Dynamic inferential NO(x) emission prediction model with delay estimation for SCR de-NO(x) process in coal-fired power plants

The selective catalytic reduction (SCR) decomposition of nitrogen oxide (de-NO(x)) process in coal-fired power plants not only displays nonlinearity, large inertia and time variation but also a lag in NO(x) analysis; hence, it is difficult to obtain an accurate model that can be used to control NH(3) injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NO(x) emission prediction. First, k-nearest neighbour mutual information 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 was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay time difference method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NO(x) process. Through the analysis of the experimental field data in the steady state, the variable state and the NO(x) analyser blowback process, the results proved that this dynamic model has high prediction accuracy during state changes and can realize advance prediction of the NO(x) emission.