The selective catalytic reduction (SCR) 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 NH₃ 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 (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-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.