循环流化床锅炉燃烧过程的小波建模研究  

Wavelet Modeling for Combustion Process of Circulating Fluidized Bed Boiler

作　　者：黄啸[1] 江青茵[1] 潘学红 樊诚[1] 曹志凯[1] 

机构地区：[1]厦门大学化学工程与生物工程系,福建厦门361005 [2]郑州新力电力有限公司,河南郑州450000

出　　处：《厦门大学学报（自然科学版）》2005年第4期525-529,共5页Journal of Xiamen University：Natural Science

基　　金：厦门市科技计划项目(3502Z20021090)

摘　　要：针对高维非线性系统,分析了基于多分辨分析的正交小波网络的建模能力,并用于循环流化床锅炉燃烧过程的动态建模.根据现场采集的实时数据进行网络训练和泛化实验。Circulating Fluidized Bed Boiler (CFBB) has a splendid future among all kinds of coal-burning furnaces.At the same time,how to control the CFBB is regarded as a challenging problem because of its strong nonlinear?coupling multivariable,time delay and time-varying characters.Neural-network-based predictive control,which takes neural networks as predictive model,has strong robustness in nonlinear MIMO process control and is recommended to be adopted in the CFBB control;The wavelet network,a type of feed-forward basis function network,is chosen to build the nonlinear predictive model due to its fast convergence,small size and especially the linear relationship between its node outputs and weight coefficients which could be expediently adjusted online and this is very important in the CFBB control.In this paper,the orthogonal wavelet network is proposed in the dynamic modeling of the combustion process of CFBB, not only for it can effectively avoid the problem of 'curse of dimensionality',but for it has more significant identifying accuracy and smaller network size when adopted in the low-frequency chemical processes.The industrial data collected from two kinds of industrial processes are used as trained samples and predicted samples.Both theory analysis and application results show that the learning accuracy and the generalization capability of this wavelet network are satisfying.

关 键 词：循环流化床锅炉 燃烧过程 建模研究 高维非线性系统 正交小波网络 多分辨分析 建模能力 动态建模 网络训练 实时数据 现场采集 辨识精度 实验 泛化 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置] TK229.66[自动化与计算机技术—控制科学与工程]