Air Volume Setting Model of Municipal Solid Waste Incineration Process Based on Color Moment Features of Combustion Flame

Municipal solid waste incineration (MSWI) technology has been widely used all over the world. As fluctuation in the solid waste components, its operation usually depends on the domain experts to judge the combustion state in the furnace. The key operating variables, such as, air volume, are manually set according to expert experience, which has strong subjectivity and randomness. To solve the above problems, this paper builds an air volume setting model of MSWI process based on color moment features of combustion flame. First, single image fast defogging and median filter algorithms are used to defog and eliminate random noise. Then, the color moment features of the preprocessed image are extracted by multi-scale sliding windows. Thirdly, the correlation between color moment features and air volume is analyzed by mutual information algorithm (MI) for selecting the key features. At last, the air volume setting model based on radial basis function neural network (RBFNN) is constructed. The effectiveness of the proposed method is verified by the actual running data of a MSWI power plant in Beijing.