基于组合模型的燃气轮机NOx排放影响因素研究

为了减少电厂污染物的排放, 如何准确估计燃气轮机氮氧化物(NOx)排放值并识别其关键影响因素, 对有效采取优化设计是至关重要的. 由于燃气轮机的运作过程存在变工况等情况, 单一模型的准确度与泛化性能难以达到工业应用的要求. 将偏最小二乘法(PLS)和互信息(MI)组合建模保证了NOx特征变量选取的有效性与精确性. 利用PLS确定影响燃气轮机NOx的特征变量数目, 避免了选择变量的主观因素并降低维数. 再用互信息(MI)选择出最优的特征变量, 通过不同的预测模型进行仿真分析, 并把单一和组合特征选择进行对比. 结果表明, 对燃气轮机NOx排放影响因素的研究中, PLS-MI组合模型选取的特征变量更具代表性, 并能够保证预测模型的泛化精度, 降低模型复杂度, 为电厂优化控制提供了理论依据, 具有一定的应用前景.

Research on Influencing Factors of NOx Emission from Gas Turbine Based on Combined Model

For the reduction of pollutant emissions from power plants, the ways to accurately estimate the emission value of nitrogen oxide (NOx) from gas turbines and identify its key influencing factors are crucial for effective optimization design. The accuracy and generalization performance of a single model can hardly meet the requirements of industrial applications because of variable operating conditions in the gas turbine operation process. The combined modeling of the partial least square (PLS) method and mutual information (MI) ensures the effectiveness and accuracy of the feature variable selection of NOx. Specifically, PLS is employed to determine the number of feature variables affecting the NOx of gas turbines, which can avoid subjective factors of variable selection and reduce dimensions. Then, mutual information (MI) is applied to select optimal feature variables. Different prediction models are used for simulation analysis, and single and combined feature selection is compared. The results show that in this study, the combined model PLS-MI can select more representative feature variables and can ensure the generalization accuracy of the prediction model, reducing the model complexity and providing a theoretical basis for optimal control of power plants with certain application prospects.