SCR (selective catalytic reduction) denitrification system can effectively reduce NO_x emission by controlling ammonia injection. However, the energy structures, load fluctuations, reactor dynamic characteristics and system delay pose great challenges to the precise ammonia injection. To achieve high-precision NO_x emissions prediction, a method that combinations dynamic joint mutual information and Bi-LSTM is proposed, where the dynamic joint mutual information theory is used to estimate the reactor dynamic characteristics and system delay. And then, the inputs of the Bi-LSTM are reconstructed according to the estimations. Thus, the Bi-LSTM is established to realize the accurate NO_x estimation at the current time and t + 3 moment of SCR outlet. Taking a 660MW tangent coal-fired boiler as an example, we establish the Bi-LSTM network by using more than 15, 000 sampling data over 11 consecutive days, and predict NO_x emissions. Experiments demonstrate that considering the dynamic joint mutual information and reconstructing the inputs, the Bi-LSTM network can greatly improve the prediction accuracy, which provides the basis for the realization of accurate ammonia injection and reduction of NO_x emissions.

SCR（选择性催化还原）系统脱氮能有效地降低NO _X通过控制氨注入发射。然而，能量结构，负荷波动，反应堆动态特性和系统延迟对精确的氨注入提出了巨大挑战。为了实现高精度的NO _X排放量的预测，即动态的组合联合互信息和Bi-LSTM提出一种方法，其中，所述动态接头互信息理论被用于估计反应器的动态特性和系统的延迟。然后，根据估计值重建Bi-LSTM的输入。因此，建立Bi-LSTM可以在当前时间和t +时实现准确的NO _x估算  SCR出口3矩。服用660MW切线燃煤锅炉作为一个例子，我们通过使用在连续11天超过15，000采样数据建立碧LSTM网络，并预测NO _X排放。实验结果表明，考虑到动态联合互信息和重建的输入，碧LSTM网络可以大大提高预测精度，其提供为实现准确的氨注入和还原NO的基础_X排放。