Intelligent Prediction Modeling of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process

To accurately predict the oxygen content in flue gas during the municipal solid waste incineration process, this paper uses mutual information, differential evolution algorithm and stochastic configuration network to select the features of the oxygen content in flue gas and predict the modeling. Firstly, the mutual information method is used to eliminate some irrelevant variables. Secondly, the differential evolution algorithm is combined with the stochastic configuration network to further eliminate redundant variables from the above selected feature variables, so as to determine the input variables for predicting the oxygen content in flue gas, and train the stochastic configuration network prediction model for the oxygen content in flue gas. Finally, the actual data sampled from a solid waste incineration plant in Beijing is used to test and verify. The results show that the hybrid feature selection method and prediction modeling method in this paper are effective and can accurately predict the oxygen content in flue gas during the solid waste incineration process.