Dioxin Emission Risk Warning Model in MSWI Process Based on GAN With Active Learning Mechanism

To solve the problem that the samples to construct a risk warning model of dioxin (DXN) emission in municipal solid waste incineration (MSWI) process are extremely scarce, a modeling method of DXN emission risk warning based on generative adversarial network (GAN) with active learning mechanism was proposed. First, the risk level of DXN was added as condition information to GAN, so that the generator generated candidate virtual samples with specified requirement. Then, the active learning mechanism based on maximum mean discrepancy and multi-view visual distribution information was used to evaluate and screen the virtual samples that met the experts' expectations. Finally, the DXN emission risk warning model was constructed based on the mixed samples composed of virtual samples and real samples. The validity and rationality of the proposed method were verified by using benchmark and MSWI process data sets. The proposed modeling method of DXN emission risk warning based on GAN with active learning mechanism can effectively solve the problem of scarce samples and improve the accuracy of the model.