Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/79365

A New Approach of Takagi-Sugeno Fuzzy Modeling Using an Improved Genetic Algorithm Optimization for Oxygen Content in a Coke Furnace

Authors Zhang, Ridong HKUST affiliated (currently or previously)
Tao, Jili HKUST affiliated (currently or previously).
Gao, Furong View this author's profile
Issue Date 2016
Source Industrial and Engineering Chemistry Research , v. 55, (22), June 2016, p. 6465-6474
Summary The oxygen content modeling of the coke furnace is important for advanced control design but not an easy job because of various disturbances and nonlinearity. A novel approach is proposed by using an improved genetic algorithm (IGA) combined with the dynamic autoregressive with exogenous input (ARX) Takagi-Sugeno (T-S) fuzzy model. The IGA algorithm automatically generates the input variable, the appropriate fuzzy if-then rules, and the ARX structure to characterize the dynamic nonlinear feature of the oxygen content by processing the operation data from the industrial coke furnace. And a more comprehensive objective function is constructed considering both the modeling precision and structure simplicity. Hybrid encoding, modified genetic operators, particularly the maintain operator, are designed to obtain the satisfactory optimization performance. The modeling accuracy and system structure of the T-S fuzzy model are compared with a benchmark Box-Jenkins gas furnace and the complex industrial coke furnace. The results show good modeling accuracy and simple structure of the T-S fuzzy model. © 2016 American Chemical Society.
ISSN 0888-5885
Language English
Format Article
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