Flue Gas Oxygen Content Modeling Approach Based on BPNN and LRDT in Municipal Solid Waste Incineration Process

Flue gas oxygen content is one of the key controlled variables of municipal solid waste incineration (MSWI) process. And it is closely related to the combustion efficiency and pollutant emission. At present, there are few reports on flue gas oxygen content model as the controlled object. The article proposes a flue gas oxygen content modeling method based on the fusion of back propagation neural network (BPNN) and linear regression decision tree (LRDT). Firstly, the BPNN model is used to construct the main model. Then, LRDT is used to construct the compensation model by using the error of the of main model as the output. Finally, the fusion output is obtained by summarizing the LRDT and BPNN model. Experimental results based on actual operating data of a MSWI plant in Beijing shows the feasibility and effectiveness of the proposed method.