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A novel method for the prediction of three-dimensional (3D) spatial distribution of NOx in a furnace is proposed and evaluated. Computational fluid dynamics (CFD) simulations are conducted to generate the data sets of 3D NOx spatial distribution. The data sets are partitioned based on NOx generation mechanisms to improve the model accuracy. Combining the Pearson coefficient and mutual information (PMI), the model input variables are optimized by feature selection. The prediction model of 3D NOx spatial distribution in the furnace is established based on extreme learning machine (ELM). The experiments are conducted considering a 350 MW coal-fired boiler with a change in the burner tilt angles under a rated load. The experimental results show that the data-driven method based on PMI-ELM can realize the rapid prediction of the 3D spatial distribution of NOx in the furnace with 12.84% mean absolute percentage error.

Prediction of the 3D Distribution of NOx in a Furnace via CFD Data Based on ELM