Title :
Utility boiler´s combustion performance modeling based on modular RBF network
Author :
Zhi Li ; Xiangfeng Wang ; Xuewei Gao ; Xu Zhang
Author_Institution :
Liaoning Key Lab. of Power Station Simulation & Control, Shenyang Inst. of Eng., Shenyang, China
Abstract :
To optimize the utility boiler´ s combustion process, a method for its combustion performance modeling based on modular Redial Basis Function (RBF) Neural Network is proposed in this paper. The whole modeling can be divided into two stages: first, get the mathematical model of carbon content of fly ash, exhaust flue gas temperature and their related input parameters; second, take the output of Neural Network as the input of boiler thermal efficiency calculation, and build a modular performance model of boiler combustion. This method can express the boiler combustion model in parts, the parts that can be described with mathematics be expressed with functions, other parts that can not be described with mathematics be expressed with RBF Neural Network. Data test and practical applications prove that this modeling method is efficient, has high precision and also meets the needs of boiler´s running optimization.
Keywords :
boilers; combustion; flue gases; fly ash; mechanical engineering computing; radial basis function networks; boiler thermal efficiency calculation; exhaust flue gas temperature; fly ash carbon content; modular RBF network; neural network; radial basis function network; utility boiler combustion process; Boilers; Carbon; Coal; Combustion; Fly ash; Mathematical model; Radial basis function networks; combustion optimization; modeling; modular RBF network; utility boiler;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022166
