  Aiming at the problem that delay time is difficult to determine and
prediction accuracy is low in building prediction model of SCR system, a
dynamic modeling scheme based on a hybrid of multiple data-driven algorithms
was proposed. First, processed abnormal values and normalized the data. To
improve the relevance of the input data, used MIC to estimate delay time and
reconstructed production data. Then used combined feature selection method to
determine input variables. To further mine data information, VMD was used to
decompose input time series. Finally, established NOx emission prediction model
combining ELM and EC model. Experimental results based on actual historical
operating data show that the MAPE of predicted results is 2.61%. Model
sensitivity analysis shows that besides the amount of ammonia injection, the
inlet oxygen concentration and the flue gas temperature have a significant
impact on NOx emission, which should be considered in SCR process control and
optimization.
