NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners

Abstract

Generally, because the actual operating data tends to be concentrated in local regions due to the habits of operators and control system design, single models can only train a prediction model from a certain data feature, but they can not fully mine and utilize the valuable information hidden in the data. In this paper, new ensemble learning methods are proposed to predict NOx concentration at the SCR inlet in coal-fired boilers. Firstly, to reduce redundant data and data dimension, mutual information(MI) feature selection method is used to select input variables. Then, all input variables are normalized and divided to prepare the data for modeling. Secondly, considering the perspective of diversity and difference, three kinds of single models, which are gate recurrent unit(GRU), convolutional neural networks(CNN) and multivariate linear regression(MLR), are constructed to serve as the base learners. To make each base learner reach their optimal state, the model structures and parameters are optimized respectively. After that, the novel ensemble learning methods are proposed and used to construct NOx emission predicting model based on the three optimized base learners, respectively. To verify the generalization of the presented ensemble models, another dataset from a different thermal power plant is selected for verification. The experimental results indicate that the presented ensemble models have excellent prediction accuracy and generalization. The RMSEs of the proposed Stacking/Blending model are only 14.115/14.017mg/Nm3 (for dataset A) and 6.842/6.447mg/Nm3 (for dataset B) respectively, which are much lower than other compared models. Additionally, the application test once again proves that the presented methods can be used in engineering practice and are able to effectively reduce the NOx emission of coal-fired boiler.