Aiming at the problem of inaccurate prediction of NO_x emission concentration when the current coal-gas boiler gas mixture is uncertain and changing

a combined online prediction method based on attention mechanism is proposed. First

the characteristic variables of the model are determined by combining the maximum information coefficient method with the Pearson correlation coefficient method; Secondly

vector autoregressive(VAR) model is constructed online with sliding time window for linearly correlated characteristic variables to realize the prediction of NO_x emission concentration under the input of multi-dimensional time series linear correlation variables For non-linear-related feature variables

the relationship between NO_x emission concentration is predicted by constructing an online Recurrent extreme learning machine(OR-ELM) model online learning. Finally

Attention Mechanism(AM) is used to dynamically weight the two forecasting models to achieve trend forecasting. Through field data verification

it shows that the VAR-OR-ELM combined online prediction model constructed in this paper can accurately predict the variation trend of NO_x emission concentration after 10 minutes. Combining prediction accuracy and prediction time

the combined prediction model is better than other single prediction models.