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The real-time prediction of NOx emissions is of great significance forpollutant emission control and unit operation of coal-fired power plants.Aiming at dealing with the large time delay and strong nonlinearcharacteristics of the combustion process, a dynamic correction predictionmodel considering the time delay is proposed. First, the maximum informationcoefficient (MIC) is used to calculate the delay time between relatedparameters and NOx emissions, and the modeling data set is reconstructed; then,an adaptive feature selection algorithm based on Lasso and ReliefF isconstructed to filter out the high correlation with NOx emissions. Parameters;Finally, an extreme learning machine (ELM) model combined with error correctionwas established to achieve the purpose of dynamically predicting theconcentration of nitrogen oxides. Experimental results based on actual datashow that the same variable has different delay times under load conditionssuch as rising, falling, and steady; and there are differences in modelcharacteristic variables under different load conditions; dynamic errorcorrection strategies effectively improve modeling accuracy; proposed Theprediction error of the algorithm under different working conditions is lessthan 2%, which can accurately predict the NOx concentration at the combustionoutlet, and provide guidance for NOx emission monitoring and combustion processoptimization.

Data Driven based Dynamic Correction Prediction Model for NOx Emission of Coal Fired Boiler