Abstract

The variations in the boiler operation conditions have a great effect on the combustion characteristics and the pollutant formation in furnaces. This work aims to investigate the effects of operational parameters on NOx formation and its distribution in furnaces using the numerical simulation method to obtain the optimum control strategy for reducing NOx emissions. The numerical simulation models of pulverized coal combustion in furnaces involving flow, heat transfer, combustion and NOx formation are established. Taking a 600 MW supercritical opposed firing pulverized coal boiler as the study object, a full-scale three-dimensional physical model of the boiler is constructed with Gambit software. On this basis, the pulverized coal combustion and the NOx formation under various boiler loads are numerically simulated using the software of Ansys Fluent 2021R1, and the accuracy and the reliability of the models established are verified by comparing the simulation data with the field test data. According to the combustion numerical simulation of 128 groups of operating conditions, the effects of boiler load, the air rate and the air temperature on combustion and NOx formation have been emphatically investigated. The simulation results indicate that the formation of NOx and the NOx concentration distribution are mainly affected by the oxygen concentration and the temperature in the furnace. Especially, the effects of the variation in the excess air coefficient, the over-fire air (OFA) ratio, the primary air ratio and the internal secondary air ratio on NOx concentration distribution vary greatly. When the air temperature increases the overall NOx concentration in the furnace increases, and the influence of the secondary air temperature and the OFA temperature is greater than that of the primary air temperature. Large amounts of simulation data are a necessary data source for further study on the NOx prediction model at the economizer outlet, which can improve the prediction ability and the generalization ability of the NOx prediction model.

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