As the core process of the thermal treatment of municipal solid waste (MSW), incineration process optimization has become a frontier topic in the field of environmental engineering. This study took a 500 t/d incinerator for engineering application as the research object. Based on a two-fluid model, a three-dimensional transient model of a proportional incinerator was established. The effects of primary air proportion and moisture content on the combustion state in the incinerator were verified and discussed using field test data, and the dynamic changes in flue gas temperature were predicted by a BPNN (Backpropagation Neural Network). The results show that the increase in air volume in the drying section promotes water evaporation but inhibits the devolatilization and combustion of fixed carbon. The position where complete devolatilization and fixed carbon combustion begins was delayed by 1.5 m~3 m. The moisture content (M) is negatively correlated with the devolatilization and combustion of fixed carbon. From M = 25% to M = 40%, the flue gas outlet temperature decreased by 140 K. In addition, a dynamic combustion BP neural network model with the movement of the grate under rated conditions was constructed, with the MSE (Mean Squared Error) being 1.629%. The model can learn data characteristics well and has a good prediction effect. This study provides a scientific basis for optimizing the operating parameters of municipal solid waste incinerators, helps to optimize the incineration process, and is of great significance to the thermal treatment of MSW.
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