Dioxin emission risk warning model in MSWI process based on adversarial generative FNN
CUI Can-lin
TANG Jian
XIA Heng
QIAO Jun-fei
Abstract：Dioxin(DXN)emission in municipal solid waste incineration(MSWI)process is the key environmental protection index strictly restricted in the world.The risk warning of DXN emission is one of the primary problems to alleviate the"not in my backyard"in incineration plant construction and to realize accurate pollution control in the city.However,due to the correlation of the whole process and the memory effect in terms of the generation mechanism of DXN,the difficulty of online detection technology,and the high cost of offline testing,its modeling samples have the characteristics of high dimension,strong uncertainty,and small quantity.To solve the above problem,the method of DXN emission risk warning model in the MSWI process based on adversarial generative fuzzy neural network(FNN)is proposed.Firstly,an adaptive feature selection algorithm based on random forest(RF)is used for input feature reduction.Then,a generative adversarial network(GAN)based on FNN is used to generate candidate virtual samples for DXN risk warning modeling to alleviate the problems of uncertainty and small samples.In addition,the virtual samples are screened through the multi-constraint selection mechanism to improve the sample quality.Finally,the risk warning model of DXN emission based on mixed samples is constructed.The effectiveness of the proposed method is verified based on actual DXN data of an MSWI power plant in Beijing.
Keywords：municipal solid waste incinerationdioxinfuzzy neural networkgenerative adversarial networkvirtual sampleswarning model
Publication Date：2025-04-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 757-766 )