Deep forest regression prediction model for dioxin emission concentration by using new representation strategy

The dioxin (DXN) is a key environmental indicator for municipal solid waste incineration (MSWI) process. However, the concentration of DXN's emission prediction model always relies on long-term and expensive offline experimental analysis. To address this issue, some researchers employed deep neural networks (DNN) to construct a soft measurement model, but it has poor training efficiency, inflexible model size, and weak interpretability. Recently, the deep forest regression (DFR) algorithm has achieved success in the field of modeling domain. Therefore, it is used for DXN emission prediction in this paper. To encourage the diversity of features between layers, this paper endeavors to implement DFR for DXN emission concentration soft measurement by a new representation strategy. Firstly, the dioxin emissions and data characteristics of the MSWI process are described. Then, the structure of DFR is analyzed and the problems of representation strategy are stated briefly. Finally, we explore the representation learning inside the DFR structure based on stacked generalization. The effectiveness of the proposed method is verified by DXN data of the MSWI process.