Modeling of the Boiler NOx Emission with a Data Driven Algorithm

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  • Tang Zhenhao
    School of Automatic Engineering, The Northeast Electric Power University
  • Wu Xiaoyan
    School of Automatic Engineering, The Northeast Electric Power University
  • Cao Shengxian
    School of Automatic Engineering, The Northeast Electric Power University
  • Yang Mingxuan
    School of Automatic Engineering, The Northeast Electric Power University

Bibliographic Information

Published
2018-08-20
DOI
  • 10.1252/jcej.17we335
Publisher
The Society of Chemical Engineers, Japan

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Description

<p>NOx emission prediction is important for efficient boiler production and waste control. An adaptive data-driven modeling method is proposed to predict the boiler NOx emissions dynamically. In this method, a linear combination kernel is presented to improve the prediction accuracy of least-square support vector machine. The parameters of the kernel are optimized adaptively by a particle swarm optimization algorithm. Additionally, an adaptive moving time window strategy is presented to maintain model performance. The computational results based on the practical data illustrate that the proposed kernel and the adaptive moving time window strategy are positive and the proposed prediction method is superior to some previous prediction methods.</p>

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