关键词: 煤燃烧, 分级配风, NO_x排放, 模型, 神经网络

Abstract: The effect of heat adding rates of blast furnace gas, coke oven gas and coal on flue gas volume was theoretically analyzed in a 300MW boiler co-firing coal with gases. Moreover, the optimization test of NO_x emissions was conducted under the condition of heat adding rates and air staging, and the prediction models of NO_x emissions was established based on the experimental data. The result showed that the theoretical flue gas volume of coal co-fired with gases was equal to that of pure coal when the ratio of blast furnace gas and coke oven gas was 0.3. The co-combustion of blast furnace gas can decrease the difference of furnace temperature, being beneficial to control the NO_x emissions. The NO_x emissions concentration decreased and the unburned carbon in fly ash increased gradually with the increase of heat adding rate of blast furnace gas and the ratio of separated overfire air. By comprehensive measurement of the NO_x emission and carbon content of fly ash, the optimal ratio of heat adding rate between blast furnace gas and coke oven gas was 1.3, and the optimal ratio of separated overfire air was 24%. The comparative analysis of the three models indicated that the GABP model had the highest prediction accuracy and could accurately describe the nonlinear relationship between the input and output parameters of boiler.

Key words: coal combustion, air staging, NO_x emissions, model, neural networks