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Aiming at the problem that delay time is difficult to determine andprediction accuracy is low in building prediction model of SCR system, adynamic modeling scheme based on a hybrid of multiple data-driven algorithmswas proposed. First, processed abnormal values and normalized the data. Toimprove the relevance of the input data, used MIC to estimate delay time andreconstructed production data. Then used combined feature selection method todetermine input variables. To further mine data information, VMD was used todecompose input time series. Finally, established NOx emission prediction modelcombining ELM and EC model. Experimental results based on actual historicaloperating data show that the MAPE of predicted results is 2.61%. Modelsensitivity analysis shows that besides the amount of ammonia injection, theinlet oxygen concentration and the flue gas temperature have a significantimpact on NOx emission, which should be considered in SCR process control andoptimization.

Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms