Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants

The selective catalytic reduction (SCR) de-NOx process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NOx analysis; hence, it is difficult to obtain an accurate model that can be used to control NH3 injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NOx emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NOx process. Through the analysis of the experimental field data in ...