Dioxin Emission Concentration Forecasting Approach Based on Latent Feature Extraction and Selection for Municipal Solid Waste Incineration

As one of the prohibit by-products of municipal solid waste incineration (MSWI) process, dioxin (DXN) is difficult to be on-line measured in terms of its multi-component characteristic and complexity production mechanism. Normally, DXN emission concentration is detected by using two steps, which are online flue gas acquirement with special instruments in the factory and off-line flue gas analysis with expensive instruments in the laboratory. In this paper, a new DXN emission concentration forecasting approach based on latent feature extraction and selection for the practical MSWI process is proposed. At first, latent features of the high dimensional process variables are extracted based on principal component analysis (PCA). Then, by using mutual information (MI) and pre-set feature selection ratio, these latent features are estimated and selected. At last, these selected latent features are fed into least-square support machine vector (LS-SVM) model with super-parameter adaptive selection strategy. Simulation results based on the practical DXN emission data of an industrial MSWI process of China show effectiveness of the proposed approach.