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Dioxin soft measuring method in municipal solid waste incineration based on virtual sample generation

Municipal solid waste incineration (MSWI) becomes the most popular technique to enhance environment protection. This process produces one of the most toxic chemicals in the world, i.e., polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs). The dioxin (DXN) production should be restricted rigidly by using operation optimization and control of MSWI process based on present industrial devices. However, it is difficult to realize the on-line real-time continuous measuring of DXN duo to the complexity formation mechanism and high-cost long-time off-line detection approach. In this paper, a soft measuring method based on virtual sample generation (VSG) is used to address this problem at the first time. A few numbers of true training samples are used to produce virtual training samples based on feasibility-based programming (FBP) model using selective ensemble kernel partial least squares (SENKPLS) and prior knowledge. Simulation result based on dataset in reference [31] for a HL MSWI process shows effectiveness of the proposed method.