Dioxin emission prediction based on improved deep forest regression for municipal solid waste incineration process

Abstract

Dioxin (DXN) emission concentration is an important environmental indicator in the municipal solid waste incineration (MSWI) process. The prediction model of DXN emission can be used for pollution control to realize actual requirements of operation optimization. Therefore, a DXN emission concentration prediction model based on improved deep forest regression (ImDFR) is proposed in this study. A feature reduction layer based on out-of-bagging error is first introduced into the ImDFR to eliminate redundant variables and feed all confidence information on DXN emission into the feature enhancement layer of the MSWI process. A deep ensemble stacking model is subsequently built to depict deep features and increase diversity and accuracy using random forests, completely random forests, GBDT, and XGBoost as subforests. Finally, the predicted value of the DXN prediction model is determined in the decision layer. The DXN emission prediction model is verified using actual historical data of two incinerators operated with a daily processing capacity of 800 tons. The experimental results showed that the proposed prediction model presents higher accuracy and better generalization ability than state-of-the-art models.