A NOx emission prediction hybrid method based on boiler data feature subset selection

Simplicity, efficiency and precision are basic principles for modeling and analyzing of coal-fired boilers data. However, the load fluctuations, system delay, multi-variable coupling pose great challenges to the high-precision modeling for NOx emission. A hybrid feature selection method for boiler data is proposed to predict accurately NOx emission. Firstly, the mechanism analysis is used to narrow the feature scope, and the feature set is selected preliminarily. Secondly, maximum information coefficient (MIC) method is introduced to calculate the correlation between features and NOx information to eliminate boiler system delay. Thirdly, a combined feature evaluation method is developed, which integrates Filter and Embedded method to obtain feature ranking, then the ranking information is regarded as priori knowledge to improve the genetic algorithm. Finally, a fitness function to maximize prediction accuracy and minimize feature dimension is constructed based on hybrid method to realize feature subset selection and NOx emission prediction. Original data are collected from one 1000 MW coal-fired unit in the Guangdong province of China. Experimental results show that the number of features is reduced by nearly 70%, the MAPE of LightGBM regression model is no more than 2%. Higher prediction accuracy can be obtained using the features extracted through the proposed hybrid method.