in both antecedents and consequents, is an extension of Type-I T-S fuzzy model. For antecedents, the fluctuation ranges of fuzzy membership are computed by averaging the membership differences among similar data, and the Type-I fuzzy set can then be expanded to Type-II T-S fuzzy sets. For consequents, the fluctuation ranges of the data output sections are computed by averaging the output differences of the data with similar input sections, and the fluctuation bounds of the linear polynomial coefficients can then be identified by least square method, which means the crisp coefficients can be expanded to Type-I fuzzy sets. A simulation platform is set up to facilitate the research of SCR flue gas denitration process, based on the platform, the simulation results of SCR process are provided to certify that the proposed approach of Type-II T-S fuzzy modelling is superior to the existing approaches of Type-I T-S fuzzy modelling in terms of accuracy.
Keywords: Type-II fuzzy sets; T-S fuzzy modelling; selective catalysis reduction; flue gas denitration; fuzzy logic.
International Journal of Modelling, Identification and Control, 2010 Vol.9 No.1/2, pp.199 - 205
Available online: 01 Apr 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article