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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received July 17, 2008
Accepted October 15, 2008

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Application of gain scheduling for modeling the nonlinear dynamic characteristics of NOx emissions from utility boilers

Liang Kong Yanjun Ding^† Yi Zhang¹ Lichuan Yuan Zhansong Wu

Department of Thermal Engineering, Tsinghua University, Beijing 100-084, China ¹School of Energy and Power Engineering, Dalian University of Technology, Dalian 116-024, China

Korean Journal of Chemical Engineering, March 2009, 26(2), 534-541(8), 10.1007/s11814-009-0091-0

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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 360MW 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.

Keywords

Utility Boiler Gain Scheduling NOx Emissions Nonlinear Dynamic Model

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