NO<sub>X</sub> Emission Model for Coal-Fired Boilers Using Principle Component Analysis and Support Vector Regression

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  • Tan Peng
    State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
  • Zhang Cheng
    State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
  • Xia Ji
    State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
  • Fang Qingyan
    State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)
  • Chen Gang
    State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST)

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  • NOx Emission Model for Coal-Fired Boilers Using Principle Component Analysis and Support Vector Regression

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Abstract

Combustion optimization is an effective and economical approach for reducing nitrogen oxide (NOX) emissions from coal-fired utility boilers. To implement an online reduction in NOX, a precise and rapid NOX emissions model is required. This study establishes an efficient NOX emission model based on the principle component analysis (PCA) and support vector regression (SVR). Modeling performance comparisons were also conducted using a traditional artificial neural network (ANN) and SVR. A considerable amount of worthwhile real data was acquired from a 1000-MW coal-fired power plant to train and validate the PCA-SVR model, as well as the traditional ANN and SVR models. The predictive accuracy of the PCA-SVR model is considerably greater than that of the ANN and SVR models. The time consumed in the establishment of the PCA-SVR model is also shorter compared with that of the other two models. The proposed PCA-SVR model may be a better choice for the online or real-time modeling of NOX emissions in achieving a reduction of NOX emissions from coal-fired power plants.

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