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Indexed by:期刊论文

Date of Publication:2009-03-01

Journal:KOREAN JOURNAL OF CHEMICAL ENGINEERING

Included Journals:SCIE

Volume:26

Issue:2

Page Number:534-541

ISSN No.:0256-1115

Key Words:Utility Boiler; Gain Scheduling; NOx Emissions; Nonlinear Dynamic Model

Abstract: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.