Nitrogen oxides(NO_x) are byproducts from utility boilers' combustion

and accurate prediction of their emission concentrations is of great significance to environmental protection and boiler combustion optimization.To solve the problems in NO_x steady-state modeling such as high dimensional parameters

strong time-varying characteristics

and limited test samples

a modeling framework based on big data mining and Bayes ensemble learning is proposed. Firstly

a high-quality steady-state database is obtained through recognition of non-steady points

outliers and stagnation values using isolated forest and R-Value algorithms. Then

the variable selection is carried out by using random forest-recursive feature elimination method. Finally

the Bayes operation-stacking generalization ensemble learning model(BO-SGEM) is established to predict the NO_x emission. Taking 72 000history operational data of a 660 MW utility boiler as an example

the NO_x emission is predicted and the results show that

the isolated forest algorithm has the best performance among 9 outlier detection algorithms

its results of steady-state diagnosis and feature selection are consistent with the boiler combustion mechanism. The accuracy and generalization ability of BO-SGEM are better than other base models such as stacking generalization ensemble(SGEM) model

support vector regressor(SVR) model

extremely randomized trees(ET) model and gradient boosting decision tree(GBDT) model.