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论文类型：期刊论文

发表时间：2009-03-01

发表刊物：KOREAN JOURNAL OF CHEMICAL ENGINEERING

收录刊物：SCIE

卷号：26

期号：2

页面范围：534-541

ISSN号：0256-1115

关键字：Utility Boiler; Gain Scheduling; NOx Emissions; Nonlinear Dynamic Model

摘要：A hierarchical gain scheduling (HGS) approach is proposed to model the nonlinear dynamics of NOx emissions of a utility boiler. At the lower level of HGS, a nonlinear static model is used to schedule the static parameters of local linear dynamic models (LDMs), such as static gains and static operating conditions. According to upper level scheduling variables, a multi-model method is used to calculate the predictive output based on lower-level LDMs. Both static and dynamic experiments are carried out at a 360 MW pulverized coal-fired boiler. Based on these data, a nonlinear static model using artificial neural network (ANN) and a series of linear dynamic models are obtained. Then,, the performance of the HGS model is compared to the common multi-model in predicting NOx emissions, and experimental results indicate that the proposed HGS model is much better than the multi-model in predicting NOx emissions in the dynamic process.