Abstract
A hierarchical gain scheduling (HGS) approach is proposed to model the nonlinear dynamics of NO x 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 NO x emissions, and experimental results indicate that the proposed HGS model is much better than the multi-model in predicting NO x emissions in the dynamic process.
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Kong, L., Ding, Y., Zhang, Y. et al. Application of gain scheduling for modeling the nonlinear dynamic characteristics of NO x emissions from utility boilers. Korean J. Chem. Eng. 26, 534–541 (2009). https://doi.org/10.1007/s11814-009-0091-0
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DOI: https://doi.org/10.1007/s11814-009-0091-0